WorldWideScience

Sample records for significant network features

  1. Identifying significant environmental features using feature recognition.

    2015-10-01

    The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...

  2. Learning Transferable Features with Deep Adaptation Networks

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  3. Input significance analysis: feature selection through synaptic ...

    Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected ... from the UCI Machine Learning Repository and executed in an online ... connectionist systems; evolving fuzzy neural network; connection weights; Garson's

  4. Schizophrenia classification using functional network features

    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.

  5. A keyword spotting model using perceptually significant energy features

    Umakanthan, Padmalochini

    The task of a keyword recognition system is to detect the presence of certain words in a conversation based on the linguistic information present in human speech. Such keyword spotting systems have applications in homeland security, telephone surveillance and human-computer interfacing. General procedure of a keyword spotting system involves feature generation and matching. In this work, new set of features that are based on the psycho-acoustic masking nature of human speech are proposed. After developing these features a time aligned pattern matching process was implemented to locate the words in a set of unknown words. A word boundary detection technique based on frame classification using the nonlinear characteristics of speech is also addressed in this work. Validation of this keyword spotting model was done using widely acclaimed Cepstral features. The experimental results indicate the viability of using these perceptually significant features as an augmented feature set in keyword spotting.

  6. Discriminative topological features reveal biological network mechanisms

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  7. Identification of significant features by the Global Mean Rank test.

    Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

    2014-01-01

    With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.

  8. Feature to prototype transition in neural networks

    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.

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

    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

  10. Mummified trophy heads from Peru: diagnostic features and medicolegal significance.

    Verano, John W

    2003-05-01

    Several forms of mummified human trophy heads were produced by prehistoric and historic native groups in South America. This paper describes the diagnostic features of trophy heads produced by the Nasca culture of ancient Peru. A growing interest in these mummified heads among collectors of Pre-Columbian art and antiquities has led to their illegal exportation from Peru, in violation of national and international antiquities laws. Requests from the Peruvian government to protect its cultural patrimony led the United States in 1997 to declare these heads as items subject to U.S. import restriction, along with six other categories of human remains. Despite such restrictions, Nasca trophy heads continue to reach private collectors outside of Peru and thus may be encountered by local, state, or federal law enforcement officials unfamiliar with their characteristic features and origin. The objective of this paper is to describe the features that allow Nasca trophy heads to be identified and distinguished from other archaeological and forensic specimens that may be submitted to a forensic anthropologist for identification.

  11. Convolutional neural network features based change detection in satellite images

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  12. Significance of MPEG-7 textural features for improved mass detection in mammography.

    Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

    2006-01-01

    The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

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

    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

  14. Innovations in individual feature history management - The significance of feature-based temporal model

    Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

    2008-01-01

    A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

  15. Significance of connective tissue diseases features in pulmonary fibrosis

    Vincent Cottin

    2013-09-01

    Full Text Available Interstitial lung disease (ILD can occur in any of the connective tissue diseases (CTD with varying frequency and severity, and an overall long-term prognosis that is less severe than that of idiopathic pulmonary fibrosis (IPF. Because ILD may be the presenting manifestation of CTD and/or the dominant manifestation of CTD, clinical extra-thoracic manifestations should be systematically considered in the diagnostic approach of ILD. When present, autoantibodies strongly contribute to the recognition and classification of the CTD. Patients with clinical extrathoracic manifestations of CTD and/or autoantibodies (especially with a high titer and/or the antibody is considered “highly specific” of an autoimmune condition, but who do not fit with established international CTD criteria may be called undifferentiated CTD or “lung-dominant CTD”. Although it remains to be determined which combination of symptoms and serologic tests best identify the subset of patients with clinically relevant CTD features, available evidence suggests that such patients may have distinct clinical and imaging presentation and may portend a distinct clinical course. However, autoantibodies alone when present in IPF patients do not seem to impact prognosis or management. Referral to a rheumatologist and multidisciplinary discussion may contribute to management of patients with undifferentiated CTD.

  16. Systematic Significance of Leaf Epidermal Features in Holcoglossum (Orchidaceae)

    Fan, Jie; He, Runli; Zhang, Yinbo; Jin, Xiaohua

    2014-01-01

    Determining the generic delimitations within Aeridinae has been a significant issue in the taxonomy of Orchidaceae, and Holcoglossum is a typical case. We investigated the phylogenetic utility of the morphological traits of leaf epidermis in the taxonomy of Holcoglossum s.l. by using light and scanning electron microscopy to analyze 38 samples representing 12 species of Holcoglossum, with five species from five closely related genera, such as Ascocentrum, Luisia, Papilionanthe, Rhynchostylis ...

  17. Patch layout generation by detecting feature networks

    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.

  18. Systematic significance of leaf epidermal features in holcoglossum (orchidaceae).

    Fan, Jie; He, Runli; Zhang, Yinbo; Jin, Xiaohua

    2014-01-01

    Determining the generic delimitations within Aeridinae has been a significant issue in the taxonomy of Orchidaceae, and Holcoglossum is a typical case. We investigated the phylogenetic utility of the morphological traits of leaf epidermis in the taxonomy of Holcoglossum s.l. by using light and scanning electron microscopy to analyze 38 samples representing 12 species of Holcoglossum, with five species from five closely related genera, such as Ascocentrum, Luisia, Papilionanthe, Rhynchostylis and Vanda. Our results indicated that Holcoglossum can be distinguished from the related genera based on cuticular wax characteristics, and the inclusion of Holcoglossum himalaicum in Holcoglossum is supported by the epidermis characteristics found by LM and SEM. The percentage of the tetracytic, brachyparacytic, and laterocytic stomata types as well as the stomata index and certain combinations of special wax types support infrageneric clades and phylogenetic relationships that have been inferred from molecular data. Laterocytic and polarcytic stomata are perhaps ecological adaptations to the strong winds and ample rains in the alpine region of the Hengduanshan Mountains.

  19. Systematic significance of leaf epidermal features in holcoglossum (orchidaceae.

    Jie Fan

    Full Text Available Determining the generic delimitations within Aeridinae has been a significant issue in the taxonomy of Orchidaceae, and Holcoglossum is a typical case. We investigated the phylogenetic utility of the morphological traits of leaf epidermis in the taxonomy of Holcoglossum s.l. by using light and scanning electron microscopy to analyze 38 samples representing 12 species of Holcoglossum, with five species from five closely related genera, such as Ascocentrum, Luisia, Papilionanthe, Rhynchostylis and Vanda. Our results indicated that Holcoglossum can be distinguished from the related genera based on cuticular wax characteristics, and the inclusion of Holcoglossum himalaicum in Holcoglossum is supported by the epidermis characteristics found by LM and SEM. The percentage of the tetracytic, brachyparacytic, and laterocytic stomata types as well as the stomata index and certain combinations of special wax types support infrageneric clades and phylogenetic relationships that have been inferred from molecular data. Laterocytic and polarcytic stomata are perhaps ecological adaptations to the strong winds and ample rains in the alpine region of the Hengduanshan Mountains.

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

    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.

  1. A framework for online social networking features

    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.

  2. Patch layout generation by detecting feature networks

    Cao, Yuanhao; Yan, Dongming; Wonka, Peter

    2015-01-01

    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

  3. Self-organizing networks for extracting jet features

    Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.

    1991-01-01

    Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)

  4. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

    P. Amudha

    2015-01-01

    Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

  5. Investigation of road network features and safety performance.

    Wang, Xuesong; Wu, Xingwei; Abdel-Aty, Mohamed; Tremont, Paul J

    2013-07-01

    The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models' results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Fundamental statistical features and self-similar properties of tagged networks

    Palla, Gergely; Farkas, Illes J; Pollner, Peter; Vicsek, Tamas; Derenyi, Imre

    2008-01-01

    We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterize the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g. the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterized by the tag-assortativity exponent, which is specific to each system.

  7. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  8. Significance of Joint Features Derived from the Modified Group Delay Function in Speech Processing

    Murthy Hema A

    2007-01-01

    Full Text Available This paper investigates the significance of combining cepstral features derived from the modified group delay function and from the short-time spectral magnitude like the MFCC. The conventional group delay function fails to capture the resonant structure and the dynamic range of the speech spectrum primarily due to pitch periodicity effects. The group delay function is modified to suppress these spikes and to restore the dynamic range of the speech spectrum. Cepstral features are derived from the modified group delay function, which are called the modified group delay feature (MODGDF. The complementarity and robustness of the MODGDF when compared to the MFCC are also analyzed using spectral reconstruction techniques. Combination of several spectral magnitude-based features and the MODGDF using feature fusion and likelihood combination is described. These features are then used for three speech processing tasks, namely, syllable, speaker, and language recognition. Results indicate that combining MODGDF with MFCC at the feature level gives significant improvements for speech recognition tasks in noise. Combining the MODGDF and the spectral magnitude-based features gives a significant increase in recognition performance of 11% at best, while combining any two features derived from the spectral magnitude does not give any significant improvement.

  9. Associations Between PET Textural Features and GLUT1 Expression, and the Prognostic Significance of Textural Features in Lung Adenocarcinoma.

    Koh, Young Wha; Park, Seong Yong; Hyun, Seung Hyup; Lee, Su Jin

    2018-02-01

    We evaluated the association between positron emission tomography (PET) textural features and glucose transporter 1 (GLUT1) expression level and further investigated the prognostic significance of textural features in lung adenocarcinoma. We evaluated 105 adenocarcinoma patients. We extracted texture-based PET parameters of primary tumors. Conventional PET parameters were also measured. The relationships between PET parameters and GLUT1 expression levels were evaluated. The association between PET parameters and overall survival (OS) was assessed using Cox's proportional hazard regression models. In terms of PET textural features, tumors expressing high levels of GLUT1 exhibited significantly lower coarseness, contrast, complexity, and strength, but significantly higher busyness. On univariate analysis, the metabolic tumor volume, total lesion glycolysis, contrast, busyness, complexity, and strength were significant predictors of OS. Multivariate analysis showed that lower complexity (HR=2.017, 95%CI=1.032-3.942, p=0.040) was independently associated with poorer survival. PET textural features may aid risk stratification in lung adenocarcinoma patients. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  10. Topological Embedding Feature Based Resource Allocation in Network Virtualization

    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.

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

    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.

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

    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...

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

    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.

  14. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Perdomo, Oscar; Arevalo, John; González, Fabio A.

    2017-11-01

    Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

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

    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.

  16. Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

    2016-04-26

    Systems, Statistics & Management Science, University of Alabama, USA. 1 DISTRIBUTION A: Distribution approved for public release. Contents 1 Summary 5...13 5 Application to Real Networks 18 5.1 2012 FBS Football Schedule Network... football schedule network. . . . . . . . . . . . . . . . . . . . . . 21 14 Stem plot of degree-ordered vertices versus the degree for college football

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

    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.

  18. Feature selection based on SVM significance maps for classification of dementia

    E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

    2014-01-01

    textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

  19. Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks.

    Tian, Ye; Zhang, Bai; Hoffman, Eric P; Clarke, Robert; Zhang, Zhen; Shih, Ie-Ming; Xuan, Jianhua; Herrington, David M; Wang, Yue

    2014-07-24

    Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowledge; missing significance assessment; and heuristic structural parameter learning. To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm. Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological

  20. Network features and pathway analyses of a signal transduction cascade

    Ryoji Yanashima

    2009-05-01

    Full Text Available The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

  1. Significance of social networks in sustainable land management in ...

    Social networks (SNs) are social frameworks that form good entry points for business and socio-economic developments. Social networks are important for small-scale, resource-poor farmers in Sub-Saharan Africa, who overly rely on informal sources of information. SNs provide opportunities for establishing effective ...

  2. Breast image feature learning with adaptive deconvolutional networks

    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).

  3. Significant histologic features differentiating cellular fibroadenoma from phyllodes tumor on core needle biopsy specimens.

    Yasir, Saba; Gamez, Roberto; Jenkins, Sarah; Visscher, Daniel W; Nassar, Aziza

    2014-09-01

    Cellular fibroepithelial lesions (CFELs) are a heterogeneous group of tumors encompassing cellular fibroadenoma (CFA) and phyllodes tumor (PT). Distinction between the two is challenging on core needle biopsy (CNB) specimens. The objective of this study was to evaluate histologic features that can help distinguish PT from CFA on CNB specimens. Records of all patients diagnosed with CFELs on CNB specimens with follow-up excision between January 2002 and December 2012 were retrieved. Histopathologic stromal features were evaluated on CNB specimens, including mitoses per 10 high-power fields (hpf), overgrowth, increased cellularity, fragmentation, adipose tissue infiltration, heterogeneity, subepithelial condensation, and nuclear pleomorphism. Twenty-seven (42.2%) of 64 were diagnosed as PT (24 benign PTs and three borderline PTs) and 37 (57.8%) as CFA on excision. All features except for increased stromal cellularity were statistically significant. The average number of histologic features seen in PT and CFA was 3.9 and 1.4, respectively (odds ratio [OR], 7.27; 95% confidence interval [CI], 2.44-21.69; P = .0004). The average number of mitoses per 10 hpf was 3.0 for PT compared with 0.8 for CFA (OR, 2.14; 95% CI, 1.18-3.86; P = .01). The presence of mitoses (three or more) and/or total histologic features of three or more on CNB specimens were the most helpful features in predicting PT on excision. Copyright© by the American Society for Clinical Pathology.

  4. Significant Histological Features Differentiating Cellular Fibroadenoma from Phyllodes Tumor on Core Needle Biopsies

    Yasir, Saba; Gamez, Roberto; Jenkins, Sarah; Visscher, Daniel W.; Nassar, Aziza

    2015-01-01

    Objectives Cellular fibroepithelial lesions (CFEL) are a heterogeneous group of tumors encompassing cellular fibroadenoma (CFA) and phyllodes tumor (PT). Distinction between the two is challenging on core needle biopsy (CNB). The objective of this study was to evaluate histological features that can help distinguish PT from CFA on CNB. Methods Records of all patients diagnosed with CFEL on CNB with follow-up excision between 2002 and 2012 were retrieved. Histopathological stromal features were evaluated on CNB including mitoses per 10 HPF, overgrowth, increased cellularity, fragmentation, adipose tissue infiltration, heterogeneity, subepithelial condensation, and nuclear pleomorphism. Results Twenty-seven of 64 (42.2%) were diagnosed as PT (24 BPT, 3 borderline PT) and 37 (57.8%) as CFA on excision. All features except for increased stromal cellularity were statistically significant. The average number of histologic features seen in PT and CFA was 3.9 and 1.4, respectively (OR 7.27; 95% CI: 2.44, 21.69; p= 0.0004). The average mitoses per 10 HPF was 3.0 for PT as compared to 0.8 for CFA (OR 2.14; 95% CI: 1.18, 3.86; p= 0.01). Conclusions The presence of mitosis (3 or more) and/or total histologic features of 3 or more on CNB were most helpful features in predicting PT on excision. PMID:25125627

  5. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Assessment of significance of features acquired from thyroid ultrasonograms in Hashimoto's disease

    Koprowski Robert

    2012-08-01

    Full Text Available Abstract Introduction This paper concerns the analysis of the features obtained from thyroid ultrasound images in left and right transverse and longitudinal sections. In the image analysis, the thyroid lobe is treated as a texture for healthy subjects and patients with Hashimoto’s disease. The applied methods of analysis and image processing were profiled to obtain 10 features of the image. Then, their significance in the classification was shown. Material In this study, the examined group consisted of 29 healthy subjects aged 18 to 60 and 65 patients with Hashimoto's disease. For each subject, four ultrasound images were taken. They were all in transverse and longitudinal sections of the right and left lobe of the thyroid, which gave 376 images in total. Method 10 different features obtained from each ultrasound image were suggested. The analyzed thyroid lobe was marked automatically or manually with a rectangular element. Results The analysis of 10 features and the creation for each one of them their own decision tree configuration resulted in distinguishing 3 most significant features. The results of the quality of classification show accuracy above 94% for a non-trimmed decision tree.

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

    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.

  8. [Study on computed tomography features of nasal septum cellule and its clinical significance].

    Huang, Dingqiang; Li, Wanrong; Gao, Liming; Xu, Guanqiang; Ou, Xiaoyi; Tang, Guangcai

    2008-03-01

    To investigate the features of nasal septum cellule in computed tomographic (CT) images and its clinical significance. CT scans data of nasal septum in 173 patients were randomly obtained from January 2001 to June 2005. Prevalence and clinical features were summarized in the data of 19 patients with nasal septum cellule retrospectively. (1) Nineteen cases with nasal septum cellule were found in 173 patients. (2) All nasal septum cellule of 19 cases located in perpendicular plate of the ethmoid bone, in which 8 cases located in upper part of nasal septum and 11 located in middle. (3) There were totally seven patients with nasal diseases related to nasal septum cellule, in which 3 cases with inflammation, 2 cases with bone fracture, 1 case with cholesterol granuloma, 1 case with mucocele. Nasal septum cellule is an anatomic variation of nasal septum bone, and its features can provide further understanding of some diseases related to nasal septum cellule.

  9. Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

    Hong-Yu LIU

    2014-10-01

    Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

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

    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.

  11. Significance of the impact of motion compensation on the variability of PET image features

    Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

    2018-03-01

    In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

  12. Roles and significance of water conducting features for transport models in performance assessment

    Carrera, J.; Sanchez-Vila, X.; Medina, A.

    1999-01-01

    The term water conducting features (WCF) refers to zones of high hydraulic conductivity. In the context of waste disposal, it is further implied that they are narrow so that chances of sampling them are low. Yet, they may carry significant amounts of water. Moreover, their relatively small volumetric water content causes solutes to travel fast through them. Water-conducting features are a rather common feature of natural media. The fact that they have become a source of concern in recent years, reflects more the increased level of testing and monitoring than any intrinsic property of low permeability media. Accurate simulations of solute transport require a realistic accounting for water conducting features. Methods are presented to do so and examples are shown to illustrate these methods. Since detailed accounting of WCF's will not be possible in actual performance assessments, efforts should be directed towards typification, so as to identify the essential effects of WCF's on solute transport through different types of rocks. Field evidence suggests that, although individual WCF's may be difficult to characterize, their effects are quite predictable. (author)

  13. Clinical Significance of Histological Features of Thrombi in Patients with Myocardial Infarction

    Sebben, Juliana Canedo; Cambruzzi, Eduardo; Avena, Luisa Martins; Gazeta, Cristina do Amaral; Gottschall, Carlos Antonio Mascia; Quadros, Alexandre Schaan de, E-mail: quadros.pesquisa@gmail.com [Instituto de Cardiologia / Fundação Universitária de Cardiologia - IC/FUC, Porto Alegre, RS (Brazil)

    2013-12-15

    Percutaneous Coronary Intervention (PCI) is the most common strategy for the treatment of Acute ST segment elevation Myocardial Infarction (STEMI), and thromboaspiration has been increasingly utilized for removal of occlusive thrombi. To analyze the influence of histopathological features of coronary thrombi in clinical outcomes of patients with STEMI, and the association of these variables with clinical, angiographic, and laboratory features and medications used in hospitalization. Prospective cohort study. All patients were monitored during hospitalization and thirty days after the event. Aspirated thrombi were preserved in formalin and subsequently stained with hematoxylin-eosin and embedded in paraffin. Thrombi were classified as recent and old. The primary outcome was the occurrence of major cardiovascular events within thirty days. During the study period, 1,149 patients were evaluated with STEMI, and 331 patients underwent thrombi aspiration, leaving 199 patients available for analysis. It was identified recent thrombi in 116 patients (58%) and old thrombi in 83 patients (42%). Recent thrombi have greater infiltration of red blood cells than old thrombi (p = 0.02), but there were no statistically significant differences between other clinical, angiographic, laboratory, and histopathological features and medications in both group of patients. The rates of clinical outcomes were similar in both groups. Recent thrombi were identified in 58% of patients with STEMI and it was observed an association with infiltration of red blood cells. There was no association between histopathological features of thrombi and clinical variables and cardiovascular outcomes.

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

    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

  15. Variability in anatomical features of human clavicle: Its forensic anthropological and clinical significance

    Jagmahender Singh Sehrawat

    2016-06-01

    Full Text Available Bones can reflect the basic framework of human body and may provide valuable information about the biological identity of the deceased. They, often, survive the morphological alterations, taphonomic destructions, decay/mutilation and decomposition insults. In-depth knowledge of variations in clavicular shape, size and its dimensions is very important from both clinical (fixation of clavicular fractures using external or inter-medullary devices, designing orthopedic fixation devices as well as forensic anthropological perspectives. Human clavicle is the most frequently fractured bone of human skeleton, possessing high degree of variability in its anatomical, biomechanical and morphological features. Extended period of skeletal growth (up to third decade in clavicle imparts it an additional advantage for forensic identification purposes. In present study, five categories of clavicular features like lengths, diameters, angles, indices and robustness were examined to explore the suitability of collarbone for forensic and clinical purposes. For this purpose, 263 pairs of adult clavicles (195 Males and 68 Females were collected from autopsied cadavers and were studied for 13 anatomical features. Gender and occupational affiliations of cadavers were found to have significant influences on anatomical dimensions of their clavicles. Product index, weight and circumference of collarbone were found the best univariate variables, discriminating sex of more than 80% individuals. The best multivariate Function-I (DF: -17.315 + 0.054 CL-L+0.196 CC-R+0.184 DM-L could identify sex and occupation of 89.4% (89.2% Male and 89.7% Female and 65.4% individuals, respectively. All clavicular variables were found bilaterally asymmetric; left clavicles being significantly longer in length, lighter in weight, smooth in texture and less curved than the right side bones. Among non-metric traits, sub-clavian groove, nutrient foramina and ‘type’ of clavicle exhibited

  16. Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization

    Iwan Syarif

    2016-12-01

    Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.

  17. Evaluation of Persian Professional Web Social Networks\\\\\\' Features, to Provide a Suitable Solution for Optimization of These Networks in Iran

    Nadjla Hariri

    2013-03-01

    Full Text Available This study aimed to determine the status of Persian professional web social networks' features and provide a suitable solution for optimization of these networks in Iran. The research methods were library research and evaluative method, and study population consisted of 10 Persian professional web social networks. In this study, for data collection, a check list of social networks important tools and features was used. According to the results, “Cloob”, “IR Experts” and “Doreh” were the most compatible networks with the criteria of social networks. Finally, some solutions were presented for optimization of capabilities of Persian professional web social networks.

  18. The MRI features of placental adhesion disorder and their diagnostic significance: systematic review

    Rahaim, N.S.A.; Whitby, E.H.

    2015-01-01

    Aim: To identify the most frequently used MRI features in the diagnosis of placenta adhesion disorder (PAD) in the antenatal period and their significance. Materials and methods: The online databases Medline via PubMed and Ovid, Google Scholar, and Scopus were searched using the keywords and subject headings MRI*, magnetic resonance imaging*, prenatal diagnosis and placenta accreta*, morbidly adherent placenta* or placenta. Cases where MRI was carried out at/after 20 weeks gestation with detailed information available in relation to criteria and sequences used were included in the review. Exclusion criteria were case report study and studies that used intravenous contrast agents. Information regards sensitivity and specificity for each feature was taken, or calculated where possible, from the papers. Any new features were identified. The overall contribution of each feature to the diagnostic process was noted. Results: Six hundred and fourteen potentially relevant articles were identified of which only 11 met the inclusion criteria. The commonest MRI criteria used were T2 dark intraplacental bands, heterogeneity of placenta, abnormal uterine bulging, and disruption of the uteroplacental zone. A newly described criterion is disorganised vasculature of placenta. MRI sensitivity and specificity varied between 75–100% and 65–100% respectively. Conclusion: MRI diagnosis of PAD relies on unstandardised criteria of diagnosis that enable systematic image interpretation of invasion status in all studies and enable the reproducibility. However, it is still has a high diagnostic accuracy and frequently aids in surgical planning, emphasising its value in supporting ultrasound. Most studies are of a small sample size. Additional multicentre studies are recommended to enhance the generalisability of the findings and asses the value of the newly described criteria

  19. New approach to ECG's features recognition involving neural network

    Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.

    2001-01-01

    A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5

  20. Categorical Structure among Shared Features in Networks of Early-Learned Nouns

    Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

    2009-01-01

    The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

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

    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

  2. Significance of social networks in sustainable land management in ...

    Prof. Adipala Ekwamu

    multi-stakeholder Innovation Platforms (IPs) necessary for catalysing wide adoption of SLM innovations. This paper analyses the significance of SNs in sustainable land management (SLM), focusing on stakeholders' characteristics and their association among agricultural rural communities in central Ethiopia and eastern ...

  3. Cumulative Significance of Hyporheic Exchange and Biogeochemical Processing in River Networks

    Harvey, J. W.; Gomez-Velez, J. D.

    2014-12-01

    Biogeochemical reactions in rivers that decrease excessive loads of nutrients, metals, organic compounds, etc. are enhanced by hydrologic interactions with microbially and geochemically active sediments of the hyporheic zone. The significance of reactions in individual hyporheic flow paths has been shown to be controlled by the contact time between river water and sediment and the intrinsic reaction rate in the sediment. However, little is known about how the cumulative effects of hyporheic processing in large river basins. We used the river network model NEXSS (Gomez-Velez and Harvey, submitted) to simulate hyporheic exchange through synthetic river networks based on the best available models of network topology, hydraulic geometry and scaling of geomorphic features, grain size, hydraulic conductivity, and intrinsic reaction rates of nutrients and metals in river sediment. The dimensionless reaction significance factor, RSF (Harvey et al., 2013) was used to quantify the cumulative removal fraction of a reactive solute by hyporheic processing. SF scales reaction progress in a single pass through the hyporheic zone with the proportion of stream discharge passing through the hyporheic zone for a specified distance. Reaction progress is optimal where the intrinsic reaction timescale in sediment matches the residence time of hyporheic flow and is less efficient in longer residence time hyporheic flow as a result of the decreasing proportion of river flow that is processed by longer residence time hyporheic flow paths. In contrast, higher fluxes through short residence time hyporheic flow paths may be inefficient because of the repeated surface-subsurface exchanges required to complete the reaction. Using NEXSS we found that reaction efficiency may be high in both small streams and large rivers, although for different reasons. In small streams reaction progress generally is dominated by faster pathways of vertical exchange beneath submerged bedforms. Slower exchange

  4. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

    2005-01-01

    The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

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

    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...

  6. Economic Features of the Internet and Network Neutrality

    Nicholas Economides

    2015-01-01

    We discuss the issue of a possible abolition of network neutrality and the introduction of paid prioritization by residential broadband access networks.We show that, in short run analysis where bandwidth is fixed, and in the absence of congestion, network neutrality tends to maximize total surplus. When an ISP violates network neutrality and invests the extra profits to bandwidth expansion, the presence of more bandwidth alleviates the allocative distortion, and can even reverse it. We also d...

  7. Extracting intrinsic functional networks with feature-based group independent component analysis.

    Calhoun, Vince D; Allen, Elena

    2013-04-01

    There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

  8. Pulmonary spheral tuberculosis: features and clinical significance of spiral dynamic CT

    Xie Ruming; Ma Daqing; Li Tieyi; Chen Yi; Lu Fudong; Zhou Xinhua

    2001-01-01

    Objective: To assess the features and clinical significance of spiral dynamic CT in patients with pulmonary spheral tuberculosis. Methods: The 54 foci in 42 patients with pulmonary spheral tuberculosis were studied. Thin-sections at 2 mm thickness and 2 mm interval through the nodular center were obtained before and after administration of contrast material. Results: In 54 pulmonary spheral tuberculosis, maximum enhanced CT value in 51 (94.4%, 51/54) foci was less than 20 HU, and more than 20 HU in the other 3(5.6%, 3/54) foci. 27(50.0%, 27/54) foci showed no any enhancement, 24, (44%, 24/54) foci showed capsular enhancement, 1(1.9%, 1/54) focus showed peripheral enhancement and 2(3.7%, 2/54) foci showed extensive enhancement. The accuracy of the correct diagnosis was 25.9% in terms of plain CT and 94.4% in terms of enhanced CT scanning. The difference was significant (x 2 = 50.1, P < 0.05). The curative effect of extensive enhanced foci and peripheral enhanced foci was optimal, capsular enhanced foci was second, and non-enhanced foci was barely satisfactory. Conclusion: Spiral dynamic CT technique may improve the accuracy of diagnosing pulmonary spheral tuberculosis. No enhancement and/or capsular enhancement were suggestive of tuberculosis. The enhancing character of foci might contribute to assess the curative effect of anti-tuberculosis

  9. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  10. Mesenteric Lymphadenopathy in Childhood Epidemic Aseptic Meningitis: Sonographic Features and Clinical Significance

    Mun, Sung Hee; Park, Young Chan; Lee, Young Hwan

    2006-01-01

    To evaluate the sonographic features of mesenteric lymphadenopathy in childhood epidemic aseptic meningitis and to assess their clinical significance. Thirty-three patients (25 male, 8 female: mean age, 8.6 years) with a diagnosis of aseptic meningitis were prospectively evaluated with abdominal ultrasonography for the presence of enlarged mesenteric nodes. The size and number of enlarged mesenteric lymph nodes were analyzed in relationship with the patient's age, between the patients with abdominal pain or diarrhea (16 cases, 48%) and asymptomatic patients (17 cases, 52%). Mesenteric lymphadenopathy was seen in 31 patients (94%), all 16 symptomatic and 15 of the 17 asymptomatic patients. The number of enlarged nodes was most prevalent between 6-10, seen in 16 patients (52%) and the largest node ranged in size from 4 to 8 mm. Among the 31 patients with mesenteric lymphadenopathy, the mean size of the largest node was statistically different between the symptomatic (6.0 mm) and asymptomatic (5.0 mm) groups (p = 0.021). The number of enlarged nodes and the patient's age were not statistically different between the two groups. Mesenteric lymphadenopathy was seen in almost all cases of childhood epidemic aseptic meningitis, and may be related to the mesenteric lymphadenitis caused by enterovirus

  11. Mesenteric Lymphadenopathy in Childhood Epidemic Aseptic Meningitis: Sonographic Features and Clinical Significance

    Mun, Sung Hee; Park, Young Chan; Lee, Young Hwan [Catholic University of Daegu, College of Medicine, Daegu (Korea, Republic of)

    2006-09-15

    To evaluate the sonographic features of mesenteric lymphadenopathy in childhood epidemic aseptic meningitis and to assess their clinical significance. Thirty-three patients (25 male, 8 female: mean age, 8.6 years) with a diagnosis of aseptic meningitis were prospectively evaluated with abdominal ultrasonography for the presence of enlarged mesenteric nodes. The size and number of enlarged mesenteric lymph nodes were analyzed in relationship with the patient's age, between the patients with abdominal pain or diarrhea (16 cases, 48%) and asymptomatic patients (17 cases, 52%). Mesenteric lymphadenopathy was seen in 31 patients (94%), all 16 symptomatic and 15 of the 17 asymptomatic patients. The number of enlarged nodes was most prevalent between 6-10, seen in 16 patients (52%) and the largest node ranged in size from 4 to 8 mm. Among the 31 patients with mesenteric lymphadenopathy, the mean size of the largest node was statistically different between the symptomatic (6.0 mm) and asymptomatic (5.0 mm) groups (p = 0.021). The number of enlarged nodes and the patient's age were not statistically different between the two groups. Mesenteric lymphadenopathy was seen in almost all cases of childhood epidemic aseptic meningitis, and may be related to the mesenteric lymphadenitis caused by enterovirus

  12. Expression features and prognostic significance of Yes-associated protein in hepatocellular carcinoma and cholangiocellular carcinoma

    WANG Chun

    2017-07-01

    Full Text Available ObjectiveTo investigate the expression of Yes-associated protein (YAP in hepatocellular carcinoma (HCC and cholangiocellular carcinoma (CC and its association with clinical prognosis. MethodsSamples were collected from 190 patients who were treated in The Second Hospital Affiliated to Chongqing Medical University from July 2004 to July 2009, among whom 110 had HCC and 80 had CC. The difference in YAP expression and its association were analyzed in both groups, and patients′ prognosis was compared between the two groups. The chi-square test was used to investigate the association between YAP expression and clinicopathological features of HCC and CC, and the Kaplan-Meier method and the log-rank test were used to assess tumor-free survival rate and overall survival rate. A univariate Cox regression analysis was used to evaluate the influence of YAP expression on the prognosis of patients with HCC and CC. ResultsThe CC group had higher expression of YAP than the HCC group (68.7% vs 56.3%, P=0.036. High YAP expression in HCC and CC was significantly associated with tumor size (P<0.001 and P=0.024, alpha fetoprotein (P=0.009 and 0034, liver cirrhosis (P=0032 and 0.006, vascular invasion (P=0.011 and 0.028, and intrahepatic metastasis (P=0.049 and 0030. In both groups, the patients with high YAP expression had significantly lower tumor-free survival rate and overall survival rate than those with low YAP expression(all P<005. Multivariate analysis showed that high YAP expression is an adverse prognostic factor for tumor-free survival and overall survival in both groups (all P<005. ConclusionHigh YAP expression is frequently found in patients with HCC and CC, and high YAP expression is associated with low survival rate.

  13. Complex network approach to characterize the statistical features of the sunspot series

    Zou, Yong; Liu, Zonghua; Small, Michael; Kurths, Jürgen

    2014-01-01

    Complex network approaches have been recently developed as an alternative framework to study the statistical features of time-series data. We perform a visibility-graph analysis on both the daily and monthly sunspot series. Based on the data, we propose two ways to construct the network: one is from the original observable measurements and the other is from a negative-inverse-transformed series. The degree distribution of the derived networks for the strong maxima has clear non-Gaussian properties, while the degree distribution for minima is bimodal. The long-term variation of the cycles is reflected by hubs in the network that span relatively large time intervals. Based on standard network structural measures, we propose to characterize the long-term correlations by waiting times between two subsequent events. The persistence range of the solar cycles has been identified over 15–1000 days by a power-law regime with scaling exponent γ = 2.04 of the occurrence time of two subsequent strong minima. In contrast, a persistent trend is not present in the maximal numbers, although maxima do have significant deviations from an exponential form. Our results suggest some new insights for evaluating existing models. (paper)

  14. Palaeopedogenic features and their palaeoclimatological significance for the nevremont formation (Lower Givetian), the Northern Ardennes, Belgium

    Molenaar, N.

    1984-01-01

    The lower member of the Nèvremont Formation is characterized by the frequent occurrence of pedogenic features, which suggest intermittent exposure of the fluvial depositional environment. The evidence for pedogenesis comprises horizons of calcite glaebules and nodular calcrete, haematite

  15. Unveiling network-based functional features through integration of gene expression into protein networks.

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. Effect of dominant features on neural network performance in the classification of mammographic lesions

    Zhimin Huo; Giger, M.L.; Metz, C.E.

    1999-01-01

    Two different classifiers, an artificial neural network (Ann) and a hybrid system (one step rule-based method followed by an artificial neural network) have been investigated to merge computer-extracted features in the task of differentiating between malignant and benign masses. A database consisting of 65 cases (38 malignant and 26 benign) was used in the study. A total of four computer-extracted features - spiculation, margin sharpness and two density-related measures - was used to characterize these masses. Results from our previous study showed that the hybrid system performed better than the ANN classifier. In our current study, to understand the difference between the two classifiers, we investigated their learning and decision-making processes by studying the relationships between the input features and the outputs. A correlation study showed that the outputs from the ANN-alone method correlated strongly with one of the input features (spiculation), yielding a correlation coefficient of 0.91, whereas the correlation coefficients (absolute value) for the other features ranged from 0.19 to 0.40. This strong correlation between the ANN output and spiculation measure indicates that the learning and decision-making processes of the ANN-alone method were dominated by the spiculation measure. Three-dimensional plots of the computer output as functions of the input features demonstrate that the ANN-alone method did not learn as effectively as the hybrid system in differentiating non-spiculated malignant masses from benign masses, thus resulting in an inferior performance at the high sensitivity levels. We found that with a limited database it is detrimental for an ANN to learn the significance of other features in the presence of a dominant feature. The hybrid system, which initially applied a rule concerning the value of the spiculation measure prior to employing an ANN, prevents over-learning from the dominant feature and performed better than the ANN-alone method

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

    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.

  18. Global-local feature attention network with reranking strategy for image caption generation

    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.

  19. An automated approach to network features of protein structure ensembles

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

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

    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.

  1. Seismic signal auto-detecing from different features by using Convolutional Neural Network

    Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

    2017-12-01

    We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

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

    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.

  3. 77 FR 37730 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    2012-06-22

    ... DEPARTMENT OF STATE [Public Notice 7928] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  4. 77 FR 7229 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    2012-02-10

    ... DEPARTMENT OF STATE [Public Notice 7794] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  5. Acute liver allograft antibody-mediated rejection: an inter-institutional study of significant histopathological features.

    O'Leary, Jacqueline G; Michelle Shiller, S; Bellamy, Christopher; Nalesnik, Michael A; Kaneku, Hugo; Jennings, Linda W; Isse, Kumiko; Terasaki, Paul I; Klintmalm, Göran B; Demetris, Anthony J

    2014-10-01

    Acute antibody-mediated rejection (AMR) occurs in a small minority of sensitized liver transplant recipients. Although histopathological characteristics have been described, specific features that could be used (1) to make a generalizable scoring system and (2) to trigger a more in-depth analysis are needed to screen for this rare but important finding. Toward this goal, we created training and validation cohorts of putative acute AMR and control cases from 3 high-volume liver transplant programs; these cases were evaluated blindly by 4 independent transplant pathologists. Evaluations of hematoxylin and eosin (H&E) sections were performed alone without knowledge of either serum donor-specific human leukocyte antigen alloantibody (DSA) results or complement component 4d (C4d) stains. Routine histopathological features that strongly correlated with severe acute AMR included portal eosinophilia, portal vein endothelial cell hypertrophy, eosinophilic central venulitis, central venulitis severity, and cholestasis. Acute AMR inversely correlated with lymphocytic venulitis and lymphocytic portal inflammation. These and other characteristics were incorporated into models created from the training cohort alone. The final acute antibody-mediated rejection score (aAMR score)--the sum of portal vein endothelial cell hypertrophy, portal eosinophilia, and eosinophilic venulitis divided by the sum of lymphocytic portal inflammation and lymphocytic venulitis--exhibited a strong correlation with severe acute AMR in the training cohort [odds ratio (OR) = 2.86, P  1.75 (sensitivity = 34%, specificity = 86%) and another that optimized sensitivity at a score > 1.0 (sensitivity = 81%, specificity = 71%). In conclusion, the routine histopathological features of the aAMR score can be used to screen patients for acute AMR via routine H&E staining of indication liver transplant biopsy samples; however, a definitive diagnosis requires substantiation by DSA testing

  6. Traction bronchiectasis in cryptogenic fibrosing alveolitis: associated computed tomographic features and physiological significance

    Desai, Sujal R.; Wells, Athol U.; Bois, Roland M. du; Rubens, Michael B.; Hansell, David M.

    2003-01-01

    Our objective was to evaluate the associated CT features and physiological consequences of traction bronchiectasis in patients with cryptogenic fibrosing alveolitis (CFA). The CT scans of 212 patients with CFA (158 men, 54 women; mean age 62.2±10.6 years) were evaluated independently by two observers. The extent of fibrosis, the proportions of a reticular pattern and ground-glass opacification and the extent of emphysema were scored at five levels. The predominant CT pattern, coarseness of a reticular pattern and severity of traction bronchiectasis were graded semiquantitatively. Physiological indices were correlated with CT features. There was traction bronchiectasis on CT in 202 of 212 (95%) patients. Increasingly severe traction bronchiectasis was independently associated with increasingly extensive CFA (p CO (p 2 (p<0.0005), but not indices of air-flow obstruction. In CFA, traction bronchiectasis increases with more extensive disease, a lower proportion of ground-glass opacification and a coarser reticular pattern, but it decreases with concurrent emphysema. Increasingly severe traction bronchiectasis is associated with additional physiological impairment for a given extent of pulmonary fibrosis and emphysema. (orig.)

  7. Traction bronchiectasis in cryptogenic fibrosing alveolitis: associated computed tomographic features and physiological significance

    Desai, Sujal R. [Department of Radiology, King' s College Hospital, Denmark Hill, SE5 9RS, London (United Kingdom); Wells, Athol U.; Bois, Roland M. du [Interstitial Lung Disease Unit, Royal Brompton Hospital, Emmanuel Kaye Building, Manresa Road, Fulham, SW6 6LR, London (United Kingdom); Rubens, Michael B.; Hansell, David M. [Department of Radiology, Royal Brompton Hospital, Sydney Street, SW3 6NP, London (United Kingdom)

    2003-08-01

    Our objective was to evaluate the associated CT features and physiological consequences of traction bronchiectasis in patients with cryptogenic fibrosing alveolitis (CFA). The CT scans of 212 patients with CFA (158 men, 54 women; mean age 62.2{+-}10.6 years) were evaluated independently by two observers. The extent of fibrosis, the proportions of a reticular pattern and ground-glass opacification and the extent of emphysema were scored at five levels. The predominant CT pattern, coarseness of a reticular pattern and severity of traction bronchiectasis were graded semiquantitatively. Physiological indices were correlated with CT features. There was traction bronchiectasis on CT in 202 of 212 (95%) patients. Increasingly severe traction bronchiectasis was independently associated with increasingly extensive CFA (p<0.0005), a coarser reticular pattern (p<0.001), a lower proportion of ground-glass opacification (p<0.005) and less extensive emphysema (p<0.0005). Increasingly severe traction bronchiectasis was independently related to depression of DL{sub CO} (p<0.005), FVC (p=0.02) and pO{sub 2} (p<0.0005), but not indices of air-flow obstruction. In CFA, traction bronchiectasis increases with more extensive disease, a lower proportion of ground-glass opacification and a coarser reticular pattern, but it decreases with concurrent emphysema. Increasingly severe traction bronchiectasis is associated with additional physiological impairment for a given extent of pulmonary fibrosis and emphysema. (orig.)

  8. Bayesian latent feature modeling for modeling bipartite networks with overlapping groups

    Jørgensen, Philip H.; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2016-01-01

    Bi-partite networks are commonly modelled using latent class or latent feature models. Whereas the existing latent class models admit marginalization of parameters specifying the strength of interaction between groups, existing latent feature models do not admit analytical marginalization...... by the notion of community structure such that the edge density within groups is higher than between groups. Our model further assumes that entities can have different propensities of generating links in one of the modes. The proposed framework is contrasted on both synthetic and real bi-partite networks...... feature representations in bipartite networks provides a new framework for accounting for structure in bi-partite networks using binary latent feature representations providing interpretable representations that well characterize structure as quantified by link prediction....

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

    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...

  10. D3.5 Report on ECO social network integration features

    Viñuales, Javier; Driesner, Jorge; Tejera, Sara; Tomasini, Alessandra; Loozen, Kjeld; Rocio, Vítor; Bohuschke, Felix; Ternier, Stefaan

    2016-01-01

    This document describes how integrations with social networks are being developed in ECO platforms. With these new features, participants will be able to share results and other contents through Facebook, Twitter, Google plus.

  11. The Significant Social Networks of Women Who Have Resided in Shelters

    Scheila Krenkel

    2015-04-01

    Full Text Available The social and institutional support networks structured around women who suffer violence are strategic tools when coping with the phenomenon, which is considered a public health problem. This qualitative study was aimed at understanding the relational dynamics of significant social networks of women who have experienced family violence and have resided in a shelter. A group of 12 women participated in the study and data collection was carried out through semi-structured interviews and the social networks map. Data analysis was based on Grounded Theory and performed using the software Atlas.ti 5.0. The results revealed that the significant social networks were important sources of help and support in the process of coping with violence experienced by women. Results also showed that the persons in the social networks develop multiple functions and present an increasing level of relational commitment to women, especially after they leave the shelter.

  12. An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

    Zena M Hira

    Full Text Available Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.

  13. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

    Joshua D. Campbell; Christina Yau; Reanne Bowlby; Yuexin Liu; Kevin Brennan; Huihui Fan; Alison M. Taylor; Chen Wang; Vonn Walter; Rehan Akbani; Lauren Averett Byers; Chad J. Creighton; Cristian Coarfa; Juliann Shih; Andrew D. Cherniack

    2018-01-01

    Summary: This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mes...

  14. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history si...

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

    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.

  16. Prediction of interface residue based on the features of residue interaction network.

    Jiao, Xiong; Ranganathan, Shoba

    2017-11-07

    Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Control range: a controllability-based index for node significance in directed networks

    Wang, Bingbo; Gao, Lin; Gao, Yong

    2012-01-01

    While a large number of methods for module detection have been developed for undirected networks, it is difficult to adapt them to handle directed networks due to the lack of consensus criteria for measuring the node significance in a directed network. In this paper, we propose a novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks. The control range of a node quantifies the size of the subnetwork that the node can effectively control. A related index, called the control range similarity, is also introduced to measure the structural similarity between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the nodes is mainly influenced by the network's degree distribution and that nodes with a low degree may have a high control range. We use the index of control range similarity to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens. Our results, as compared with other approaches to module detection such as modularity optimization algorithm, dynamic algorithm and clique percolation method, indicate that the proposed indices are effective and practical in depicting structural and modular characteristics of sparse directed networks

  18. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

    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.

  19. TEMPO: an ESA-funded project for uncovering significant features of the South Atlantic Anomaly

    Pavón-Carrasco, F. Javier; De Santis, Angelo

    2016-04-01

    In this work we provide the last results of the ESA (European Space Agency) funded project TEMPO ("Is The Earth's Magnetic field POtentially reversing? New insights from Swarm mission"). The mail goal of this project is to analyse the time and spatial evolution of one of the most important features of the present geomagnetic field, i.e. the South Atlantic Anomaly (SAA). The region covered by this anomaly is characterized by values of geomagnetic field intensity around 30% lower than expected for those latitudes and extends over a large area in the South Atlantic Ocean, South America, South Africa and the Eastern Pacific Ocean. This large depression of the geomagnetic field strength has its origin in a prominent patch of reversed polarity flux in the Earth's outer core. The study of the SAA is an important challenge nowadays not only for the geomagnetic and paleomagnetic community, but also for other areas focused on the Earth Observation due to the protective role of this potential field against the charged particles forming the solar wind. A further increase of the SAA surface extent could have dramatic consequences for human health and technologies because a larger number of solar charged particles could reach the Earth's surface.

  20. GalaxyGAN: Generative Adversarial Networks for recovery of galaxy features

    Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Krishnan Santhanam, Gokula

    2017-02-01

    GalaxyGAN uses Generative Adversarial Networks to reliably recover features in images of galaxies. The package uses machine learning to train on higher quality data and learns to recover detailed features such as galaxy morphology by effectively building priors. This method opens up the possibility of recovering more information from existing and future imaging data.

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

    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

  2. Feature-based automatic color calibration for networked camera system

    Yamamoto, Shoji; Taki, Keisuke; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

    2011-01-01

    In this paper, we have developed a feature-based automatic color calibration by using an area-based detection and adaptive nonlinear regression method. Simple color matching of chartless is achieved by using the characteristic of overlapping image area with each camera. Accurate detection of common object is achieved by the area-based detection that combines MSER with SIFT. Adaptive color calibration by using the color of detected object is calculated by nonlinear regression method. This method can indicate the contribution of object's color for color calibration, and automatic selection notification for user is performed by this function. Experimental result show that the accuracy of the calibration improves gradually. It is clear that this method can endure practical use of multi-camera color calibration if an enough sample is obtained.

  3. The Radiologic Features of Cystic versus Noncystic Glioblastoma Multiforme as Significant Prognostic Factors

    Choi, Seung Joon; Hwang, Hee Young; Kim, Na Rae; Lee, Sheen Woo; Kim, Jeong Ho; Choi, Hye Young; Kim, Hyung Sik

    2010-01-01

    The purpose of this study was to determine the preoperative radiological characteristic and survival differences of glioblastoma multiforme (GBM) with and without cysts. Twenty-one GBMs were collected retrospectively; these tumors were pathologic confirmed as GBM. Based on the preoperative MR imaging, we compared the cystic GBMs with the noncystic GBMs according to the the tumor size, the tumor interface, the tumor wall thickness and peritumoral edema. Seven cases were classified as cystic GBMs and fourteen were noncystic GBMs. The cystic GBMs had a well-defined tumor interface, a less than 2 cm thickness of the tumor wall and less than 40 cm 3 thick peritumoral edema as compared to that of the noncystic GBMs. There was a statistically significant difference in age between the patients with cystic tumors and those with noncystic tumors. For the patients with cystic GBMs and noncystic GBMs, median survival time after surgery was 43.8 months and 12.5 months, respectively. The cystic GBMs had a well-defined tumor interface, a thin wall and minimal edema, as compared with that of the noncystic GBMs. The patients with cystic GBMs were significantly younger and they had more favorable survival outcomes than did the patients with noncystic GBMs

  4. Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit

    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.

  5. An input feature selection method applied to fuzzy neural networks for signal esitmation

    Na, Man Gyun; Sim, Young Rok

    2001-01-01

    It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

  6. Considerations on command and response language features for a network of heterogeneous autonomous computers

    Engelberg, N.; Shaw, C., III

    1984-01-01

    The design of a uniform command language to be used in a local area network of heterogeneous, autonomous nodes is considered. After examining the major characteristics of such a network, and after considering the profile of a scientist using the computers on the net as an investigative aid, a set of reasonable requirements for the command language are derived. Taking into account the possible inefficiencies in implementing a guest-layered network operating system and command language on a heterogeneous net, the authors examine command language naming, process/procedure invocation, parameter acquisition, help and response facilities, and other features found in single-node command languages, and conclude that some features may extend simply to the network case, others extend after some restrictions are imposed, and still others require modifications. In addition, it is noted that some requirements considered reasonable (user accounting reports, for example) demand further study before they can be efficiently implemented on a network of the sort described.

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

    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...

  8. Historical feature pattern extraction based network attack situation sensing algorithm.

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  9. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    Yong Zeng

    2014-01-01

    Full Text Available The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE. First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  10. CT diagnosis of splenic infarction in blunt trauma: imaging features, clinical significance and complications

    Miller, L.A.; Mirvis, S.E.; Shanmuganathan, K.; Ohson, A.S.

    2004-01-01

    AIM: The object of this study is to describe the appearance, complications, and outcome of segmental splenic infarctions occurring after blunt trauma using computed tomography (CT). MATERIALS AND METHODS: Thirteen blunt trauma patients were identified with splenic infarction on contrast-enhanced CT. CT images were retrospectively reviewed and the percentage of infarcted splenic tissue and presence of splenic injury separate from the site of infarction were identified. Splenic angiograms were reviewed and follow-up CT images were assessed for interval change in the appearance of the infarcts. RESULTS: The mean age of patients was 32 years and the most common mechanism of injury was road traffic accident. The majority (54%) had 25-50% infarction of the spleen. Splenic angiograms were performed in nine patients and seven demonstrated wedge-shaped regions of decreased perfusion corresponding to the infarction seen on CT with no need for intervention. Eleven patients underwent a follow-up CT that demonstrated the following: no significant change in six, near-complete resolution in two, delayed appearance of infarction in one, abscess formation in one, and delayed splenic rupture in one. CONCLUSION: Segmental splenic infarction is a rare manifestation of blunt splenic trauma. The diagnosis is readily made using contrast-enhanced CT. The majority will decrease in size on follow-up CT and resolve without clinical sequelae. Resolution of infarction is also seen and these cases are best described as temporary perfusion defects. Splenic abscess or delayed rupture are uncommon complications that may necessitate angiographic or surgical intervention

  11. CT diagnosis of splenic infarction in blunt trauma: imaging features, clinical significance and complications

    Miller, L.A.; Mirvis, S.E.; Shanmuganathan, K.; Ohson, A.S. E-mail: lmiller@um.edu

    2004-04-01

    AIM: The object of this study is to describe the appearance, complications, and outcome of segmental splenic infarctions occurring after blunt trauma using computed tomography (CT). MATERIALS AND METHODS: Thirteen blunt trauma patients were identified with splenic infarction on contrast-enhanced CT. CT images were retrospectively reviewed and the percentage of infarcted splenic tissue and presence of splenic injury separate from the site of infarction were identified. Splenic angiograms were reviewed and follow-up CT images were assessed for interval change in the appearance of the infarcts. RESULTS: The mean age of patients was 32 years and the most common mechanism of injury was road traffic accident. The majority (54%) had 25-50% infarction of the spleen. Splenic angiograms were performed in nine patients and seven demonstrated wedge-shaped regions of decreased perfusion corresponding to the infarction seen on CT with no need for intervention. Eleven patients underwent a follow-up CT that demonstrated the following: no significant change in six, near-complete resolution in two, delayed appearance of infarction in one, abscess formation in one, and delayed splenic rupture in one. CONCLUSION: Segmental splenic infarction is a rare manifestation of blunt splenic trauma. The diagnosis is readily made using contrast-enhanced CT. The majority will decrease in size on follow-up CT and resolve without clinical sequelae. Resolution of infarction is also seen and these cases are best described as temporary perfusion defects. Splenic abscess or delayed rupture are uncommon complications that may necessitate angiographic or surgical intervention.

  12. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

    Joshua D. Campbell

    2018-04-01

    Full Text Available Summary: This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs from five sites associated with smoking and/or human papillomavirus (HPV. SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs, DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+ and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches. : Campbell et al. reveal that squamous cell cancers from different tissue sites may be distinguished from other cancers and subclassified molecularly by recurrent alterations in chromosomes, DNA methylation, messenger and microRNA expression, or by mutations. These affect squamous cell pathways and programs that provide candidates for therapy. Keywords: genomics, transcriptomics, proteomics, head and neck squamous cell carcinoma, lung squamous cell carcinoma, esophageal squamous cell carcinoma, cervical squamous cell carcinoma, bladder carcinoma with squamous differentiation, human papillomavirus

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

    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

  14. An approach to evaluate the topological significance of motifs and other patterns in regulatory networks

    Wingender Edgar

    2009-05-01

    that enables to evaluate the topological significance of various connected patterns in a regulatory network. Applying this method onto transcriptional networks of three largely distinct organisms we could prove that it is highly suitable to identify most important pattern instances, but that neither motifs nor any pattern in general appear to play a particularly important role per se. From the results obtained so far, we conclude that the pairwise disconnectivity index will most likely prove useful as well in identifying other (higher-order pattern instances in transcriptional and other networks.

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

    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.

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

    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.

  17. Critical features of coupling parameter in synchronization of small world neural networks

    Li Yanlong; Ma Jun; Xu Wenke; Li Hongbo; Wu Min

    2008-01-01

    The critical features of a coupling parameter in the synchronization of small world neural networks are investigated. A power law decay form is observed in this spatially extended system, the larger linked degree becomes, the larger critical coupling intensity. There exists maximal and minimal critical coupling intensity for synchronization in the extended system. An approximate synchronization diagram has been constructed. In the case of partial coupling, a primary result is presented about the critical coupling fraction for various linked degree of networks

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

    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.

  19. Optical implementation of a feature-based neural network with application to automatic target recognition

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  20. Automatic target recognition using a feature-based optical neural network

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

  1. Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

    Su-Zhe Wang

    2016-01-01

    Full Text Available Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

  2. Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

    Horikawa, Tomoyasu; Kamitani, Yukiyasu

    2017-01-01

    Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

  3. Features of complex networks in a free-software operating system

    Nair, Rajiv; Nagarjuna, G; Ray, Arnab K

    2012-01-01

    We propose a mathematical model to fit the degree distribution of directed dependency networks in free and open-source software. In this complex system, the intermediate scales of both the in-directed and out-directed dependency networks follow a power-law trend (specifically Zipf's law). Deviations from this feature are found both for the highly linked nodes, and the poorly linked nodes. This is due to finite-size effects in the networks, and the parameters needed to model finite-size behaviour make a quantitative distinction between the in-directed and out-directed networks. We also provide a model to describe the dynamic evolution of the network, and account for its saturation in the long-time limit.

  4. Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

    Ettore Mosca

    2017-09-01

    Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

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

    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.

  6. Marketplace Plans With Narrow Physician Networks Feature Lower Monthly Premiums Than Plans With Larger Networks.

    Polsky, Daniel; Cidav, Zuleyha; Swanson, Ashley

    2016-10-01

    The introduction of health insurance Marketplaces under the Affordable Care Act has been associated with growth of restricted provider networks. The value of this plan design strategy, including its association with lower premiums, is uncertain. We used data from all silver plans offered in the 2014 health insurance exchanges in the fifty states and the District of Columbia to estimate the association between the breadth of a provider network and plan premiums. We found that within a market, for plans of otherwise equivalent design and controlling for issuer-specific pricing strategy, a plan with an extra-small network had a monthly premium that was 6.7 percent less expensive than that of a plan with a large network. Because narrow networks remain an important strategy available to insurance companies to offer lower-cost plans on health insurance Marketplaces, the success of health insurance coverage expansions may be tied to the successful implementation of narrow networks. Project HOPE—The People-to-People Health Foundation, Inc.

  7. Intraocular pressure asymmetry is not a clinically-significant feature when using the PULSAIR non-contact tonometer.

    Pointer, J S

    1997-11-01

    This report describes the results of a retrospective analysis of intraocular pressure (i.o.p.) values recorded from the right (R) and left (L) eyes of middle-aged and elderly at-risk but assumed non-glaucomatous subjects. The tensions had been measured using the Keeler PULSAIR non-contact tonometer (NCT) in the course of routine optometric practice when individuals attended for a sight test. These bilateral IOP data were collated on the basis of each subject's gender, (male/female), age (40-59 years/60+ years) and the time of the tonometry assessment (a.m./p.m.). Wherever possible material was equi-partitioned across these three bipartite variables producing balanced data groupings. Pair-wise testing of R versus L absolute values of pneumo-applanation pressures across any of the balanced data groupings failed to reveal a statistically-significant difference between the paired IOP distributions. There was a consistent but small relative IOP asymmetry (L > R) in these data. Further analysis indicated that this asymmetry only attained borderline statistical significance with respect to subject's age: neither gender nor the time of assessment were statistically significant features, and there were no statistically-significant interactions between any of the three variables. In conclusion, provided that the manufacturer's operating instructions are adhered to, IOP asymmetry is not a clinically-significant feature when using the PULSAIR NCT on a clinical population at risk of developing glaucoma.

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

    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.

  9. Neural network-based feature point descriptors for registration of optical and SAR images

    Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

    2018-04-01

    Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

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

    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...

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

    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...

  12. Local Area Network Material Accounting System (LANMAS) Functions and Features Overview

    Robichaux, J.J.

    1998-07-01

    The Local Area Network Material Accounting System (LANMAS) application is a standardized approach to comply with the DOE Order 5633.3B, control and Accountability of Nuclear Material, material accounting requirements. This paper provides a general overview of the functions and features included in the LANMAS application

  13. Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

    Zheng, B. [Univ. of South Florida, Tampa, FL (United States)]|[Nanjing Univ. of Posts and Telecommunications (China). Dept. of Telecommunication Engineering; Qian, W.; Clarke, L.P. [Univ. of South Florida, Tampa, FL (United States)

    1996-10-01

    A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC`s) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC`s and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP`s MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed.

  14. Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

    Zheng, B.

    1996-01-01

    A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC's) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC's and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP's MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed

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

    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

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

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

    2018-02-05

    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 Elsevier B.V. All rights reserved.

  17. Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

    Wu Xiaogang

    2012-06-01

    Full Text Available In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease, and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI database, and pathway enrichment from the human pathway database (HPD. We use a recently-published microarray dataset (GSE24215 related to insulin resistance and type 2 diabetes (T2D as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

  18. Social networking strategies that aim to reduce obesity have achieved significant although modest results.

    Ashrafian, Hutan; Toma, Tania; Harling, Leanne; Kerr, Karen; Athanasiou, Thanos; Darzi, Ara

    2014-09-01

    The global epidemic of obesity continues to escalate. Obesity accounts for an increasing proportion of the international socioeconomic burden of noncommunicable disease. Online social networking services provide an effective medium through which information may be exchanged between obese and overweight patients and their health care providers, potentially contributing to superior weight-loss outcomes. We performed a systematic review and meta-analysis to assess the role of these services in modifying body mass index (BMI). Our analysis of twelve studies found that interventions using social networking services produced a modest but significant 0.64 percent reduction in BMI from baseline for the 941 people who participated in the studies' interventions. We recommend that social networking services that target obesity should be the subject of further clinical trials. Additionally, we recommend that policy makers adopt reforms that promote the use of anti-obesity social networking services, facilitate multistakeholder partnerships in such services, and create a supportive environment to confront obesity and its associated noncommunicable diseases. Project HOPE—The People-to-People Health Foundation, Inc.

  19. Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

    Mehdi Maboudi

    2016-08-01

    Full Text Available Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

  20. Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

    Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

    2018-01-01

    Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

  1. Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

    Jaehong Yoon

    2018-01-01

    Full Text Available Feature of event-related potential (ERP has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

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

    2011-03-01

    displays active TCP connections, ports on which the computer is listening and their associated PID, Ethernet statistics, the IP routing table, IPv4 ...who find new ways of infiltration rendering the selection of only those few features that worked in the past history to be easily thwarted when faced

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

    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. Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

    Soheila Gheisari

    2018-01-01

    Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

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

    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.

  6. Artificial Neural Networks and Gene Expression Programing based age estimation using facial features

    Baddrud Z. Laskar

    2015-10-01

    Full Text Available This work is about estimating human age automatically through analysis of facial images. It has got a lot of real-world applications. Due to prompt advances in the fields of machine vision, facial image processing, and computer graphics, automatic age estimation via faces in computer is one of the dominant topics these days. This is due to widespread real-world applications, in areas of biometrics, security, surveillance, control, forensic art, entertainment, online customer management and support, along with cosmetology. As it is difficult to estimate the exact age, this system is to estimate a certain range of ages. Four sets of classifications have been used to differentiate a person’s data into one of the different age groups. The uniqueness about this study is the usage of two technologies i.e., Artificial Neural Networks (ANN and Gene Expression Programing (GEP to estimate the age and then compare the results. New methodologies like Gene Expression Programing (GEP have been explored here and significant results were found. The dataset has been developed to provide more efficient results by superior preprocessing methods. This proposed approach has been developed, tested and trained using both the methods. A public data set was used to test the system, FG-NET. The quality of the proposed system for age estimation using facial features is shown by broad experiments on the available database of FG-NET.

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

    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

  8. Vascular dynamics aid a coupled neurovascular network learn sparse independent features: A computational model

    Ryan Thomas Philips

    2016-02-01

    Full Text Available Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as `hemoneural hypothesis' postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of `vascular units' supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its `projective field'. Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto

  9. Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model.

    Chherawala, Youssouf; Roy, Partha Pratim; Cheriet, Mohamed

    2016-12-01

    The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.

  10. Development and validation of a survey to measure features of clinical networks.

    Brown, Bernadette Bea; Haines, Mary; Middleton, Sandy; Paul, Christine; D'Este, Catherine; Klineberg, Emily; Elliott, Elizabeth

    2016-09-30

    Networks of clinical experts are increasingly being implemented as a strategy to improve health care processes and outcomes and achieve change in the health system. Few are ever formally evaluated and, when this is done, not all networks are equally successful in their efforts. There is a need to formatively assess the strategic and operational management and leadership of networks to identify where functioning could be improved to maximise impact. This paper outlines the development and psychometric evaluation of an Internet survey to measure features of clinical networks and provides descriptive results from a sample of members of 19 diverse clinical networks responsible for evidence-based quality improvement across a large geographical region. Instrument development was based on: a review of published and grey literature; a qualitative study of clinical network members; a program logic framework; and consultation with stakeholders. The resulting domain structure was validated for a sample of 592 clinical network members using confirmatory factor analysis. Scale reliability was assessed using Cronbach's alpha. A summary score was calculated for each domain and aggregate level means and ranges are reported. The instrument was shown to have good construct validity across seven domains as demonstrated by a high level of internal consistency, and all Cronbach's α coefficients were equal to or above 0.75. In the survey sample of network members there was strong reported commitment and belief in network-led quality improvement initiatives, which were perceived to have improved quality of care (72.8 %) and patient outcomes (63.2 %). Network managers were perceived to be effective leaders and clinical co-chairs were perceived as champions for change. Perceived external support had the lowest summary score across the seven domains. This survey, which has good construct validity and internal reliability, provides a valid instrument to use in future research related to

  11. Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

    Igaki, Hiroshi; Nakamura, Masahide

    This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

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

    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.

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

    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.

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

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

    2017-01-01

    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. (paper)

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

    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

  16. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S

    2010-12-01

    Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.

  17. Feature extraction with deep neural networks by a generalized discriminant analysis.

    Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

    2012-04-01

    We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

  18. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  19. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

    Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

    2018-02-01

    The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

  20. A neural network model of semantic memory linking feature-based object representation and words.

    Cuppini, C; Magosso, E; Ursino, M

    2009-06-01

    Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

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

    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.

  2. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  3. A dense microseismic monitoring network in Korea for uncovering relationship between seismic activity and neotectonic features

    Kang, T.; Lee, J. M.; Kim, W.; Jo, B. G.; Chung, T.; Choi, S.

    2012-12-01

    A few tens of surface traces indicating movements in Quaternary were found in the southeastern part of the Korean Peninsula. Following both the geological and engineering definitions, those features are classified into "active", in geology, or "capable", in engineering, faults. On the other hand, the present-day seismicity of the region over a couple of thousand years is indistinguishable on the whole with the rest of the Korean Peninsula. It is therefore of great interest whether the present seismic activity is related to the neotectonic features or not. Either of conclusions is not intuitive in terms of the present state of seismic monitoring network in the region. Thus much interest in monitoring seismicity to provide an improved observation resolution and to lower the event-detection threshold has increased with many observations of the Quaternary faults. We installed a remote, wireless seismograph network which is composed of 20 stations with an average spacing of 10 km. Each station is equipped with a three-component Trillium Compact seismometer and Taurus digitizer. Instrumentation and analysis advancements are now offering better tools for this monitoring. This network is scheduled to be in operation over about one and a half year. In spite of the relatively short observation period, we expect that the high density of the network enables us to monitor seismic events with much lower magnitude threshold compared to the preexisting seismic network in the region. Following the Gutenberg-Richter relationship, the number of events with low magnitude is logarithmically larger than that with high magnitude. Following this rule, we can expect that many of microseismic events may reveal behavior of their causative faults, if any. We report the results of observation which has been performed over a year up to now.

  4. CLINICAL FEATURES AND SIGNIFICANCE OF CYTOKINE IL-4 IN CHILDREN WITH DENGUE AT A TERTIARY CARE CENTRE

    Rakesh Manoharan

    2016-12-01

    Full Text Available BACKGROUND Dengue is a mosquito borne viral infection in tropical and subtropical regions caused by one of the four serotypes of dengue viruses (DENV1-DENV4. The consequences of DENV infection range from asymptomatic condition Dengue Fever (DF or severe forms such as Dengue Haemorrhagic Fever (DHF and Dengue Shock Syndrome (DSS. The host immune responses have been considered as the major factor responsible for dengue pathogenesis. Endothelial activation markers such as expression of adhesion molecules and receptors have been found to serve as biomarkers of severe dengue disease. In this study, the cytokine IL-4 is reviewed for its utility as potential biomarker of severe dengue disease. MATERIALS AND METHODS 120 children of paediatric age group from 1 month till 18 years of age with fever for more than 5 days with either dengue NS1 antigen or dengue IgM positive were included. 30 children who were admitted for noninfectious disease (e.g. surgery without fever, any systemic illness and preexisting illness (tuberculosis, asthma in SRMC and RI were taken as controls. Cases were classified as uncomplicated dengue (dengue without warning signs and complicated dengue (dengue with warning signs and severe dengue. Clinical features and IL-4 (ELISA kit were analysed and compared among the study population and statistical analysis done for the obtained data. RESULTS Analysis of clinical features among the study groups revealed children with complicated dengue had persistent vomiting (95%, abdominal pain (80%, decreased urine output (50%, bleeding manifestations (83.3%, hepatomegaly (70%, haemoconcentration with concurrent thrombocytopenia (93.3%, altered coagulation profile (28.3%, ICU stay (54.7%, leucocytosis (15%, leucopenia (66.6% and normal leucocytes (18.4%. Analysis of IL-4 levels revealed children with complicated dengue showed >6 fold elevation in IL-4 levels (p=0.003. Mean IL-4 levels in complicated dengue group was also statistically

  5. An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

    Chen Liu

    2018-05-01

    Full Text Available How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

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

    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.

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

    AlShahrani, Mona

    2015-01-01

    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.

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

    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.

  9. The significance of the Danube ecological corridor in the proceedings of implementing ecological networks in Serbia

    Filipović Dejan

    2015-01-01

    Full Text Available With the modern processes for exploiting land people have altered the original appearance of areas and created cultural environments. The remaining natural environments, whether protected or not, take up a relatively small portion of space and represent isolated islands which in itself can not be sufficient for the preservation of biodiversity or for the fulfillment of national, regional or international goals and commitments related to their preservation. In order to secure the preservation of biodiversity, the strengthening of integrity and the natural processes, such as animal migrations, succession of vegetation and evolution processes, the communication between natural habitats is imperative. Ecological corridors, as integral elements of ecological networks, ensure the preservation of vital ecological interactions by providing a connection between different habitats or areas. Depending on a range of factors, from the fulfillment of demands of different species to the connecting of regions, corridors of local, sub-regional, regional and international importance are identified. The Danube ecological corridor is one of the most significant corridors of international importance which encompasses a large number of habitats which are part of the natural watercourse of the corridor. There are numerous protected areas in the Danube coastal area on Serbia's territory which present themselves as central areas for forming the ecological network, such as: Gornje Podunavlje, Karađorđevo, Fruška Gora, Titelski Breg hill, Kovalski rit marsh, Dunavski loess bluffs, the Sava mouth, Labudovo okno, Deliblato sands, Đerdap and Mala Vrbica. The diverse and mosaic vegetation of the floodplain, as well as the consistency of the protected areas within the Danube corridor have a direct influence on the quality and functionality of this corridor. The goal of this paper is to show the significance of the Danube ecological corridor in the process of implementing

  10. A Cross-Layer Duty Cycle MAC Protocol Supporting a Pipeline Feature for Wireless Sensor Networks

    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.

  11. Crystal surface analysis using matrix textural features classified by a Probabilistic Neural Network

    Sawyer, C.R.; Quach, V.T.; Nason, D.; van den Berg, L.

    1991-01-01

    A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlappings subimage and features are extracted from each subimage based on statistical measures of the gray tone distribution, according to the method of Haralick [1]. Twenty parameters are derived from each subimage and presented to a Probabilistic Neural Network (PNN) [2] for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities. 6 refs., 4 figs

  12. Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

    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.

  13. Scale-invariant feature extraction of neural network and renormalization group flow

    Iso, Satoshi; Shiba, Shotaro; Yokoo, Sumito

    2018-05-01

    Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T =0 to T =6 generates a flow along which the temperature approaches the critical value Tc=2.2 7 . This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards Tc and how the RBM learns to extract features of spin configurations.

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

    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

  15. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

    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/.

  16. Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

    Suryanarayana, Gowri; Lago Garcia, J.; Geysen, Davy; Aleksiejuk, Piotr; Johansson, Christian

    2018-01-01

    Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear

  17. Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

    Calhoun, Vince D.; Allen, Elena

    2013-01-01

    There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

  18. Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

    Moon Young Kang; Byungho Park

    2018-01-01

    Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy) in corporate social media on customer...

  19. A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery

    Askari, Farid

    1999-01-01

    This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence...

  20. Evaluation of Techniques to Detect Significant Network Performance Problems using End-to-End Active Network Measurements

    Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark

    2006-01-25

    End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our

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

    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.

  2. Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

    Wang, Xun-Heng; Li, Lihua

    2015-01-01

    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

  3. Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

    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

  4. Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

    Xue Yang

    2018-01-01

    Full Text Available Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN, which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN, DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

  5. Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks

    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.

  6. Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

    Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

    2018-02-28

    The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

  7. Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

    Tigges, P; Kathmann, N; Engel, R R

    1997-07-01

    Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

  8. Three-Dimensional Precession Feature Extraction of Ballistic Targets Based on Narrowband Radar Network

    Zhao Shuang

    2017-02-01

    Full Text Available Micro-motion is a crucial feature used in ballistic target recognition. To address the problem that single-view observations cannot extract true micro-motion parameters, we propose a novel algorithm based on the narrowband radar network to extract three-dimensional precession features. First, we construct a precession model of the cone-shaped target, and as a precondition, we consider the invisible problem of scattering centers. We then analyze in detail the micro-Doppler modulation trait caused by the precession. Then, we match each scattering center in different perspectives based on the ratio of the top scattering center’s micro-Doppler frequency modulation coefficient and extract the 3D coning vector of the target by establishing associated multi-aspect equation systems. In addition, we estimate feature parameters by utilizing the correlation of the micro-Doppler frequency modulation coefficient of the three scattering centers combined with the frequency compensation method. We then calculate the coordinates of the conical point in each moment and reconstruct the 3D spatial portion. Finally, we provide simulation results to validate the proposed algorithm.

  9. Grain surface features and clay mineralogy of the quaternary sediments from Western Deccan Trap Region, India, and their palaeoclimatic significance

    Veena U. Joshi

    2011-06-01

    Full Text Available Quartz sand grains obtained from a deeply gullied topography along the banks of two tributaries of River Pravara in Maharashtra (India have been examined with a scanning electron microscope (SEM. Quartz grains have been selected after a heavy mineral separation and micro-photographs of each grain were taken at various angles and magnifications. The sediments reveal features resulting from mechanical grinding as well as from chemical alteration. Conchoidal fractures, cleavage planes, grooves, v-shaped indentations etc. are the mechanical features documented on the grains whereas solution pits of varying sizes and intensity, precipitation surfaces, oriented v-pits, solution crevasses and etching are the features of chemical origin. Several evidences indicate that the samples have undergone digenetic changes. Few grains show the features of intense chemical breakdown. The overall assemblages of the grain surface features suggest that the samples have been subjected to subaqueous transport for a considerable period of time. The minor chemical features such as solution pits or semi circular arcuate steps found in abundance on these grains are due to the dissolution of the sediments in a low energy fluviatile environment. For clay mineralogy, fractions between <2 and <0.2 mm were separated out from the sediments. The clay fractions were then subjected to examination by X-ray diffraction (XRD of oriented K/Ca saturated samples using a Philips Diffractometer and Ni-filtered Cu Ka radiation with the scanning speed of 10 2Ө min -1. The main clay minerals for all the samples are identical and show the presence of hydroxy-interlayered smectites with minor quantities of mica, kaolinite, smectites, quartz and feldspar. The first weathering product of the Deccan Basalt (DB is the dioctahedral smectite. Since the present semi aridic climatic condition of the study area can not transform a smectite to HIS and either smectite to kaolin, it is quite likely that

  10. Significant performance improvement obtained in a wireless mesh network using a beamswitching antenna

    Lysko, AA

    2012-09-01

    Full Text Available mesh network operated in a fixed 11 Mbps mode. The throughput improvement in multi-hop communication obtained in the presence of an interferer is tenfold, from 0.2 Mbps to 2 Mbps. Index Terms?antenna, smart antenna, wireless mesh network, WMN... efficiency in the communications, and active research and development of new methods and technologies enabling this at the physical layer, including multiple antenna techniques, such as multiple input multiple output (MIMO) and smart antennas...

  11. Acute liver allograft antibody-mediated rejection:an inter-institutional study of significant histopathological features

    O'Leary, Jacqueline G; Shiller, S Michelle; Bellamy, Christopher; Nalesnik, Michael A; Kaneku, Hugo; Jennings, Linda W; Isse, Kumiko; Terasaki, Paul I; Klintmalm, Göran B; Demetris, Anthony J

    2014-01-01

    Acute antibody-mediated rejection (AMR) occurs in a small minority of sensitized liver transplant recipients. Although histopathologic characteristics have been described, specific features that could be used: a) for a generalizable scoring system; and b) to trigger a more in-depth analysis are needed to screen for this rare but important finding. Toward this goal, we created a training and validation cohort from 3 high volume liver transplant programs of putative acute AMR and control cases ...

  12. Distribution of late gadolinium enhancement in various types of cardiomyopathies: Significance in differential diagnosis, clinical features and prognosis

    Satoh, Hiroshi; Sano, Makoto; Suwa, Kenichiro; Saitoh, Takeji; Nobuhara, Mamoru; Saotome, Masao; Urushida, Tsuyoshi; Katoh, Hideki; Hayashi, Hideharu

    2014-01-01

    The recent development of cardiac magnetic resonance (CMR) techniques has allowed detailed analyses of cardiac function and tissue characterization with high spatial resolution. We review characteristic CMR features in ischemic and non-ischemic cardiomyopathies (ICM and NICM), especially in terms of the location and distribution of late gadolinium enhancement (LGE). CMR in ICM shows segmental wall motion abnormalities or wall thinning in a particular coronary arterial territory, and the suben...

  13. Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

    Scott, RE; Ghule, PN; Stein, JL; Stein, GS

    2015-01-01

    The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with p = e−13 to e−36. Cell cycle expression networks show species, sex and tissue variability and they are enriched in mRNA transcripts associated with mitosis many of which are associated with chromosomal instability. PMID:25808367

  14. BP network for atorvastatin effect evaluation from ultrasound images features classification

    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.

  15. Development of Sorting System for Fishes by Feed-forward Neural Networks Using Rotation Invariant Features

    Shiraishi, Yuhki; Takeda, Fumiaki

    In this research, we have developed a sorting system for fishes, which is comprised of a conveyance part, a capturing image part, and a sorting part. In the conveyance part, we have developed an independent conveyance system in order to separate one fish from an intertwined group of fishes. After the image of the separated fish is captured in the capturing part, a rotation invariant feature is extracted using two-dimensional fast Fourier transform, which is the mean value of the power spectrum with the same distance from the origin in the spectrum field. After that, the fishes are classified by three-layered feed-forward neural networks. The experimental results show that the developed system classifies three kinds of fishes captured in various angles with the classification ratio of 98.95% for 1044 captured images of five fishes. The other experimental results show the classification ratio of 90.7% for 300 fishes by 10-fold cross validation method.

  16. Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

    Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

    2018-04-01

    We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

  17. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    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

  18. Study on Reverse Reconstruction Method of Vehicle Group Situation in Urban Road Network Based on Driver-Vehicle Feature Evolution

    Xiaoyuan Wang

    2017-01-01

    Full Text Available Vehicle group situation is the status and situation of dynamic permutation which is composed of target vehicle and neighboring traffic entities. It is a concept which is frequently involved in the research of traffic flow theory, especially the active vehicle security. Studying vehicle group situation in depth is of great significance for traffic safety. Three-lane condition was taken as an example; the characteristics of target vehicle and its neighboring vehicles were synthetically considered to restructure the vehicle group situation in this paper. The Gamma distribution theory was used to identify the vehicle group situation when target vehicle arrived at the end of the study area. From the perspective of driver-vehicle feature evolution, the reverse reconstruction method of vehicle group situation in the urban road network was proposed. Results of actual driving, virtual driving, and simulation experiments showed that the model established in this paper was reasonable and feasible.

  19. Distribution of late gadolinium enhancement in various types of cardiomyopathies: Significance in differential diagnosis, clinical features and prognosis.

    Satoh, Hiroshi; Sano, Makoto; Suwa, Kenichiro; Saitoh, Takeji; Nobuhara, Mamoru; Saotome, Masao; Urushida, Tsuyoshi; Katoh, Hideki; Hayashi, Hideharu

    2014-07-26

    The recent development of cardiac magnetic resonance (CMR) techniques has allowed detailed analyses of cardiac function and tissue characterization with high spatial resolution. We review characteristic CMR features in ischemic and non-ischemic cardiomyopathies (ICM and NICM), especially in terms of the location and distribution of late gadolinium enhancement (LGE). CMR in ICM shows segmental wall motion abnormalities or wall thinning in a particular coronary arterial territory, and the subendocardial or transmural LGE. LGE in NICM generally does not correspond to any particular coronary artery distribution and is located mostly in the mid-wall to subepicardial layer. The analysis of LGE distribution is valuable to differentiate NICM with diffusely impaired systolic function, including dilated cardiomyopathy, end-stage hypertrophic cardiomyopathy (HCM), cardiac sarcoidosis, and myocarditis, and those with diffuse left ventricular (LV) hypertrophy including HCM, cardiac amyloidosis and Anderson-Fabry disease. A transient low signal intensity LGE in regions of severe LV dysfunction is a particular feature of stress cardiomyopathy. In arrhythmogenic right ventricular cardiomyopathy/dysplasia, an enhancement of right ventricular (RV) wall with functional and morphological changes of RV becomes apparent. Finally, the analyses of LGE distribution have potentials to predict cardiac outcomes and response to treatments.

  20. Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

    Moon Young Kang

    2018-04-01

    Full Text Available Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy in corporate social media on customers’ purchase intentions, mediated by brand attitude and corporate trust. Especially, social media platforms are believed to provide a good marketing platform for small and medium enterprises (SMEs by providing access to huge audiences at a very low cost. The findings from this study based on a structural equation model suggest that brand attitude and corporate trust have larger impacts on purchase intention for SMEs than large firms. This implies that SMEs with little to no presence in the market should pay more attention to building corporate trust and brand attitude for their sustainable growth.

  1. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

    Tao Ye

    2018-06-01

    Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  2. Classification of protein-protein interaction full-text documents using text and citation network features.

    Kolchinsky, Artemy; Abi-Haidar, Alaa; Kaur, Jasleen; Hamed, Ahmed Abdeen; Rocha, Luis M

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

  3. Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments

    Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke

    2017-01-01

    Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features

  4. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

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

    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

  6. Light and electron microscopy of the European beaver (Castor fiber) stomach reveal unique morphological features with possible general biological significance.

    Ziółkowska, Natalia; Lewczuk, Bogdan; Petryński, Wojciech; Palkowska, Katarzyna; Prusik, Magdalena; Targońska, Krystyna; Giżejewski, Zygmunt; Przybylska-Gornowicz, Barbara

    2014-01-01

    Anatomical, histological, and ultrastructural studies of the European beaver stomach revealed several unique morphological features. The prominent attribute of its gross morphology was the cardiogastric gland (CGG), located near the oesophageal entrance. Light microscopy showed that the CGG was formed by invaginations of the mucosa into the submucosa, which contained densely packed proper gastric glands comprised primarily of parietal and chief cells. Mucous neck cells represented beaver stomach was the presence of specific mucus with a thickness up to 950 µm (in frozen, unfixed sections) that coated the mucosa. Our observations suggest that the formation of this mucus is complex and includes the secretory granule accumulation in the cytoplasm of pit cells, the granule aggregation inside cells, and the incorporation of degenerating cells into the mucus.

  7. A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

    Yongwei Zhang

    2017-01-01

    Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.

  8. Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

    Hong Cheng

    2017-01-01

    Full Text Available (1 Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN; (2 Methods: Two back propagation neural network (BPNN models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image, pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3 Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2 between the estimated and the actual weighted yield, mean forecast error (MFE, mean absolute percentage error (MAPE, and root mean square error (RMSE were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit and improved near harvest (red, large fruit to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv.

  9. Prognostic Significance of Clinicopathologic Features in Patients With Breast Ductal Carcinoma-in-Situ Who Received Breast-Conserving Surgery.

    Kuo, Sung-Hsin; Lo, Chiao; Chen, Yu-Hsuan; Lien, Huang-Chun; Kuo, Wen-Hung; Wang, Ming-Yang; Lee, Yi-Hsuan; Huang, Chiun-Sheng

    2018-04-10

    To identify whether a certain group of breast ductal carcinoma-in-situ (DCIS) patients can be treated with breast-conserving surgery (BCS) alone; to analyze the clinicopathologic features of DCIS and tamoxifen administration in patients treated with BCS who developed ipsilateral breast tumor recurrence (IBTR). Data for 375 women with breast DCIS who underwent BCS at our institute between June 2003 and October 2010 were analyzed. The patients were divided into different categories according to the recurrence risk predicted using the California/Van Nuys Prognostic Index (USC/VNPI) score (4-6, 7-9, and 10-12), Eastern Cooperative Oncology Group (ECOG) E5194 criteria, or combined risk features with USC/VNPI score and ECOG E5194 criteria. The IBTR and disease-free survival (DFS) rates were calculated by the Kaplan-Meier method. The prognostic effects of age, tumor size, tumor grade, margin width, estrogen receptor status, USC/VNPI score, low-risk characteristics, and tamoxifen use were evaluated by log-rank tests. Of the patients, 168 were treated with breast irradiation after BCS and 207 were not. The patients who were treated with radiotherapy (RT) tended to be younger (USC/VNPI scores (7-9), and to meet the ECOG E5194 non-cohort 1 criteria. The 7-year risk of IBTR was 6.2% (n = 11) in the patients who received irradiation and 9.0% (n = 22) in those who did not. DFS rates were better in the patients who underwent RT than in those who did not (93.3% vs. 88.5%, P = .056). Among the patients who underwent BCS alone, age ≥ 40 years, margin width > 10 mm, USC/VNPI scores 4-6, ECOG E5194 cohort 1 criteria, estrogen receptor-positive status, and tamoxifen use predicted lower IBTR and better DFS rates. In the multivariate analysis, combined low-risk characteristics (USC/VNPI scores 4-6 and meeting the ECOG E5194 cohort 1 criteria) were identified as an independent prognostic factor of lower IBTR (P = .028) and better DFS (P = .005). RT reduces the risk of IBTR after

  10. Forests in the biogeographical corridors connecting the Fennoscandian shield and the Russian plain: natural features, contemporary status, environmental significance

    A. N. Gromtsev

    2016-12-01

    Full Text Available The results of long-term research on forests in natural biogeographical corridors (territories with forests, mires, inland lakes and other land categories connecting the largest bodies of water in Northern Europe (Baltic Sea-Gulf of Finland and lakes Ladoga and Onego to the White Sea are reported. These corridors link isolated pieces of the Eurasian taiga biome at the boundary between two of Europe’s physiographic divisions – Fennoscandian Shield and Russian Plain. They facilitate the dispersal and migration of plant and animal species. The straight-line terrestrial stretch between the Gulf of Finland and the White Sea is around 320 km, and it falls into three sections in the southern, middle and northern taiga subzones, respectively. The corridors were characterized and assessed as follows: 1 physiographic (landscape features; 2 key natural characteristics (typological structure, quantitative ratios, spatial arrangement, productivity, etc., present-day condition of forests, including data from forest management inventories of the past decade; 3 overall assessment of the forest cover transformation by human impact; 4 current system of protected areas and protective forests, and its capacity to fulfill the functions of the corridors (sufficiency.

  11. Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

    Hyoung‐Gook Kim

    2017-12-01

    Full Text Available Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

  12. Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology

    Ukil, Sanchaita; Sinha, Meenakshee; Varshney, Lavneesh; Agrawal, Shipra

    Type 2 Diabetes is a complex multifactorial disease, which alters several signaling cascades giving rise to serious complications. It is one of the major risk factors for cardiovascular diseases. The present research work describes an integrated functional network biology approach to identify pathways that get transcriptionally altered and lead to complex complications thereby amplifying the phenotypic effect of the impaired disease state. We have identified two sub-network modules, which could be activated under abnormal circumstances in diabetes. Present work describes key proteins such as P85A and SRC serving as important nodes to mediate alternate signaling routes during diseased condition. P85A has been shown to be an important link between stress responsive MAPK and CVD markers involved in fibrosis. MAPK8 has been shown to interact with P85A and further activate CTGF through VEGF signaling. We have traced a novel and unique route correlating inflammation and fibrosis by considering P85A as a key mediator of signals. The next sub-network module shows SRC as a junction for various signaling processes, which results in interaction between NF-kB and beta catenin to cause cell death. The powerful interaction between these important genes in response to transcriptionally altered lipid metabolism and impaired inflammatory response via SRC causes apoptosis of cells. The crosstalk between inflammation, lipid homeostasis and stress, and their serious effects downstream have been explained in the present analyses.

  13. Significant breakthroughs in monitoring networks of the volcanological and seismological French observatories

    lemarchand, A.; Francois, B.; Bouin, M.; Brenguier, F.; Clouard, V.; Di Muro, A.; Ferrazzini, V.; Shapiro, N.; Staudacher, T.; Kowalski, P.; Agrinier, P.

    2013-12-01

    Others authors: S. Tait (1), D. Amorese (4,1), JB de Chabalier (1), A. Anglade (4,1), P. Kowalski (5,1),the teams in the IPGP Volcanological and Seismological observatories In the last few years, French West Indies observatories, in collaboration with the Seismic Research Center (University of West Indies-Trinidad), have modernized the Lesser Antilles Arc seismic and deformation monitoring network. 16 new permanent stations have been installed to strengthen and expand its detection capabilities. The global network of the IPGP-SRC consortium is now composed of 21 modernized stations, all equipped with broadband seismometers, strong motion sensors, GNSS sensors and satellite communication for real-time data transfer to the observatories of Trinidad (SRC), Guadeloupe (OVSG), Martinique (OVSM). To improve the sensitivity and reduce ambient noise, special efforts were made to enhance the design of the seismic vault and the original Stuttgart shielding (D. Kurrle R. Widmer-Schnidrig, 2005) of the broadband seismometers (240 and 120 sec). This renewed network feeds the Caribbean Tsunami Warning System supported by UNESCO and establishes a monitoring tool that produces high quality data for studying subduction and volcanism interactions in the Lesser Antilles arc. Since 2010, the UnderVolc research program has been an opportunity to reinforce the existing volcanic seismic network of Piton de la Fournaise on La Réunion Island (Indian Ocean). 20 broadband seismometers, 20 short-period sensors, and 26 GNSS receivers now cover the volcano. The program successfully developed many new data treatment tools. They have proven to be well-adapted for monitoring volcanic activity such as the tracking of seismic velocity changes inferred from seismic noise, or the injection of dike and the resulting deformations. This upgrade has now established the monitoring network of La Réunion hot spot to high quality standards which will foster the scientific attractiveness of OVPF-IPGP. During

  14. Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

    Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

    2018-03-07

    Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

  15. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  16. Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast

    Szabo, Botond K.; Wiberg, Maria Kristoffersen; Bone, Beata; Aspelin, Peter

    2004-01-01

    The discriminative ability of established diagnostic criteria for MRI of the breast is assessed, and their relative relevance using artificial neural networks (ANNs) is determined. A total of 89 women with 105 histopathologically verified breast lesions (73 invasive cancers, 2 in situ cancers, and 30 benign lesions) were included in this study. A T1-weighted 3D FLASH sequence was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.2 mmol/kg body weight. ANN models were built to test the discriminative ability of kinetic, morphologic, and combined MR features. The subjects were randomly divided into two parts: a training set of 59 lesions and a verification set of 46 lesions. The training set was used for learning, and the performance of each model was evaluated on the verification set by measuring the area under the ROC curve (A z ). An optimally minimized model was constructed using the most relevant input variables that were determined by the automatic relevance determination (ARD) method. ANN models were compared with the performance of a human reader. Margin type, time-to-peak enhancement, and washout ratio showed the highest discriminative ability among diagnostic criteria and comprised the minimized model. Compared with the expert radiologist (A z =0.799), using the same prediction scale, the minimized ANN model performed best (A z =0.771), followed by the best kinetic (A z =0.743), the maximized (A z =0.727), and the morphologic model (A z =0.678). The performance of a neural network prediction model is comparable to that of an expert radiologist. A neurostatistical approach is preferred for the analysis of diagnostic criteria when many parameters are involved and complex nonlinear relationships exist in the data set. (orig.)

  17. Implementation of a FPGA-Based Feature Detection and Networking System for Real-time Traffic Monitoring

    Chen, Jieshi; Schafer, Benjamin Carrion; Ho, Ivan Wang-Hei

    2016-01-01

    With the growing demand of real-time traffic monitoring nowadays, software-based image processing can hardly meet the real-time data processing requirement due to the serial data processing nature. In this paper, the implementation of a hardware-based feature detection and networking system prototype for real-time traffic monitoring as well as data transmission is presented. The hardware architecture of the proposed system is mainly composed of three parts: data collection, feature detection,...

  18. The construction of digital 3D arterial vascular network of uterine leiomyomas and its clinical significance

    Chen Chunlin; Xu Yujing; Liu Ping

    2012-01-01

    Objective: To discuss the method of constructing digital 3D arterial vascular network of uterine leiomyomas based on the CTA data, by which to lay the fundamental work for the observation of the origin and distribution of hysteromyoma blood supply. Methods: A total of 64 cases of uterine leiomyomas were enrolled in this study. Dual-source CT angiography was performed in all the patients, and the CTA original images were obtained. By using Mimics 10.01 software the digital 3D arterial vascular network of uterine was reconstructed. The reconstructed models were analyzed. Results: (1) The constructing process of arterial vascular network was successfully accomplished in all 64 patients. The pelvic main arteries, the uterine arteries and tumor-feeding arteries as well as the blood distribution type were clearly demonstrated on the reconstructed images. (2) The origins of hysteromyoma blood supply included uterine artery (81.25%), uterine artery and unilateral ovarian artery (10.94%), uterine artery and bilateral ovarian artery (4.69%) and ovarian artery (3.12%). (3) Distribution pattern of blood supply of uterine leiomyomas could be divided into 4 types: (1) Type Ⅰ. The unilateral arterial blood supply dominant type (unilateral uterine artery with or without ipsilateral ovarian arterial, providing more than 1/2 blood supply of hysteromyoma), which accounted for 35.94% of all patients (23/26); (2) Type Ⅱ. The bilateral arterial blood supply balanced type (bilateral uterine artery with or without ipsilateral ovarian artery, providing about 1/2 blood supply of hysteromyoma), which accounted for 53.13% of all patients (34/64); (3) Type Ⅲ. The unilateral uterine artery was the main blood supply of uterine leiomyomas, which accounted for 7.81% of all patients (5/64); (4) Type Ⅳ. The ovarian artery was the main blood supply of uterine leiomyomas, which accounted for 3.13% of all patients (3/64). Conclusion: Based on CTA data and with the help of reconstruction

  19. FEATURES OF THE CLINICAL SIGNIFICANCE OF POLYMORPHIC VARIANTS OF ENOS AND AGTR2 GENES IN PATIENTS WITH CAD

    A. L. Khokhlov

    2016-01-01

    Full Text Available Coronary heart disease (CHD is a major cause of mortality. Morphological substrate of CHD in most cases is atherosclerosis, which is based on structural genes polymorphism eNOS and AGTR2. The aim of the study was to study the prevalence of eNOS and AGTR2 genes in patients with coronary artery disease and the association of these genes with coronary heart disease. The study involved 187 patients aged 36 to 86 years (62,2±11,2 with different forms of CHD: stable and unstable angina, myocardial infarction and 45 people without CHD. Determination of gene polymorphisms was performed by real-time PCR analyzer of nucleic acids IQ 5 Bio-Rad. Statistical analysis was performed using Statistica 10.0. The study revealed a significant difference between the incidence of homozygous AA allelic variant gene AGTR2 group of patients with myocardial infarction and the comparison group; polymorphic variant AA AGTR2 gene is associated with earlier onset of coronary artery disease; It found that carriers of the polymorphic variant gene GA AGTR2 beginning statistically CHD occurred significantly later than in carriers of alleles GG and AA; age CHD debut TT allele carriers of the eNOS gene is associated with an earlier onset of the disease and statistically significantly different from the age of first CHD in carriers of alleles of polymorphic variants of GG and GT; revealed a positive correlation between the polymorphic allele AGTR2 gene with the presence of arterial hypertension in patients with coronary artery disease; It determined that the T allele carriers of the polymorphic gene eNOS is associated more early onset of hypertension, found the association of the polymorphic allele gene AGTR2 the need to use higher doses of ACE inhibitor — perindopril.

  20. EDDY - a FORTRAN program to extract significant features from eddy-current test data - the basis of the CANSCAN system

    Jarvis, R.G.; Cranston, R.J.

    1982-09-01

    The FORTRAN program EDDY is designed to analyse data: from eddy-current scans of steam generator tubes. It is written in modular form, for future development, and it uses signal-recognition techniques that the authors developed in the profilometry of irradiated fuel elements. During a scan, significant signals are detected and extracted for immediate attention or more detailed analysis later. A version of the program was used in the CANSCAN system 'for automated eddy-current in-service inspection of nuclear steam generator tubing'

  1. The diagnosis and management of pre-invasive breast disease: Flat epithelial atypia – classification, pathologic features and clinical significance

    Schnitt, Stuart J

    2003-01-01

    Flat epithelial atypia is a descriptive term that encompasses lesions of the breast terminal duct lobular units in which variably dilated acini are lined by one to several layers of epithelial cells, which are usually columnar in shape and which display low-grade cytologic atypia. Observational studies have suggested that at least some of these lesions may represent either a precursor of ductal carcinoma in situ (DCIS) or the earliest morphological manifestation of DCIS. In contrast, the limited available clinical follow-up data suggest that the risk of both local recurrence and progression of these lesions to invasive cancer is extremely low, supporting the notion that categorizing such lesions as 'clinging carcinoma' and managing them as if they were fully developed DCIS will result in overtreatment of many patients. Additional studies are needed to better understand the biological nature and clinical significance of these lesions

  2. Fractured reservoir discrete feature network technologies. Annual report, March 7, 1996--February 28, 1997

    Dershowitz, W.S.; La Pointe, P.R.; Einstein, H.H.; Ivanova, V.

    1998-01-01

    This report describes progress on the project, {open_quotes}Fractured Reservoir Discrete Feature Network Technologies{close_quotes} during the period March 7, 1996 to February 28, 1997. The report presents summaries of technology development for the following research areas: (1) development of hierarchical fracture models, (2) fractured reservoir compartmentalization and tributary volume, (3) fractured reservoir data analysis, and (4) integration of fractured reservoir data and production technologies. In addition, the report provides information on project status, publications submitted, data collection activities, and technology transfer through the world wide web (WWW). Research on hierarchical fracture models included geological, mathematical, and computer code development. The project built a foundation of quantitative, geological and geometrical information about the regional geology of the Permian Basin, including detailed information on the lithology, stratigraphy, and fracturing of Permian rocks in the project study area (Tracts 17 and 49 in the Yates field). Based on the accumulated knowledge of regional and local geology, project team members started the interpretation of fracture genesis mechanisms and the conceptual modeling of the fracture system in the study area. Research on fractured reservoir compartmentalization included basic research, technology development, and application of compartmentalized reservoir analyses for the project study site. Procedures were developed to analyze compartmentalization, tributary drainage volume, and reservoir matrix block size. These algorithms were implemented as a Windows 95 compartmentalization code, FraCluster.

  3. Fast hybrid fractal image compression using an image feature and neural network

    Zhou Yiming; Zhang Chao; Zhang Zengke

    2008-01-01

    Since fractal image compression could maintain high-resolution reconstructed images at very high compression ratio, it has great potential to improve the efficiency of image storage and image transmission. On the other hand, fractal image encoding is time consuming for the best matching search between range blocks and domain blocks, which limits the algorithm to practical application greatly. In order to solve this problem, two strategies are adopted to improve the fractal image encoding algorithm in this paper. Firstly, based on the definition of an image feature, a necessary condition of the best matching search and FFC algorithm are proposed, and it could reduce the search space observably and exclude most inappropriate domain blocks according to each range block before the best matching search. Secondly, on the basis of FFC algorithm, in order to reduce the mapping error during the best matching search, a special neural network is constructed to modify the mapping scheme for the subblocks, in which the pixel values fluctuate greatly (FNFC algorithm). Experimental results show that the proposed algorithms could obtain good quality of the reconstructed images and need much less time than the baseline encoding algorithm

  4. Significance of clinical and biologic features in Stage 3 neuroblastoma: a report from the International Neuroblastoma Risk Group project.

    Meany, Holly J; London, Wendy B; Ambros, Peter F; Matthay, Katherine K; Monclair, Tom; Simon, Thorsten; Garaventa, Alberto; Berthold, Frank; Nakagawara, Akira; Cohn, Susan L; Pearson, Andrew D J; Park, Julie R

    2014-11-01

    International Neuroblastoma Staging System (INSS) Stage 3 neuroblastoma is a heterogeneous disease. Data from the International Neuroblastoma Risk Group (INRG) database were analyzed to define patient and tumor characteristics predictive of outcome. Of 8,800 patients in the INRG database, 1,483 with INSS Stage 3 neuroblastoma and complete follow-up data were analyzed. Secondary analysis was performed in 1,013 patients (68%) with MYCN-non-amplified (NA) tumors. Significant prognostic factors were identified via log-rank test comparisons of survival curves. Multivariable Cox proportional hazards regression model was used to identify factors independently predictive of event-free survival (EFS). Age at diagnosis (P INSS Stage 3 neuroblastoma patients, age at diagnosis, MYCN status and histology predict outcome. Patients <547 days of age with MYCN-NA tumors that lack chromosome 11q aberrations or those with serum ferritin <96 ng/ml have excellent prognosis and should be considered for therapy reduction. Prospective clinical trials are needed to identify optimal therapy for those patients ≥ 547 days of age with undifferentiated histology or elevated serum ferritin. © 2014 Wiley Periodicals, Inc.

  5. Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

    Wang Xiaojia; Mao Qirong; Zhan Yongzhao

    2008-01-01

    There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction

  6. Networking for ovarian rare tumors: a significant breakthrough improving disease management.

    Chiannilkulchai, N; Pautier, P; Genestie, C; Bats, A S; Vacher-Lavenu, M C; Devouassoux-Shisheboran, M; Treilleux, I; Floquet, A; Croce, S; Ferron, G; Mery, E; Pomel, C; Penault-Llorca, F; Lefeuvre-Plesse, C; Henno, S; Leblanc, E; Lemaire, A S; Averous, G; Kurtz, J E; Ray-Coquard, I

    2017-06-01

    Rare ovarian tumors represent >20% of all ovarian cancers. Given the rarity of these tumors, natural history, prognostic factors are not clearly identified. The extreme variability of patients (age, histological subtypes, stage) induces multiple and complex therapeutic strategies. Since 2011, a national network with a dedicated system for referral, up to 22 regional and three national reference centers (RC) has been supported by the French National Cancer Institute (INCa). The network aims to prospectively monitor the management of rare ovarian tumors and provide an equal access to medical expertise and innovative treatments to all French patients through a dedicated website, www.ovaire-rare.org. Over a 5-year activity, 4612 patients have been included. Patients' inclusions increased from 553 in 2011 to 1202 in 2015. Expert pathology review and patients' files discussion in dedicated multidisciplinary tumor boards increased from 166 cases in 2011 (25%) to 538 (45%) in 2015. Pathology review consistently modified the medical strategy in 5-9% every year. The rate of patients' files discussed in RC similarly increased from 294 (53%) to 789 (66%). An increasing number (357 in 5 years) of gynecologic (non-ovarian) rare tumors were also registered by physicians seeking for pathological or medical advice from expert tumor boards. Such a nation-wide organization for rare gynecological tumors has invaluable benefits, not only for patients, but also for epidemiological, clinical and biological research. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

    Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

    2017-02-03

    The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

    Schetinin, Vitaly; Jakaite, Livija; Nyah, Ndifreke; Novakovic, Dusica; Krzanowski, Wojtek

    2018-08-01

    The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

  9. Significant Enhancement of the Chiral Correlation Length in Nematic Liquid Crystals by Gold Nanoparticle Surfaces Featuring Axially Chiral Binaphthyl Ligands.

    Mori, Taizo; Sharma, Anshul; Hegmann, Torsten

    2016-01-26

    surface is diminished as the size of the particle is reduced. However, in comparison to the free ligands, per chiral molecule all tested gold nanoparticles induce helical distortions in a 10- to 50-fold larger number of liquid crystal host molecules surrounding each particle, indicating a significantly enhanced chiral correlation length. We propose that both the helicity and the chirality transfer efficiency of axially chiral binaphthyl derivatives can be controlled at metal nanoparticle surfaces by adjusting the particle size and curvature as well as the number and density of the chiral ligands to ultimately measure and tune the chiral correlation length.

  10. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation

    YU Wenhao

    2015-01-01

    Full Text Available The distribution pattern and the distribution density of urban facility POIs are of great significance in the fields of infrastructure planning and urban spatial analysis. The kernel density estimation, which has been usually utilized for expressing these spatial characteristics, is superior to other density estimation methods (such as Quadrat analysis, Voronoi-based method, for that the Kernel density estimation considers the regional impact based on the first law of geography. However, the traditional kernel density estimation is mainly based on the Euclidean space, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. Hence, this research proposed a computational model of network kernel density estimation, and the extension type of model in the case of adding constraints. This work also discussed the impacts of distance attenuation threshold and height extreme to the representation of kernel density. The large-scale actual data experiment for analyzing the different POIs' distribution patterns (random type, sparse type, regional-intensive type, linear-intensive type discusses the POI infrastructure in the city on the spatial distribution of characteristics, influence factors, and service functions.

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

    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…

  12. Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

    Lee, Jongpil; Nam, Juhan

    2017-08-01

    Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

  13. Features of Random Metal Nanowire Networks with Application in Transparent Conducting Electrodes

    Maloth, Thirupathi

    2017-01-01

    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

  14. Significance of bacteria associated with invertebrates in drinking water distribution networks.

    Wolmarans, E; du Preez, H H; de Wet, C M E; Venter, S N

    2005-01-01

    The implication of invertebrates found in drinking water distribution networks to public health is of concern to water utilities. Previous studies have shown that the bacteria associated with the invertebrates could be potentially pathogenic to humans. This study investigated the level and identity of bacteria commonly associated with invertebrates collected from the drinking water treatment systems as well as from the main pipelines leaving the treatment works. On all sampling occasions bacteria were isolated from the invertebrate samples collected. The highest bacterial counts were observed for the samples taken before filtration as was expected. There were, however, indications that optimal removal of invertebrates from water did not always occur. During the investigation, 116 colonies were sampled for further identification. The isolates represent several bacterial genera and species that are pathogenic or opportunistic pathogens of humans. Diarrhoea, meningitis, septicaemia and skin infections are among the diseases associated with these organisms. The estimated number of bacteria that could be associated with a single invertebrate (as based on average invertebrate numbers) could range from 10 to 4000 bacteria per organism. It can, therefore, be concluded that bacteria associated with invertebrates might under the worst case scenario pose a potential health risk to water users. In the light of the above findings it is clear that invertebrates in drinking water should be controlled at levels as low as technically and economically feasible.

  15. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  16. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

    Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

    2009-12-21

    Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct

  17. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

    Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

    2016-10-18

    Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

  18. The mediational significance of negative/depressive affect in the relationship of childhood maltreatment and eating disorder features in adolescent psychiatric inpatients.

    Hopwood, C J; Ansell, E B; Fehon, D C; Grilo, C M

    2011-03-01

    Childhood maltreatment is a risk factor for eating disorder and negative/depressive affect appears to mediate this relation. However, the specific elements of eating- and body-related psychopathology that are influenced by various forms of childhood maltreatment remain unclear, and investigations among adolescents and men/boys have been limited. This study investigated the mediating role of negative affect/depression across multiple types of childhood maltreatment and eating disorder features in hospitalized adolescent boys and girls. Participants were 148 adolescent psychiatric inpatients who completed an assessment battery including measures of specific forms of childhood maltreatment (sexual, emotional, and physical abuse), negative/depressive affect, and eating disorder features (dietary restriction, binge eating, and body dissatisfaction). Findings suggest that for girls, negative/depressive affect significantly mediates the relationships between childhood maltreatment and eating disorder psychopathology, although effects varied somewhat across types of maltreatment and eating disorder features. Generalization of mediation effects to boys was limited.

  19. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

    Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

    2018-05-01

    Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

  20. AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

    Maqlin Paramanandam

    2016-11-01

    Full Text Available The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN. The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges

  1. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks

    National Aeronautics and Space Administration — In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used...

  2. NeuroCharter: A Neural Networks Software to Visually Discover the Effects and Contributions between Interrelated Features

    Mohammad N. Elnesr

    2017-09-01

    Full Text Available NeuroCharter is an open-source software that helps in prediction problems in scientific research through artificial neural networks. The program is designed mainly for researchers who focus on details of the neural-network’s parameters, in addition to easy reuse of the trained network. The program outputs almost all the necessary graphs regarding the network and features contributions and relative outputs for both numeric and categorical features. The program was implemented in Python 2.7.11 and is open sourced for reuse and future development. The program consists of four main classes, one for the neural networks calculation, one for data manipulation, one for plotting the neural network, and the main class that manages and links the other classes. The source code and some experimental data are freely available at the GitHub code repository http://j.mp/NeuroCharter.   Funding Statement: The project was financially supported by King Saud University, Vice Deanship of Research Chairs.

  3. Feature-based comparison and selection of software defined networking (SDN) controllers

    Khondoker, Rahamatullah; Zaalouk, Adel; Marx, Ronald; Bayarou, Kpatcha

    2014-01-01

    Software Defined Networking (SDN) is seen as one way to solve some problems of the Internet including security, managing complexity, multi-casting, load balancing, and energy efficiency. SDN is an architectural paradigm that separates the control plane of a networking device (e.g., a switch / router) from its data plane, making it feasible to control, monitor, and manage a network from a centralized node (the SDN controller). However, today there exists many SDN controllers including POX, Flo...

  4. Expression profile and specific network features of the apoptotic machinery explain relapse of acute myeloid leukemia after chemotherapy

    Ragusa, Marco; Consoli, Carla; Camuglia, Maria Grazia; Di Pietro, Cinzia; Milone, Giuseppe; Purrello, Michele; Avola, Giuseppe; Angelica, Rosario; Barbagallo, Davide; Guglielmino, Maria Rosa; Duro, Laura R; Majorana, Alessandra; Statello, Luisa; Salito, Loredana

    2010-01-01

    According to the different sensitivity of their bone marrow CD34+ cells to in vitro treatment with Etoposide or Mafosfamide, Acute Myeloid Leukaemia (AML) patients in apparent complete remission (CR) after chemotherapy induction may be classified into three groups: (i) normally responsive; (ii) chemoresistant; (iii) highly chemosensitive. This inversely correlates with in vivo CD34+ mobilization and, interestingly, also with the prognosis of the disease: patients showing a good mobilizing activity are resistant to chemotherapy and subject to significantly higher rates of Minimal Residual Disease (MRD) and relapse than the others. Based on its known role in patients' response to chemotherapy, we hypothesized an involvement of the Apoptotic Machinery (AM) in these phenotypic features. To investigate the molecular bases of the differential chemosensitivity of bone marrow hematopoietic stem cells (HSC) in CR AML patients, and the relationship between chemosensitivity, mobilizing activity and relapse rates, we analyzed their AM expression profile by performing Real Time RT-PCR of 84 AM genes in CD34+ pools from the two extreme classes of patients (i.e., chemoresistant and highly chemosensitive), and compared them with normal controls. The AM expression profiles of patients highlighted features that could satisfactorily explain their in vitro chemoresponsive phenotype: specifically, in chemoresistant patients we detected up regulation of antiapoptotic BIRC genes and down regulation of proapoptotic APAF1, FAS, FASL, TNFRSF25. Interestingly, our analysis of the AM network showed that the dysregulated genes in these patients are characterized by high network centrality (i.e., high values of betweenness, closeness, radiality, stress) and high involvement in drug response. AM genes represent critical nodes for the proper execution of cell death following pharmacological induction in patients. We propose that their dysregulation (either due to inborn or de novo genomic

  5. Expression profile and specific network features of the apoptotic machinery explain relapse of acute myeloid leukemia after chemotherapy

    Di Pietro Cinzia

    2010-07-01

    Full Text Available Abstract Background According to the different sensitivity of their bone marrow CD34+ cells to in vitro treatment with Etoposide or Mafosfamide, Acute Myeloid Leukaemia (AML patients in apparent complete remission (CR after chemotherapy induction may be classified into three groups: (i normally responsive; (ii chemoresistant; (iii highly chemosensitive. This inversely correlates with in vivo CD34+ mobilization and, interestingly, also with the prognosis of the disease: patients showing a good mobilizing activity are resistant to chemotherapy and subject to significantly higher rates of Minimal Residual Disease (MRD and relapse than the others. Based on its known role in patients' response to chemotherapy, we hypothesized an involvement of the Apoptotic Machinery (AM in these phenotypic features. Methods To investigate the molecular bases of the differential chemosensitivity of bone marrow hematopoietic stem cells (HSC in CR AML patients, and the relationship between chemosensitivity, mobilizing activity and relapse rates, we analyzed their AM expression profile by performing Real Time RT-PCR of 84 AM genes in CD34+ pools from the two extreme classes of patients (i.e., chemoresistant and highly chemosensitive, and compared them with normal controls. Results The AM expression profiles of patients highlighted features that could satisfactorily explain their in vitro chemoresponsive phenotype: specifically, in chemoresistant patients we detected up regulation of antiapoptotic BIRC genes and down regulation of proapoptotic APAF1, FAS, FASL, TNFRSF25. Interestingly, our analysis of the AM network showed that the dysregulated genes in these patients are characterized by high network centrality (i.e., high values of betweenness, closeness, radiality, stress and high involvement in drug response. Conclusions AM genes represent critical nodes for the proper execution of cell death following pharmacological induction in patients. We propose that their

  6. Oscillatory neuronal activity reflects lexical-semantic feature integration within and across sensory modalities in distributed cortical networks.

    van Ackeren, Markus J; Schneider, Till R; Müsch, Kathrin; Rueschemeyer, Shirley-Ann

    2014-10-22

    Research from the previous decade suggests that word meaning is partially stored in distributed modality-specific cortical networks. However, little is known about the mechanisms by which semantic content from multiple modalities is integrated into a coherent multisensory representation. Therefore we aimed to characterize differences between integration of lexical-semantic information from a single modality compared with two sensory modalities. We used magnetoencephalography in humans to investigate changes in oscillatory neuronal activity while participants verified two features for a given target word (e.g., "bus"). Feature pairs consisted of either two features from the same modality (visual: "red," "big") or different modalities (auditory and visual: "red," "loud"). The results suggest that integrating modality-specific features of the target word is associated with enhanced high-frequency power (80-120 Hz), while integrating features from different modalities is associated with a sustained increase in low-frequency power (2-8 Hz). Source reconstruction revealed a peak in the anterior temporal lobe for low-frequency and high-frequency effects. These results suggest that integrating lexical-semantic knowledge at different cortical scales is reflected in frequency-specific oscillatory neuronal activity in unisensory and multisensory association networks. Copyright © 2014 the authors 0270-6474/14/3314318-06$15.00/0.

  7. Analysis of significance of environmental factors in landslide susceptibility modeling: Case study Jemma drainage network, Ethiopia

    Vít Maca

    2017-06-01

    Full Text Available Aim of the paper is to describe methodology for calculating significance of environmental factors in landslide susceptibility modeling and present result of selected one. As a study area part of a Jemma basin in Ethiopian Highland is used. This locality is highly affected by mass movement processes. In the first part all major factors and their influence are described briefly. Majority of the work focuses on research of other methodologies used in susceptibility models and design of own methodology. This method is unlike most of the methods used completely objective, therefore it is not possible to intervene in the results. In article all inputs and outputs of the method are described as well as all stages of calculations. Results are illustrated on specific examples. In study area most important factor for landslide susceptibility is slope, on the other hand least important is land cover. At the end of article landslide susceptibility map is created. Part of the article is discussion of results and possible improvements of the methodology.

  8. Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

    Yan-li Yang

    2015-01-01

    Full Text Available It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

  9. Using Artificial Neural Networks to Determine Significant Factors Affecting the Pricing of WPT Effluent for Industrial Uses in Isfahan

    Masoud Mirmohamadsaseghi

    2017-03-01

    Full Text Available The evidence indicates increasing trend of use of municipal wastewater treatment effluent as an alternative source of water both in developed and developing countries. Proper pricing of this unconventional water is one of the most effective economic tools to encourage optimum use of fresh water resources. In this study, artificial neural network is employed to identify and assess the factors affecting effluent tariffs supplied to local industries in Isfahan region. Given the wide variety of factors involved in the ultimate value of wastewater traement plant effluent, an assortment of relevant factors  has been considered in this study; the factors include the population served by the treatment plant, volume of effluent produced, maintenance, repair and replacement. costs of operating plants, topography, different water uses in the region, industrial wastewater collection fees, unit cost of pipe and fittings, and the volumes of water supplied from springs and aqueducts  in the region. Neural network modeling is used as a tool to determine the significance of each factor for pricing effluent. Based on the available data and the neural network models, the effects of different model architectures with different intermediate layers and numbers of nodes in each layer on the price of wastewater were investigated to develop aand adopt a final neural network model. Results indicate that the proposed neural network model enjoys a high potential and has been well capable of determining the weights of the parameter affecting in pricing effluent. Based on the the results of this study, the factors with the greatest role in effluent pricing are unit cost of pipe and fittings, industrial use of water, and the costs of plant maintentance, repair and replacement.

  10. Batch Image Encryption Using Generated Deep Features Based on Stacked Autoencoder Network

    Fei Hu

    2017-01-01

    Full Text Available Chaos-based algorithms have been widely adopted to encrypt images. But previous chaos-based encryption schemes are not secure enough for batch image encryption, for images are usually encrypted using a single sequence. Once an encrypted image is cracked, all the others will be vulnerable. In this paper, we proposed a batch image encryption scheme into which a stacked autoencoder (SAE network was introduced to generate two chaotic matrices; then one set is used to produce a total shuffling matrix to shuffle the pixel positions on each plain image, and another produces a series of independent sequences of which each is used to confuse the relationship between the permutated image and the encrypted image. The scheme is efficient because of the advantages of parallel computing of SAE, which leads to a significant reduction in the run-time complexity; in addition, the hybrid application of shuffling and confusing enhances the encryption effect. To evaluate the efficiency of our scheme, we compared it with the prevalent “logistic map,” and outperformance was achieved in running time estimation. The experimental results and analysis show that our scheme has good encryption effect and is able to resist brute-force attack, statistical attack, and differential attack.

  11. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

    Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

    2016-05-01

    We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

  12. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

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

    De, C.; Chakraborty, B.

    7.0, Neural network Toolbox, The Math Works, Inc., 1984 -2004. Michalopoulou, Z. -H., D. Alexandrou, and C. de Moustier. 1995. Application of neural and statistical classifiers to the problem of seafloor characterization. IEEE Journal of Oceanic...

  14. Distinct neural networks for target feature versus dimension changes in visual search, as revealed by EEG and fMRI.

    Becker, Stefanie I; Grubert, Anna; Dux, Paul E

    2014-11-15

    In visual search, responses are slowed, from one trial to the next, both when the target dimension changes (e.g., from a color target to a size target) and when the target feature changes (e.g., from a red target to a green target) relative to being repeated across trials. The present study examined whether such feature and dimension switch costs can be attributed to the same underlying mechanism(s). Contrary to this contention, an EEG study showed that feature changes influenced visual selection of the target (i.e., delayed N2pc onset), whereas dimension changes influenced the later process of response selection (i.e., delayed s-LRP onset). An fMRI study provided convergent evidence for the two-system view: Compared with repetitions, feature changes led to increased activation in the occipital cortex, and superior and inferior parietal lobules, which have been implicated in spatial attention. By contrast, dimension changes led to activation of a fronto-posterior network that is primarily linked with response selection (i.e., pre-motor cortex, supplementary motor area and frontal areas). Taken together, the results suggest that feature and dimension switch costs are based on different processes. Specifically, whereas target feature changes delay attention shifts to the target, target dimension changes interfere with later response selection operations. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

  15. Nanostructural Features of Radiation Cured Networks for High Performance Composites: From Incurred Heterogeneities to Tailored Nanocomposites

    Krzeminski, Mickael; Ranoux, Guillaume; Coqueret, Xavier; Molinari, Michael; Chabbert, Brigitte; Aguié, Véronique; Defoort, Brigitte

    2011-01-01

    The radiation-induced polymerization of multiacrylates is suspected to generate heterogeneous networks at various dimension scales. In order to gain an insight into the polymer microstructure, a combination of analytic methods was used to quantify polymer segment mobility in the different domains [4,5]. Model epoxy or ethoxylated bis-phenol A diacrylates, EPAC and ETAC respectively, were used as precursors of representative networks for our investigations

  16. Nanostructural Features of Radiation Cured Networks for High Performance Composites: From Incurred Heterogeneities to Tailored Nanocomposites

    Krzeminski, Mickael [Institut de Chimie Moléculaire de Reims (France); EADS Astrium, BP 20011, 33165 Saint Médard en Jalles Cedex (France); Ranoux, Guillaume; Coqueret, Xavier [Institut de Chimie Moléculaire de Reims (France); Molinari, Michael [Laboratoire des Microscopies et d’Etude des Nanostructures (France); Chabbert, Brigitte; Aguié, Véronique [UMR INRA Fractionnement des Agro-ressources et Environnement, Université de Reims Champagne Ardenne - 51687 Reims (France); Defoort, Brigitte [EADS Astrium, BP 20011, 33165 Saint Médard en Jalles Cedex, (France)

    2011-07-01

    The radiation-induced polymerization of multiacrylates is suspected to generate heterogeneous networks at various dimension scales. In order to gain an insight into the polymer microstructure, a combination of analytic methods was used to quantify polymer segment mobility in the different domains [4,5]. Model epoxy or ethoxylated bis-phenol A diacrylates, EPAC and ETAC respectively, were used as precursors of representative networks for our investigations.

  17. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

    He Weiming

    2010-07-01

    Full Text Available Abstract Background Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. Results Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways. Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. Conclusions Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

  19. Aboveground Biomass Estimation Using Reconstructed Feature of Airborne Discrete-Return LIDAR by Auto-Encoder Neural Network

    Li, T.; Wang, Z.; Peng, J.

    2018-04-01

    Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R2 by 5.44 %, 18.09 %, decreased RMSE value by 10.06 %, 22.13 % and reduced RMSEcv by 10.00 %, 21.70 % for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

  20. Features of spillover networks in international financial markets: Evidence from the G20 countries

    Liu, Xueyong; An, Haizhong; Li, Huajiao; Chen, Zhihua; Feng, Sida; Wen, Shaobo

    2017-08-01

    The objective of this study is to investigate volatility spillover transmission systematically in stock markets across the G20 countries. To achieve this objective, we combined GARCH-BEKK model with complex network theory using the linkages of spillovers. GARCH-BEKK model was used to capture volatility spillover between stock markets. Then, an information spillover network was built. The data encompass the main stock indexes from 19 individual countries in the G20. To consider the dynamic spillover, the full data set was divided into several sub-periods. The main contribution of this paper is considering the volatility spillover relationships as the edges of a complex network, which can capture the propagation path of volatility spillovers. The results indicate that the volatility spillovers among the stock markets of the G20 countries constitute a holistic associated network, another finding is that Korea acts a role of largest sender in long-term, while Brazil is the largest long-term recipient in the G20 spillover network.

  1. Risk assessment of safety data link and network communication in digital safety feature control system of nuclear power plant

    Lee, Sang Hun; Son, Kwang Seop; Jung, Wondea; Kang, Hyun Gook

    2017-01-01

    Highlights: • Safety data communication risk assessment framework and quantitative scheme were proposed. • Fault-tree model of ESFAS unavailability due to safety data communication failure was developed. • Safety data link and network risk were assessed based on various ESF-CCS design specifications. • The effect of fault-tolerant algorithm reliability of safety data network on ESFAS unavailability was assessed. - Abstract: As one of the safety-critical systems in nuclear power plants (NPPs), the Engineered Safety Feature-Component Control System (ESF-CCS) employs safety data link and network communication for the transmission of safety component actuation signals from the group controllers to loop controllers to effectively accommodate various safety-critical field controllers. Since data communication failure risk in the ESF-CCS has yet to be fully quantified, the ESF-CCS employing data communication systems have not been applied in NPPs. This study therefore developed a fault tree model to assess the data link and data network failure-induced unavailability of a system function used to generate an automated control signal for accident mitigation equipment. The current aim is to provide risk information regarding data communication failure in a digital safety feature control system in consideration of interconnection between controllers and the fault-tolerant algorithm implemented in the target system. Based on the developed fault tree model, case studies were performed to quantitatively assess the unavailability of ESF-CCS signal generation due to data link and network failure and its risk effect on safety signal generation failure. This study is expected to provide insight into the risk assessment of safety-critical data communication in a digitalized NPP instrumentation and control system.

  2. EEG signal classification based on artificial neural networks and amplitude spectra features

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  3. Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

    Ahmed, M; Gu, F; Ball, A

    2011-01-01

    Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

  4. Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

    Ahmed, M; Gu, F; Ball, A, E-mail: M.Ahmed@hud.ac.uk [Diagnostic Engineering Research Group, University of Huddersfield, HD1 3DH (United Kingdom)

    2011-07-19

    Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

  5. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

    Sivaramakrishnan Rajaraman

    2018-04-01

    Full Text Available Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx methods using machine learning (ML techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI. In contrast, Convolutional Neural Networks (CNN, a class of deep learning (DL models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.

  6. Web-based thyroid imaging reporting and data system: Malignancy risk of atypia of undetermined significance or follicular lesion of undetermined significance thyroid nodules calculated by a combination of ultrasonography features and biopsy results.

    Choi, Young Jun; Baek, Jung Hwan; Shin, Jung Hee; Shim, Woo Hyun; Kim, Seon-Ok; Lee, Won-Hong; Song, Dong Eun; Kim, Tae Yong; Chung, Ki-Wook; Lee, Jeong Hyun

    2018-05-13

    The purpose of this study was to construct a web-based predictive model using ultrasound characteristics and subcategorized biopsy results for thyroid nodules of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) to stratify the risk of malignancy. Data included 672 thyroid nodules from 656 patients from a historical cohort. We analyzed ultrasound images of thyroid nodules and biopsy results according to nuclear atypia and architectural atypia. Multivariate logistic regression analysis was performed to predict whether nodules were diagnosed as malignant or benign. The ultrasound features, including spiculated margin, marked hypoechogenicity, calcifications, biopsy results, and cytologic atypia, showed significant differences between groups. A 13-point risk scoring system was developed, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the development and validation sets were 0.837 and 0.830, respectively (http://www.gap.kr/thyroidnodule_b3.php). We devised a web-based predictive model using the combined information of ultrasound characteristics and biopsy results for AUS/FLUS thyroid nodules to stratify the malignant risk. © 2018 Wiley Periodicals, Inc.

  7. Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

    Jie Wang

    2017-03-01

    Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

  8. An Evaluation of optional timing/synchronization features to support selection of an optimum design for the DCS digital communication network

    Bradley, D. B.; Cain, J. B., III; Williard, M. W.

    1978-01-01

    The task was to evaluate the ability of a set of timing/synchronization subsystem features to provide a set of desirable characteristics for the evolving Defense Communications System digital communications network. The set of features related to the approaches by which timing/synchronization information could be disseminated throughout the network and the manner in which this information could be utilized to provide a synchronized network. These features, which could be utilized in a large number of different combinations, included mutual control, directed control, double ended reference links, independence of clock error measurement and correction, phase reference combining, and self organizing.

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

    Naidoo, T

    2015-09-01

    Full Text Available for Anomaly–Based Network Intrusion Detection Using Cluster Validity Indices Tyrone Naidoo_, Jules–Raymond Tapamoy, Andre McDonald_ Modelling and Digital Science, Council for Scientific and Industrial Research, South Africa 1tnaidoo2@csir.co.za 3...

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

    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.

  11. An initiative to improve the management of clinically significant test results in a large health care network.

    Roy, Christopher L; Rothschild, Jeffrey M; Dighe, Anand S; Schiff, Gordon D; Graydon-Baker, Erin; Lenoci-Edwards, Jennifer; Dwyer, Cheryl; Khorasani, Ramin; Gandhi, Tejal K

    2013-11-01

    The failure of providers to communicate and follow up clinically significant test results (CSTR) is an important threat to patient safety. The Massachusetts Coalition for the Prevention of Medical Errors has endorsed the creation of systems to ensure that results can be received and acknowledged. In 2008 a task force was convened that represented clinicians, laboratories, radiology, patient safety, risk management, and information systems in a large health care network with the goals of providing recommendations and a road map for improvement in the management of CSTR and of implementing this improvement plan during the sub-force sequent five years. In drafting its charter, the task broadened the scope from "critical" results to "clinically significant" ones; clinically significant was defined as any result that requires further clinical action to avoid morbidity or mortality, regardless of the urgency of that action. The task force recommended four key areas for improvement--(1) standardization of policies and definitions, (2) robust identification of the patient's care team, (3) enhanced results management/tracking systems, and (4) centralized quality reporting and metrics. The task force faced many challenges in implementing these recommendations, including disagreements on definitions of CSTR and on who should have responsibility for CSTR, changes to established work flows, limitations of resources and of existing information systems, and definition of metrics. This large-scale effort to improve the communication and follow-up of CSTR in a health care network continues with ongoing work to address implementation challenges, refine policies, prepare for a new clinical information system platform, and identify new ways to measure the extent of this important safety problem.

  12. Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features.

    Gao, Zhong-Ke; Jin, Ning-De; Wang, Wen-Xu; Lai, Ying-Cheng

    2010-07-01

    The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

  13. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  14. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  15. Prognostic significance of social network, social support and loneliness for course of major depressive disorder in adulthood and old age.

    van den Brink, R H S; Schutter, N; Hanssen, D J C; Elzinga, B M; Rabeling-Keus, I M; Stek, M L; Comijs, H C; Penninx, B W J H; Oude Voshaar, R C

    2018-06-01

    Poor recovery from depressive disorder has been shown to be related to low perceived social support and loneliness, but not to social network size or frequency of social interactions. Some studies suggest that the significance of social relationships for depression course may be greater in younger than in older patients, and may differ between men and women. None of the studies examined to what extent the different aspects of social relationships have unique or overlapping predictive values for depression course. It is the aim of the present study to examine the differential predictive values of social network characteristics, social support and loneliness for the course of depressive disorder, and to test whether these predictive associations are modified by gender or age. Two naturalistic cohort studies with the same design and overlapping instruments were combined to obtain a study sample of 1474 patients with a major depressive disorder, of whom 1181 (80.1%) could be studied over a 2-year period. Social relational variables were assessed at baseline. Two aspects of depression course were studied: remission at 2-year follow-up and change in depression severity over the follow-up period. By means of logistic regression and random coefficient analysis, the individual and combined predictive values of the different social relational variables for depression course were studied, controlling for potential confounders and checking for effect modification by age (below 60 v. 60 years or older) and gender. Multiple aspects of the social network, social support and loneliness were related to depression course, independent of potential confounders - including depression severity - but when combined, their predictive values were found to overlap to a large extent. Only the social network characteristic of living in a larger household, the social support characteristic of few negative experiences with the support from a partner or close friend, and limited feelings of

  16. Switching auditory attention using spatial and non-spatial features recruits different cortical networks.

    Larson, Eric; Lee, Adrian K C

    2014-01-01

    Switching attention between different stimuli of interest based on particular task demands is important in many everyday settings. In audition in particular, switching attention between different speakers of interest that are talking concurrently is often necessary for effective communication. Recently, it has been shown by multiple studies that auditory selective attention suppresses the representation of unwanted streams in auditory cortical areas in favor of the target stream of interest. However, the neural processing that guides this selective attention process is not well understood. Here we investigated the cortical mechanisms involved in switching attention based on two different types of auditory features. By combining magneto- and electro-encephalography (M-EEG) with an anatomical MRI constraint, we examined the cortical dynamics involved in switching auditory attention based on either spatial or pitch features. We designed a paradigm where listeners were cued in the beginning of each trial to switch or maintain attention halfway through the presentation of concurrent target and masker streams. By allowing listeners time to switch during a gap in the continuous target and masker stimuli, we were able to isolate the mechanisms involved in endogenous, top-down attention switching. Our results show a double dissociation between the involvement of right temporoparietal junction (RTPJ) and the left inferior parietal supramarginal part (LIPSP) in tasks requiring listeners to switch attention based on space and pitch features, respectively, suggesting that switching attention based on these features involves at least partially separate processes or behavioral strategies. © 2013 Elsevier Inc. All rights reserved.

  17. Prognostic significance of several histological features in intermediate and high-risk endometrial cancer patients treated with curative intent using surgery and adjuvant radiotherapy

    Narayan, K.; Bernshaw, D.; Quinn, M.; Allen, D.; Rejeki, V.; Herschtal, A.; Jobling, T.

    2009-01-01

    Full text: The purpose of the present study was to explore the prognostic significance of several histological features with respect to lymph node metastasis, failure-free survival (FeS), and overall survival (Os) in intermediate and high-risk endometrial cancer patients treated with curative intent. One hundred and eighty patients with endometrial cancer were treated with hysterectomy with or without lymphadenectomy and received external beam radiotherapy (EBRT). The mean follow-up period was 4.25 years (range 0.44-10.45 years). In multifactor analysis, fractional myometrial invasion (MI) (P = 0.047), histology (P < 0.001) and lymph-vascular space invasion (LVSI) (P = 0.025) were significant predictors for FFS when nodal status was not included. When lymph node status was known, histology (P - 0.007) and LVSI (P = 0.014) remained significant factors for FFS. For OS, histology (P < 0.001) and fractional MI (P = 0.004) were the significant factors. Lymph node status could be predicted by tumour grading (P = 0.016) and absolute MI (P 0.002). Histology type and the presence of LVSI were the most important prognostic factors in high-risk endometrial cancer patients treated by surgery and postoperative radiotherapy. Absolute MI and tumour grading were useful predictors of nodal spread.

  18. Image Fusion Based on the Self-Organizing Feature Map Neural Networks

    ZHANG Zhaoli; SUN Shenghe

    2001-01-01

    This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

  19. Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

    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%.

  20. Ordination of self-organizing feature map neural networks and its application to the study of plant communities

    Jintun ZHANG; Dongping MENG; Yuexiang XI

    2009-01-01

    A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

  1. Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

    Mioulet, L.; Bideault, G.; Chatelain, C.; Paquet, T.; Brunessaux, S.

    2015-01-01

    The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

  2. Feature-Augmented Neural Networks for Patient Note De-identification

    Lee, Ji Young; Dernoncourt, Franck; Uzuner, Ozlem; Szolovits, Peter

    2016-01-01

    Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-t...

  3. FEATURES OF SELECTION OF CAPACITOR BANKS IN ELECTRIC NETWORKS WITH INTERHARMONIC SOURCES

    Yu. L. Sayenko

    2017-10-01

    Full Text Available Purpose. Development of a methodology for selecting capacitor bank parameters designed to compensate for reactive power, if there are sources of interharmonics in the electrical network. Development of a methodology for selecting the parameters of capacitor banks that are part of resonant filters of higher harmonics and interharmonics. Methodology. For the research, we used the decomposition of the non-sinusoidal voltage (current curve into the sum of the harmonic components with frequencies as multiple of the fundamental frequency - higher harmonics, and not multiple fundamental frequencies - interharmonics. Results. Expressions are obtained for checking the absence of inadmissible overloads of capacitor banks by voltage and current in the presence of voltage (current in the curve, along with higher harmonics, of the discrete spectrum of interharmonics. When selecting capacitor banks, both for reactive power compensation and for filter-compensating devices, the necessity of constructing the frequency characteristics of the input and mutual resistances of the electrical network for analyzing possible resonant phenomena is confirmed. Originality. The expediency of simplified calculation of the voltage variation at the terminals of the banks of the capacitors of the higher harmonics filters and interharmonics due to the presence of the reactor in the filters is substantiated. Practical value. The use of the proposed approaches will make it possible to resolve a number of issues related to the choice of parameters of capacitor banks in networks with nonlinear loads, including: ensuring reliable operation of capacitor banks when their parameters deviate from their nominal values, as well as deviations in the parameters of the supply network and sources of harmonic distortion; ensuring the absence of resonant phenomena at frequencies of both higher harmonics and interharmonics.

  4. Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

    Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

    2016-10-24

    Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. EDUCATIONAL NETWORK RESOURCES IN JOURNALISM AND PUBLISHING: FEATURES OF THE SITES' CONTENT AND DESIGN

    Hanna A. Zenzina

    2012-12-01

    Full Text Available The article deals with the concept of electronic educational resource, its importance for the orientation of students in cyberspace, the basic requirements for the design and content of educational resources eligibility criteria standards. Filed benefits of having their own educational resource for high school. Singled out the importance of the interaction of educational resources with social networks. Detected differences and similarities of design and content of educational resources of Kyiv Universities in journalism and publishing.

  6. Significant differe nces in demographic, clinical, and pathological features in relation to smoking and alcohol consumption among 1,633 head and neck cancer patients

    Raquel Ajub Moyses

    2013-06-01

    Full Text Available OBJECTIVE: As a lifestyle-related disease, social and cultural disparities may influence the features of squamous cell carcinoma of the head and neck in different geographic regions. We describe demographic, clinical, and pathological aspects of squamous cell carcinoma of the head and neck according to the smoking and alcohol consumption habits of patients in a Brazilian cohort. METHODS: We prospectively analyzed the smoking and alcohol consumption habits of 1,633 patients enrolled in five São Paulo hospitals that participated in the Brazilian Head and Neck Genome Project - Gencapo. RESULTS: The patients who smoked and drank were younger, and those who smoked were leaner than the other patients, regardless of alcohol consumption. The non-smokers/non-drinkers were typically elderly white females who had more differentiated oral cavity cancers and fewer first-degree relatives who smoked. The patients who drank presented significantly more frequent nodal metastasis, and those who smoked presented less-differentiated tumors. CONCLUSIONS: The patients with squamous cell carcinoma of the head and neck demonstrated demographic, clinical, and pathological features that were markedly different according to their smoking and drinking habits. A subset of elderly females who had oral cavity cancer and had never smoked or consumed alcohol was notable. Alcohol consumption seemed to be related to nodal metastasis, whereas smoking correlated with the degree of differentiation.

  7. Assortative and dissortative priorities for game interaction and strategy adaptation significantly bolster network reciprocity in the prisoner’s dilemma

    Tanimoto, Jun

    2014-01-01

    In 2 × 2 prisoner’s dilemma games, network reciprocity is one mechanism for adding social viscosity, which leads to cooperative equilibrium. Here we show that combining the process for selecting a gaming partner with the process for selecting an adaptation partner significantly enhances cooperation, even though such selection processes require additional costs to collect further information concerning which neighbor should be chosen. Based on elaborate investigations of the dynamics generated by our model, we find that high levels of cooperation result from two kinds of behavior: cooperators tend to interact with cooperators to prevent being exploited by defectors and defectors tend to choose cooperators to exploit despite the possibility that some defectors convert to cooperators. (paper)

  8. MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification.

    Burgess, K E V; Borutzki, Y; Rankin, N; Daly, R; Jourdan, F

    2017-12-15

    Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships. We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  9. Features of Random Metal Nanowire Networks with Application in Transparent Conducting Electrodes

    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.

  10. Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

    Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

    2015-01-01

    This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

  11. PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

    McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

    2009-01-01

    Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of

  12. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma.

    Cheng, Nai-Ming; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Huang, Chung-Guei; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Lin, Chien-Yu; Liao, Chun-Ta; Yen, Tzu-Chen

    2013-10-01

    Previous studies have shown that total lesion glycolysis (TLG) may serve as a prognostic indicator in oropharyngeal squamous cell carcinoma (OPSCC). We sought to investigate whether the textural features of pretreatment (18)F-FDG PET/CT images can provide any additional prognostic information over TLG and clinical staging in patients with advanced T-stage OPSCC. We retrospectively analyzed the pretreatment (18)F-FDG PET/CT images of 70 patients with advanced T-stage OPSCC who had completed concurrent chemoradiotherapy, bioradiotherapy, or radiotherapy with curative intent. All of the patients had data on human papillomavirus (HPV) infection and were followed up for at least 24 mo or until death. A standardized uptake value (SUV) of 2.5 was taken as a cutoff for tumor boundary. The textural features of pretreatment (18)F-FDG PET/CT images were extracted from histogram analysis (SUV variance and SUV entropy), normalized gray-level cooccurrence matrix (uniformity, entropy, dissimilarity, contrast, homogeneity, inverse different moment, and correlation), and neighborhood gray-tone difference matrix (coarseness, contrast, busyness, complexity, and strength). Receiver-operating-characteristic curves were used to identify the optimal cutoff values for the textural features and TLG. Thirteen patients were HPV-positive. Multivariate Cox regression analysis showed that age, tumor TLG, and uniformity were independently associated with progression-free survival (PFS) and disease-specific survival (DSS). TLG, uniformity, and HPV positivity were significantly associated with overall survival (OS). A prognostic scoring system based on TLG and uniformity was derived. Patients who presented with TLG > 121.9 g and uniformity ≤ 0.138 experienced significantly worse PFS, DSS, and OS rates than those without (P 121.9 g or uniformity ≤ 0.138 were further divided according to age, and different PFS and DSS were observed. Uniformity extracted from the normalized gray

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

    Edyta Balejko; Zbigniew Nowak; Jerzy A. Balejko

    2012-01-01

      Background. 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. Material and methods. 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 us...

  14. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  15. A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

    Ashfaq Ahmad

    2015-12-01

    Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

  16. An additional DNS feature for different routing of electronic mail inside and outside of a campus network

    Bobyshev, A.; Ernst, M.

    2001-01-01

    Several years ago DESY faced the need to change the Electronic Mail Service to support it on a central cluster of servers. The centralized architecture was necessary for deployment of unified internal E-Mail standards, better quality of service and security. To implement a new policy for Electronic Mail Service and avoid huge modifications to a few hundreds network nodes, an additional DNS feature has been added to ISC's (Internet Software Consortium) software bind-4.9.7. The DNS servers running at DESY are capable of distinguishing between DNS queries coming from inside and outside of the campus network and reply with different list of MX (Mail Exchanger) records. The external hosts always get a list of MX records pointing to the central mail servers while the internal hosts may use different paths for mail exchange within the campus network. A modified version of DNS software has been used at DESY since 1997. It is fully compliant with the original goal of the project and shows good operational performance and reliability

  17. A method for risk-informed safety significance categorization using the analytic hierarchy process and bayesian belief networks

    Ha, Jun Su; Seong, Poong Hyun

    2004-01-01

    A risk-informed safety significance categorization (RISSC) is to categorize structures, systems, or components (SSCs) of a nuclear power plant (NPP) into two or more groups, according to their safety significance using both probabilistic and deterministic insights. In the conventional methods for the RISSC, the SSCs are quantitatively categorized according to their importance measures for the initial categorization. The final decisions (categorizations) of SSCs, however, are qualitatively made by an expert panel through discussions and adjustments of opinions by using the probabilistic insights compiled in the initial categorization process and combining the probabilistic insights with the deterministic insights. Therefore, owing to the qualitative and linear decision-making process, the conventional methods have the demerits as follows: (1) they are very costly in terms of time and labor, (2) it is not easy to reach the final decision, when the opinions of the experts are in conflict and (3) they have an overlapping process due to the linear paradigm (the categorization is performed twice - first, by the engineers who propose the method, and second, by the expert panel). In this work, a method for RISSC using the analytic hierarchy process (AHP) and bayesian belief networks (BBN) is proposed to overcome the demerits of the conventional methods and to effectively arrive at a final decision (or categorization). By using the AHP and BBN, the expert panel takes part in the early stage of the categorization (that is, the quantification process) and the safety significance based on both probabilistic and deterministic insights is quantified. According to that safety significance, SSCs are quantitatively categorized into three categories such as high safety significant category (Hi), potentially safety significant category (Po), or low safety significant category (Lo). The proposed method was applied to the components such as CC-V073, CV-V530, and SI-V644 in Ulchin Unit

  18. Features of intrinsic ganglionated plexi in both atria after extensive pulmonary isolation and their clinical significance after catheter ablation in patients with atrial fibrillation.

    Kurotobi, Toshiya; Shimada, Yoshihisa; Kino, Naoto; Ito, Kazato; Tonomura, Daisuke; Yano, Kentaro; Tanaka, Chiharu; Yoshida, Masataka; Tsuchida, Takao; Fukumoto, Hitoshi

    2015-03-01

    The features of intrinsic ganglionated plexi (GP) in both atria after extensive pulmonary vein isolation (PVI) and their clinical implications have not been clarified in patients with atrial fibrillation (AF). The purpose of this study was to assess the features of GP response after extensive PVI and to evaluate the relationship between GP responses and subsequent AF episodes. The study population consisted of 216 consecutive AF patients (104 persistent AF) who underwent an initial ablation. We searched for the GP sites in both atria after an extensive PVI. GP responses were determined in 186 of 216 patients (85.6%). In the left atrium, GP responses were observed around the right inferior GP in 116 of 216 patients (53.7%) and around the left inferior GP in 57 of 216 (26.4%). In the right atrium, GP responses were observed around the posteroseptal area: inside the CS in 64 of 216 patients (29.6%), at the CS ostium in 150 of 216 (69.4%), and in the lower right atrium in 45 of 216 (20.8%). The presence of a positive GP response was an independent risk factor for AF recurrence (hazard ratio 4.04, confidence interval 1.48-11.0) in patients with paroxysmal, but not persistent, AF. The incidence of recurrent atrial tachyarrhythmias in patients with paroxysmal AF with a positive GP response was 51% vs 8% in those without a GP response (P = .002). The presence of GP responses after extensive PVI was significantly associated with increased AF recurrence after ablation in patients with paroxysmal AF. Copyright © 2015 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

  19. Network features of sector indexes spillover effects in China: A multi-scale view

    Feng, Sida; Huang, Shupei; Qi, Yabin; Liu, Xueyong; Sun, Qingru; Wen, Shaobo

    2018-04-01

    The spillover effects among sectors are of concern for distinct market participants, who are in distinct investment horizons and concerned with the information in different time scales. In order to uncover the hidden spillover information in multi-time scales in the rapidly changing stock market and thereby offer guidance to different investors concerning distinct time scales from a system perspective, this paper constructed directional spillover effect networks for the economic sectors in distinct time scales. The results are as follows: (1) The "2-4 days" scale is the most risky scale, and the "8-16 days" scale is the least risky one. (2) The most influential and sensitive sectors are distinct in different time scales. (3) Although two sectors in the same community may not have direct spillover relations, the volatility of one sector will have a relatively strong influence on the other through indirect relations.

  20. Impacts of battery characteristics, driver preferences and road network features on travel costs of a plug-in hybrid electric vehicle (PHEV) for long-distance trips

    Arslan, Okan; Yıldız, Barış; Ekin Karaşan, Oya

    2014-01-01

    In a road network with refueling and fast charging stations, the minimum-cost driving path of a plug-in hybrid electric vehicle (PHEV) depends on factors such as location and availability of refueling/fast charging stations, capacity and cost of PHEV batteries, and driver tolerance towards extra mileage or additional stopping. In this paper, our focus is long-distance trips of PHEVs. We analyze the impacts of battery characteristics, often-overlooked driver preferences and road network features on PHEV travel costs for long-distance trips and compare the results with hybrid electric and conventional vehicles. We investigate the significance of these factors and derive critical managerial insights for shaping the future investment decisions about PHEVs and their infrastructure. In particular, our findings suggest that with a certain level of deployment of fast charging stations, well established cost and emission benefits of PHEVs for the short range trips can be extended to long distance. Drivers' stopping intolerance may hamper these benefits; however, increasing battery capacity may help overcome the adverse effects of this intolerance. - Highlights: • We investigate the travel costs of CVs, HEVs and PHEVs for long-distance trips. • We analyze the impacts of battery, driver and road network characteristics on the costs. • We provide critical managerial insights to shape the investment decisions about PHEVs. • Drivers' stopping intolerance may hamper the cost and emission benefits of PHEVs. • Negative effect of intolerance on cost may be overcome by battery capacity expansion

  1. Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

    Keshavarz, M; Mojra, A

    2015-05-01

    Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

  2. AUTOMATIC GENERALIZABILITY METHOD OF URBAN DRAINAGE PIPE NETWORK CONSIDERING MULTI-FEATURES

    S. Zhu

    2018-05-01

    Full Text Available Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

  3. EMG signals characterization in three states of contraction by fuzzy network and feature extraction

    Mokhlesabadifarahani, Bita

    2015-01-01

    Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.

  4. Automatic Generalizability Method of Urban Drainage Pipe Network Considering Multi-Features

    Zhu, S.; Yang, Q.; Shao, J.

    2018-05-01

    Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

  5. Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

    Jie-sheng Wang

    2014-01-01

    Full Text Available For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

  6. QUESTDONE APPLICATION WITH SOCIAL NETWORKING FEATURES AS THE ACTUAL WORLD INTERACTION MEDIA ON ANDROID SMARTPHONE

    Rhio Sutoyo

    2012-10-01

    Full Text Available The purpose of this research is the design and implementation of an application that allows users to play and socialize at the same time and directly with the environment around the Android-based smart phone. This application is also expected to be a campaign media for a new product or specific event. The used method consists of two ways: the methods of analysis and design. The method of analysis includes the study of literatures, questionnaires, and comparisons with similar applications. The design method used for this research is Scrum. The obtained results are an application that helps users of Android-based smart phone to do social interaction and provide knowledge about the route that users will be addressed. It is also used as a new campaign media for entrepreneurs who want to promote a product or event. Furthermore, this application is also built with interesting but not complex design, thus allowing users to easily use it. The conclusions are this application provides experience for users to visit various places and can be a media campaign for a new product or specific event. It also becomes a tool of social interaction and useful for finding location of friends.Keywords: Applications; Social Networking; Interaction; Real World; Smartphone; Android

  7. The obtaining of statistical characteristics of informative features of signals in the Autonomous information systems using neural networks

    V. K. Hohlov

    2014-01-01

    Full Text Available The article studies a neural network approach to obtain the statistical characteristics of the input vector implementations of signal and noise at ill-conditioned matrices of correlation moments to solve the problems to select and reduce the vector dimensions of informative features at detection and recognition of signals and noise based on regression methods.A scientific novelty is determined by applying neural network algorithms for the efficient solution of problems to select the informative features and determine the parameters of regression algorithms in terms of the degeneracy or ill-conditioned data with unknown expectation and covariance matrices.The article proposes to use a single-layer neural network with no zero weights and activation functions to calculate the initial regression characteristics and the mean-square value error of multiple initial regression representations, which are necessary to justify the selection of informative features, reduce a dimension of sign vectors and implement the regression algorithms. It is shown that when excluding direct links between the inputs and their corresponding neurons, in the training network the weight coefficients of neuron inputs are the coefficients of initial multiple regression, the error meansquare value of multiple initial regression representations is calculated at the outputs of neurons. The article considers conditionality of the problem to calculate the matrix that is inverse one for matrix of correlation moments. It defines a condition number, which characterizes the relative error of stated task.The problem concerning the matrix condition of the correlation moment of informative signal features and noise arises when solving the problem to find the multiple coefficients of initial regression (MCIR and the residual mean-square values of the multiple regression representations. For obtaining the MCIR and finding the residual mean-square values the matrix of correlation moments of

  8. Implementing voice over Internet protocol in mobile ad hoc network – analysing its features regarding efficiency, reliability and security

    Naveed Ahmed Sheikh

    2014-05-01

    Full Text Available Providing secure and efficient real-time voice communication in mobile ad hoc network (MANET environment is a challenging problem. Voice over Internet protocol (VoIP has originally been developed over the past two decades for infrastructure-based networks. There are strict timing constraints for acceptable quality VoIP services, in addition to registration and discovery issues in VoIP end-points. In MANETs, ad hoc nature of networks and multi-hop wireless environment with significant packet loss and delays present formidable challenges to the implementation. Providing a secure real-time VoIP service on MANET is the main design objective of this paper. The authors have successfully developed a prototype system that establishes reliable and efficient VoIP communication and provides an extremely flexible method for voice communication in MANETs. The authors’ cooperative mesh-based MANET implementation can be used for rapidly deployable VoIP communication with survivable and efficient dynamic networking using open source software.

  9. Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

    Tlhabano, Lorato

    2018-05-01

    Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques' and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

  10. SPECIFIC FEATURES OF POWER CONSUMPTION OF LED DEVICES AND ACCOUNTING THEM IN CALCULATION OF ELECTRICAL NETWORKS

    V. N. Radkevich

    2016-01-01

    networks is the calculation of acceptable heat with respect to temperature of the environment.

  11. Frequency tuning allows flow direction control in microfluidic networks with passive features.

    Jain, Rahil; Lutz, Barry

    2017-05-02

    Frequency tuning has emerged as an attractive alternative to conventional pumping techniques in microfluidics. Oscillating (AC) flow driven through a passive valve can be rectified to create steady (DC) flow, and tuning the excitation frequency to the characteristic (resonance) frequency of the underlying microfluidic network allows control of flow magnitude using simple hardware, such as an on-chip piezo buzzer. In this paper, we report that frequency tuning can also be used to control the direction (forward or backward) of the rectified DC flow in a single device. Initially, we observed that certain devices provided DC flow in the "forward" direction expected from previous work with a similar valve geometry, and the maximum DC flow occurred at the same frequency as a prominent peak in the AC flow magnitude, as expected. However, devices of a slightly different geometry provided the DC flow in the opposite direction and at a frequency well below the peak AC flow. Using an equivalent electrical circuit model, we found that the "forward" DC flow occurred at the series resonance frequency (with large AC flow peak), while the "backward" DC flow occurred at a less obvious parallel resonance (a valley in AC flow magnitude). We also observed that the DC flow occurred only when there was a measurable differential in the AC flow magnitude across the valve, and the DC flow direction was from the channel with large AC flow magnitude to that with small AC flow magnitude. Using these observations and the AC flow predictions from the equivalent circuit model, we designed a device with an AC flowrate frequency profile that was expected to allow the DC flow in opposite directions at two distinct frequencies. The fabricated device showed the expected flow reversal at the expected frequencies. This approach expands the flow control toolkit to include both magnitude and direction control in frequency-tuned microfluidic pumps. The work also raises interesting questions about the

  12. MAIN FEATURES OF BALNEOLOGICAL AND MUD RESORTS NETWORK OF THE BLACK SEA COUNTRIES

    Молодецький, А. Е.; Пишна, Г. О.

    2016-01-01

    Purpose. The aim of the article is the characteristics of geographical (natural and socio-economic) prerequisites for the development of coastal resorts of Black Sea region countries, with emphasis on balneological and mud-bath of recreational resources systems. Six European and Asian countries – Ukraine, Russia, Georgia, Turkey, Bulgaria and Romania have a diverse and significant resort and recreational resources of the Black Sea coast. However, for many decades the Black Sea resorts of thes...

  13. Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

    Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

    2011-10-15

    Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

  14. Significant rise of the prevalence and clinical features of childhood asthma in Qingdao China: cluster sampling investigation of 10,082 children.

    Lin, Rongjun; Guan, Renzheng; Liu, Xiaomei; Zhao, Baochun; Guan, Jie; Lu, Ling

    2014-09-26

    Recent investigations suggested that the trend of childhood asthma has been stabilizing or even reversing in some countries. The observation provides contrast to our experience. Thus, the study aimed to investigate the prevalence and clinical features of asthma in children aged 0-14 years in Qingdao China, determine the changes of childhood asthma in China, and discover evidence that can allow better diagnosis and treatment of childhood asthma. A cluster sampling method was used. We randomly extracted the investigation clusters from schools, kindergartens, and communities in Qingdao. Subsequently, we interviewed the members of the clusters using a questionnaire from the International Study of Asthma and Allergies in Childhood (ISAAC) to find children with asthmatic symptoms. After determination by the doctors, more details on the asthmatic children were obtained by asking questions from the National Epidemiology Study of Asthma and Allergies in China questionnaire to obtain more details. We intended to survey 10,800 children. However, the actual number of children was 10,082. The prevalence of asthma in Qingdao children aged 0-14 years was 3.69%. The prevalence among male children was higher than in female (χ2 = 24.53,P China increased significantly based on data obtained ten years ago (2000). Respiratory tract infections were the most important precursors of asthma attack. The attack was most commonly manifested as cough. The treatment, especially the use of ICS, was more rational. However, a certain difference was found, which has yet to be contrasted with the Global Initiative for Asthma (GINA) project.

  15. A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

    Robinson, G.R.; Kapo, K.E.

    2004-01-01

    Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features. The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations. Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km2 (200 people/mile 2). No aggregate recycling operations are sited in counties with less than 19 people/km2 (50 people/mile2), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate. Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative

  16. Effect of various features on the life cycle cost of the timing/synchronization subsystem of the DCS digital communications network

    Kimsey, D. B.

    1978-01-01

    The effect on the life cycle cost of the timing subsystem was examined, when these optional features were included in various combinations. The features included mutual control, directed control, double-ended reference links, independence of clock error measurement and correction, phase reference combining, self-organization, smoothing for link and nodal dropouts, unequal reference weightings, and a master in a mutual control network. An overall design of a microprocessor-based timing subsystem was formulated. The microprocessor (8080) implements the digital filter portion of a digital phase locked loop, as well as other control functions such as organization of the network through communication with processors at neighboring nodes.

  17. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-01-01

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

  18. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-03-20

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  19. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Dat Tien Nguyen

    2017-03-01

    Full Text Available Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT, speed-up robust feature (SURF, local binary patterns (LBP, histogram of oriented gradients (HOG, and weighted HOG. Recently, the convolutional neural network (CNN method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  20. Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics.

    Sridharan, Gautham Vivek; D'Alessandro, Matthew; Bale, Shyam Sundhar; Bhagat, Vicky; Gagnon, Hugo; Asara, John M; Uygun, Korkut; Yarmush, Martin L; Saeidi, Nima

    2017-09-01

    Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

  1. Significance of application of the nine parametric coordinate transformation where local state network is not enough reliable

    Ristić Kornelija T.

    2016-01-01

    Full Text Available The most commonly used method for establishing the mathematical basis of surveying and spatial data collection is the method of Global Navigation Satellite Positioning System (GNSS. However, these data relate to the World Geodetic Date WGS84 which is different from the State geodetic network,. As a part of realization the project of determining spatial local reference network Mrkonjić Grad the GNSS observations on 15 trigonometric points whose position is known to the State system of coordinates (x, y, h were made. For the purpose of coordinate transformation between the two system two different transformation models were anlyzed. Beside the most commonly used Helmert seven parameter transformation, afina nine parametric transformation was tested. Comparing the two transformations models, conclusion was made that showes some benefits of using affina nine parameter transformation models in Republic of Serpska.

  2. A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

    Jiang, Guangli; Liu, Leibo; Zhu, Wenping; Yin, Shouyi; Wei, Shaojun

    2015-09-04

    This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

  3. A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction

    Guangli Jiang

    2015-09-01

    Full Text Available This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

  4. Assessment of the environmental significance of nutrients and heavy metal pollution in the river network of Serbia.

    Dević, Gordana; Sakan, Sanja; Đorđević, Dragana

    2016-01-01

    In this paper, the data for ten water quality variables collected during 2009 at 75 monitoring sites along the river network of Serbia are considered. The results are alarming because 48% of the studied sites were contaminated by Ni, Mn, Pb, As, and nutrients, which are key factors impairing the water quality of the rivers in Serbia. Special attention should be paid to Zn and Cu, listed in the priority toxic pollutants of US EPA for aquatic life protection. The employed Q-model cluster analysis grouped the data into three major pollution zones (low, moderate, and high). Most sites classified as "low pollution zones" (LP) were in the main rivers, whereas those classified as "moderate and high pollution zones" (MP and HP, respectively) were in the large and small tributaries/hydro-system. Principal component analysis/factor analysis (PCA/FA) showed that the dissolved metals and nutrients in the Serbian rivers varied depending on the river, the heterogeneity of the anthropogenic activities in the basins (influenced primarily by industrial wastewater, agricultural activities, and urban runoff pollution), and natural environmental variability, such as geological characteristics. In LP dominated non-point source pollution, such as agricultural and urban runoff, whereas mixed source pollution dominated in the MP and HP zones. These results provide information to be used for developing better pollution control strategies for the river network of Serbia.

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

    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.

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

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

    2015-01-01

    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

  7. Search of significant features in a direct non parametric pattern recognition method. Application to the classification of a multiwire spark chamber picture

    Buccheri, R.; Coffaro, P.; Di Gesu, V.; Salemi, S.; Colomba, G.

    1975-01-01

    Preliminary results are given of the application of a direct non parametric pattern recognition method to the classification of the pictures of a multiwire spark chamber. The method, developed in an earlier work for an optical spark chamber, looks promising. The picture sample used has with respect to the previous one, the following characteristis: a) the event pictures have a more complicated structure; b) the amount of background sparks in an event is greater; c) there exists a kind of noise which is almost always present in some structured way (double sparkling, bursts...). New features have been used to characterize the event pictures; the results show that the method could be also used as a super filter to reduce the cost of further analysis. (Auth.)

  8. [The peculiar features and significance of the psycho-emotional status of the patient in the context of functional and aesthetic rhinosurgery].

    Kim, I A; Nosulja, E V

    The objective of the present study was to summarize the literature data concerning the peculiar features of the psycho-emotional status of the patients presenting with nasal deformities and the importance of its evaluation for the preparation for the surgical rhinoplastic intervention. We present the results of analysis of the materials pertinent to the history and the current state of the psychological aspects of rhinoplasty, the importance of the development and application of the diagnostic tools for the evaluation of the psycho-emotional status of the patients presenting with deformations of the external nose. It is concluded that the disregard of the psycho-emotional status of the patients by a thonosurgeon and the resulting mistakes in determining indications for the surgical intervention can be the causes accounting for the patient's dissatisfaction with the outcome of even an ideally performed operation.

  9. Lattice constant changes leading to significant changes of the spin-gapless features and physical nature in a inverse Heusler compound Zr2MnGa

    Wang, Xiaotian; Cheng, Zhenxiang; Khenata, Rabah; Wu, Yang; Wang, Liying; Liu, Guodong

    2017-12-01

    The spin-gapless semiconductors with parabolic energy dispersions [1-3] have been recently proposed as a new class of materials for potential applications in spintronic devices. In this work, according to the Slater-Pauling rule, we report the fully-compensated ferrimagnetic (FCF) behavior and spin-gapless semiconducting (SGS) properties for a new inverse Heusler compound Zr2MnGa by means of the plane-wave pseudo-potential method based on density functional theory. With the help of GGA-PBE, the electronic structures and the magnetism of Zr2MnGa compound at its equilibrium and strained lattice constants are systematically studied. The calculated results show that the Zr2MnGa is a new SGS at its equilibrium lattice constant: there is an energy gap between the conduction and valence bands for both the majority and minority electrons, while there is no gap between the majority electrons in the valence band and the minority electrons in the conduction band. Remarkably, not only a diverse physical nature transition, but also different types of spin-gapless features can be observed with the change of the lattice constants. Our calculated results of Zr2MnGa compound indicate that this material has great application potential in spintronic devices.

  10. Software Defined Networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

    Shamugam, Veeramani; Murray, I; Leong, J A; Sidhu, Amandeep S

    2016-01-01

    Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations. (paper)

  11. Software Defined Networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

    Shamugam, Veeramani; Murray, I.; Leong, J. A.; Sidhu, Amandeep S.

    2016-03-01

    Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations.

  12. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Song, Jiangning; Li, Fuyi; Takemoto, Kazuhiro; Haffari, Gholamreza; Akutsu, Tatsuya; Chou, Kuo-Chen; Webb, Geoffrey I

    2018-04-14

    Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence-structure-function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence-structure-function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations

  13. The Significance and Impact of Innovation Networks of Academia and Business with a Special Emphasis on Work-Based Learning

    Hogeforster Max A.

    2014-10-01

    Full Text Available The Europe 2020 Strategy puts the quality and relevance of education and training systems at the heart of EU’s efforts to improve innovation and competitiveness and to achieve intelligent, sustainable and inclusive growth. The development of partnerships between vocational schools or higher-education institutions and the business sector must be considered as a critical factor in identifying learning requirements, improving the relevance of education and facilitating access to education and learning. The growing lack of skills is one of the major challenges for companies that rely on more highly qualified personnel. To increase the cooperation between academia and the business world means to integrate small and medium-sized enterprises (SMEs, since 99.2 per cent of European businesses are SMEs. They are the blood cells of the European economy and are essential for growth, yet a very heterogeneous group that can only be integrated in cooperation networks by intermediate organisations which tackle the needs of this diverse group of businesses. Such a partnership of 17 universities and polytechnics, including the University of Latvia, was founded in 2010 and is shortly introduced as a best practice example.To stay competitive in the globalised world, companies need to be innovative and that requires cooperation with knowledge institutions. A survey conducted in 2013 revealed that one of the major obstacles for SMEs to improve their innovation capabilities is their inability to find qualified personnel. This corresponds to the huge challenges the labour markets face in Europe. Almost all countries report a growing lack of skilled workforce while at the same time youth unemployment is increasing. This gap between the current qualifications and the qualifications demanded by businesses sector can be overcome by a closer cooperation between enterprises and education facilities, on a national but also international level between Western and Eastern

  14. Metastasis features of 546 patients with stage IV non-small cell lung cancer at first visit and the significance in radiotherapy

    Li Fenghu; Lu Bing; Fu Heyi; Han Lei; Li Qingsong; Li Huiqin

    2012-01-01

    Objective: To investigate the clinical metastasis features and the possibility of 3 dimensional radiotherapy of stage IV non-small cell lung cancer (NSCLC). Methods: The clinical materials of 546 patients with stage IV NSCLC and the relationship b T and N stage and metastasis were retrospectively analyzed. Results In 546 patients with stage IV NSCLC, the number with bone metastasis was 294, the number with brain metastasis was 167, the number with lung metastasis was 137, the number with liver metastasis was 79, the number with adrenal gland metastasis was 66, 37 with lymph node metastasis, 35 with subcutaneous metastasis and 10 with other organ metastasis. The number with single organ metastasis was 379 (69.4%) ,in which 37.7% with bone metastasis, 19.8% with brain metastasis, 16.9% with lung metastasis, 7.4% with liver metastasis, 7.4% with adrenal gland metastasis, 4.5% with lymph node metastasis, 5.5% with subcutaneous metastasis and 0.8% with other organ metastasis. The bone metastasis probability of T 3+4 patient was similar with T 1+2 (69.4%, 30.6%, χ 2 = 7.65, P = 0.067), but N 2+3 patient was more than N 0+1 (69.7%, 30.3%, χ 2 = 7.89, P = 0.044). The brain metastasis probability of T 3+4 patient was more than T 1+2 (70.7%, 29.3%, χ 2 = 10.64, P = 0.018), but N 2+3 patient was similar with N 0+1 (54.5%, 45.5%, χ 2 = 7.14, P = 0.079), and N 1+3+3 patient was more than N 0 (86.8%, 13.2%, χ 2 = 10.26, P = 0.024). Conclusions: In 546 patients with stage IV NSCLC, the most common metastatic organ is bone, the second is brain, the third is lung, the forth is liver, followed by adrenal gland; single organ metastasis is more common than multiple organ metastasis. The later the T stage is, the more severe is the metastasis. Through 3 dimensional radiotherapy, not only the quality of life of some stage IV patients is improved, but also the survival time was prolonged observably. (authors)

  15. SCinet Architecture: Featured at the International Conference for High Performance Computing,Networking, Storage and Analysis 2016

    Lyonnais, Marc; Smith, Matt; Mace, Kate P.

    2017-02-06

    SCinet is the purpose-built network that operates during the International Conference for High Performance Computing,Networking, Storage and Analysis (Super Computing or SC). Created each year for the conference, SCinet brings to life a high-capacity network that supports applications and experiments that are a hallmark of the SC conference. The network links the convention center to research and commercial networks around the world. This resource serves as a platform for exhibitors to demonstrate the advanced computing resources of their home institutions and elsewhere by supporting a wide variety of applications. Volunteers from academia, government and industry work together to design and deliver the SCinet infrastructure. Industry vendors and carriers donate millions of dollars in equipment and services needed to build and support the local and wide area networks. Planning begins more than a year in advance of each SC conference and culminates in a high intensity installation in the days leading up to the conference. The SCinet architecture for SC16 illustrates a dramatic increase in participation from the vendor community, particularly those that focus on network equipment. Software-Defined Networking (SDN) and Data Center Networking (DCN) are present in nearly all aspects of the design.

  16. Insights into significant pathways and gene interaction networks in peripheral blood mononuclear cells for early diagnosis of hepatocellular carcinoma

    Jian Xin Jiang

    2016-01-01

    Conclusions: Using identified DEGs, significantly changed biological processes such as nucleic acid metabolic process and KEGG pathways such as cytokine-cytokine receptor interaction in PBMCs of HCC patients were identified. In addition, several important hub genes, for example, CUL4A, and interleukin (IL 8 were also uncovered.

  17. Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

    Holland, David O; Johnson, Margaret E

    2018-03-01

    Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

  18. Salient Feature Selection Using Feed-Forward Neural Networks and Signal-to-Noise Ratios with a Focus Toward Network Threat Detection and Classification

    2014-03-27

    0.8.0. The virtual machine’s network adapter was set to internal network only to keep any outside traffic from interfering. A MySQL -based query...primary output of Fullstats is the ARFF file format, intended for use with the WEKA Java -based data mining software developed at the University of Waikato

  19. Fourth international conference on Networks & Communications

    Meghanathan, Natarajan; Nagamalai, Dhinaharan; Computer Networks & Communications (NetCom)

    2013-01-01

    Computer Networks & Communications (NetCom) is the proceedings from the Fourth International Conference on Networks & Communications. This book covers theory, methodology and applications of computer networks, network protocols and wireless networks, data communication technologies, and network security. The proceedings will feature peer-reviewed papers that illustrate research results, projects, surveys and industrial experiences that describe significant advances in the diverse areas of computer networks & communications.

  20. Metamorphic rock-hosted orogenic gold deposit style at Bombana (Southeast Sulawesi and Buru Island (Maluku: Their key features and significances for gold exploration in Eastern Indonesia

    Arifudin Idrus

    2017-06-01

    are identified. Early quartz veins are segmented, sigmoidal discontinuous and parallel to the foliation of the host rock. This generation of quartz veins is characterized by crystalline relatively clear quartz, and weakly mineralized with low sulfide and gold contents. The second type of quartz veins occurs within the ‘mineralized zone’ of about 100 m in width and ~1,000 m in length. Gold mineralization is intensely overprinted by argillic alteration. The mineralization-alteration zone is probably parallel to the mica schist foliation and strongly controlled by N-S or NE-SW-trending structures. Gold-bearing quartz veins are characterized by banded texture particularly following host rock foliation and sulphide banding, brecciated and rare bladed-like texture. Alteration types consist of propylitic (chlorite, calcite, sericite, argillic and carbonation represented by graphite banding and carbon flakes. Ore mineral comprises pyrite, native gold, pyrrhotite, and arsenopyrite. Cinnabar and stibnite are present in association with gold. Ore chemistry indicates that 11 out of 15 samples yielded more than 1 g/t Au, in which 6 of them graded in excess of 3 g/t Au. All high-grade samples are composed of limonite or partly contain limonitic material. This suggests the process of supergene enrichment. Interestingly, most of the high-grade samples contain also high concentrations of As (up to 991ppm, Sb (up to 885ppm, and Hg (up to 75ppm. Fluid inclusions in both quartz vein types consist of 4 phases including L-rich, V-rich, L-V-rich and L1-L2-V (CO2-rich phases. The mineralizing hydrothermal fluid typically is CO2-rich, of moderate temperature (300-400 ºC, and low salinity (0.36 to 0.54 wt.% NaCl eq. Based on those key features, gold mineralization in Bombana and Buru Island tends to meet the characteristics of orogenic, mesothermal types of gold deposit. Metamorphic rock-hosted gold deposits could represent the new targets for gold exploration particularly in Eastern

  1. BLACK HOLE ATTACK IN AODV & FRIEND FEATURES UNIQUE EXTRACTION TO DESIGN DETECTION ENGINE FOR INTRUSION DETECTION SYSTEM IN MOBILE ADHOC NETWORK

    HUSAIN SHAHNAWAZ

    2012-10-01

    Full Text Available Ad-hoc network is a collection of nodes that are capable to form dynamically a temporary network without the support of any centralized fixed infrastructure. Since there is no central controller to determine the reliable & secure communication paths in Mobile Adhoc Network, each node in the ad hoc network has to rely on each other in order to forward packets, thus highly cooperative nodes are required to ensure that the initiated data transmission process does not fail. In a mobile ad hoc network (MANET where security is a crucial issue and they are forced to rely on the neighbor node, trust plays an important role that could improve the number of successful data transmission. Larger the number of trusted nodes, higher successful data communication process rates could be expected. In this paper, Black Hole attack is applied in the network, statistics are collected to design intrusion detection engine for MANET Intrusion Detection System (IDS. Feature extraction and rule inductions are applied to find out the accuracy of detection engine by using support vector machine. In this paper True Positive generated by the detection engine is very high and this is a novel approach in the area of Mobile Adhoc Intrusion detection system.

  2. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

    Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

    2017-04-12

    Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

  3. Features at some significant estuaries of India

    Bhattathiri, P.M.A.

    in the second and 162 in the third. Most of the studies on various aspects have been confined to very few of these, and that too, mostly to minor ones. Very little work has been carried out from many of the estuaries of the major rivers. An overview...

  4. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Christoph Hartmann

    2015-12-01

    Full Text Available Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN, which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural

  5. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

    Carlos E. Galván-Tejada

    2016-01-01

    Full Text Available This work presents a human activity recognition (HAR model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC. Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source.

  6. Fifth International Conference on Networks & Communications

    Nagamalai, Dhinaharan; Rajasekaran, Sanguthevar

    2014-01-01

    This book covers theory, methodology and applications of computer networks, network protocols and wireless networks, data communication technologies, and network security. The book is based on the proceedings from the Fifth International Conference on Networks & Communications (NetCom). The proceedings will feature peer-reviewed papers that illustrate research results, projects, surveys and industrial experiences that describe significant advances in the diverse areas of computer networks & communications.

  7. Recent advances on failure and recovery in networks of networks

    Shekhtman, Louis M.; Danziger, Michael M.; Havlin, Shlomo

    2016-01-01

    Until recently, network science has focused on the properties of single isolated networks that do not interact or depend on other networks. However it has now been recognized that many real-networks, such as power grids, transportation systems, and communication infrastructures interact and depend on other networks. Here, we will present a review of the framework developed in recent years for studying the vulnerability and recovery of networks composed of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes, like for example certain people, play a role in two networks, i.e. in a multiplex. Dependency relations may act recursively and can lead to cascades of failures concluding in sudden fragmentation of the system. We review the analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. The general theory and behavior of interdependent networks has many novel features that are not present in classical network theory. Interdependent networks embedded in space are significantly more vulnerable compared to non-embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences. Finally, when recovery of components is possible, global spontaneous recovery of the networks and hysteresis phenomena occur. The theory developed for this process points to an optimal repairing strategy for a network of networks. Understanding realistic effects present in networks of networks is required in order to move towards determining system vulnerability.

  8. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

    Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

    2017-01-15

    Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

  9. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

    Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

    2016-07-01

    Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All

  10. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

  11. Evolutionarily significant units of the critically endangered leaf frog Pithecopus ayeaye (Anura, Phyllomedusidae) are not effectively preserved by the Brazilian protected areas network.

    de Magalhães, Rafael Félix; Lemes, Priscila; Camargo, Arley; Oliveira, Ubirajara; Brandão, Reuber Albuquerque; Thomassen, Hans; Garcia, Paulo Christiano de Anchietta; Leite, Felipe Sá Fortes; Santos, Fabrício Rodrigues

    2017-11-01

    Protected areas (PAs) are essential for biodiversity conservation, but their coverage is considered inefficient for the preservation of all species. Many species are subdivided into evolutionarily significant units (ESUs) and the effectiveness of PAs in protecting them needs to be investigated. We evaluated the usefulness of the Brazilian PAs network in protecting ESUs of the critically endangered Pithecopus ayeaye through ongoing climate change. This species occurs in a threatened mountaintop ecosystem known as campos rupestres . We used multilocus DNA sequences to delimit geographic clusters, which were further validated as ESUs with a coalescent approach. Ecological niche modeling was used to estimate spatial changes in ESUs' potential distributions, and a gap analysis was carried out to evaluate the effectiveness of the Brazilian PAs network to protect P. ayeaye in the face of climate changes. We tested the niche overlap between ESUs to gain insights for potential management alternatives for the species. Pithecopus ayeaye contains at least three ESUs isolated in distinct mountain regions, and one of them is not protected by any PA. There are no climatic niche differences between the units, and only 4% of the suitable potential area of the species is protected in present and future projections. The current PAs are not effective in preserving the intraspecific diversity of P. ayeaye in its present and future range distributions. The genetic structure of P. ayeaye could represent a typical pattern in campos rupestres endemics, which should be considered for evaluating its conservation status.

  12. Changes in T-cell subpopulations and cytokine network during early period of ibrutinib therapy in chronic lymphocytic leukemia patients: the significant decrease in T regulatory cells number.

    Podhorecka, Monika; Goracy, Aneta; Szymczyk, Agnieszka; Kowal, Malgorzata; Ibanez, Blanca; Jankowska-Lecka, Olga; Macheta, Arkadiusz; Nowaczynska, Aleksandra; Drab-Urbanek, Elzbieta; Chocholska, Sylwia; Jawniak, Dariusz; Hus, Marek

    2017-05-23

    B cell receptor (BCR) stimulation signal plays an important role in the pathogenesis of chronic lymphocytic leukemia (CLL), and kinase inhibitors directed toward the BCR pathway are now the promising anti-leukemic drugs. Ibrutinib, a Bruton tyrosine kinase inhibitor, demonstrates promising clinical activity in CLL. It is reported that ibrutinib, additionally to directly targeting leukemic cells, also inhibits the interactions of these cells with T cells, macrophages and accessory cells. Assessment of these mechanisms is important because of their non -direct anti-leukemic effects and to identify possible side effects connected with long-term drug administration.The aim of this study was to assess the in vivo effects of ibrutinib on T-cell subpopulations and cytokine network in CLL. The analysis was performed on a group of 19 patients during first month of ibrutinib therapy. The standard multicolor flow cytometry and cytometric bead array methods were used for assessment of T-cell subsets and cytokines/chemokines, respectively.The data obtained indicates that Ibrutinib treatment results in changes in T-cell subpopulations and cytokine network in CLL patients. Particularly, a significant reduction of T regulatory cells in peripheral blood was observed. By targeting these populations of T cells Ibrutinib can stimulate rejection of tumor cells by the immune system.

  13. Insights into significant pathways and gene interaction networks underlying breast cancer cell line MCF-7 treated with 17β-estradiol (E2).

    Huan, Jinliang; Wang, Lishan; Xing, Li; Qin, Xianju; Feng, Lingbin; Pan, Xiaofeng; Zhu, Ling

    2014-01-01

    Estrogens are known to regulate the proliferation of breast cancer cells and to alter their cytoarchitectural and phenotypic properties, but the gene networks and pathways by which estrogenic hormones regulate these events are only partially understood. We used global gene expression profiling by Affymetrix GeneChip microarray analysis, with KEGG pathway enrichment, PPI network construction, module analysis and text mining methods to identify patterns and time courses of genes that are either stimulated or inhibited by estradiol (E2) in estrogen receptor (ER)-positive MCF-7 human breast cancer cells. Of the genes queried on the Affymetrix Human Genome U133 plus 2.0 microarray, we identified 628 (12h), 852 (24h) and 880 (48 h) differentially expressed genes (DEGs) that showed a robust pattern of regulation by E2. From pathway enrichment analysis, we found out the changes of metabolic pathways of E2 treated samples at each time point. At 12h time point, the changes of metabolic pathways were mainly focused on pathways in cancer, focal adhesion, and chemokine signaling pathway. At 24h time point, the changes were mainly enriched in neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction and calcium signaling pathway. At 48 h time point, the significant pathways were pathways in cancer, regulation of actin cytoskeleton, cell adhesion molecules (CAMs), axon guidance and ErbB signaling pathway. Of interest, our PPI network analysis and module analysis found that E2 treatment induced enhancement of PRSS23 at the three time points and PRSS23 was in the central position of each module. Text mining results showed that the important genes of DEGs have relationship with signal pathways, such as ERbB pathway (AREG), Wnt pathway (NDP), MAPK pathway (NTRK3, TH), IP3 pathway (TRA@) and some transcript factors (TCF4, MAF). Our studies highlight the diverse gene networks and metabolic and cell regulatory pathways through which E2 operates to achieve its

  14. Implications of conspecific background noise for features of blue tit, Cyanistes caeruleus , communication networks at dawn

    Poesel, Angelika; Dabelsteen, Torben; Pedersen, Simon Boel

    2007-01-01

    Abstract  Communication among animals often comprises several signallers and receivers within the signal's transmission range. In such communication networks, individuals can extract information about differences in relative performance of conspecifics by eavesdropping on their signalling...... interactions. In songbirds, information can be encoded in the timing of signals, which either alternate or overlap, and both male and female receivers may utilise this information when engaging in territorial interactions or making reproductive decisions, respectively. We investigated how conspecific...... may potentially constrain the perception of singing patterns and may constitute costs for eavesdroppers. On the other hand, signallers may position themselves strategically and privatise their interactions....

  15. Significant Need for a French Network of Expert Centers Enabling a Better Characterization and Management of Treatment-Resistant Depression (Fondation FondaMental

    Antoine Yrondi

    2017-11-01

    Full Text Available BackgroundMajor depression is characterized by (i a high lifetime prevalence of 16–17% in the general population; (ii a high frequency of treatment resistance in around 20–30% of cases; (iii a recurrent or chronic course; (iv a negative impact on the general functioning and quality of life; and (v a high level of comorbidity with various psychiatric and non-psychiatric disorders, high occurrence of completed suicide, significant burden along with the personal, societal, and economic costs. In this context, there is an important need for the development of a network of expert centers for treatment-resistant depression (TRD, as performed under the leadership of the Fondation FondaMental.MethodsThe principal mission of this national network is to establish a genuine prevention, screening, and diagnosis policy for TRD to offer a systematic, comprehensive, longitudinal, and multidimensional evaluation of cases. A shared electronic medical file is used referring to a common exhaustive and standardized set of assessment tools exploring psychiatric, non-psychiatric, metabolic, biological, and cognitive dimensions of TRD. This is paralleled by a medico-economic evaluation to examine the global economic burden of the disease and related health-care resource utilization. In addition, an integrated biobank has been built by the collection of serum and DNA samples for the measurement of several biomarkers that could further be associated with the treatment resistance in the recruited depressed patients. A French observational long-term follow-up cohort study is currently in progress enabling the extensive assessment of resistant depressed patients. In those unresponsive cases, each expert center proposes relevant therapeutic options that are classically aligned to the international guidelines referring to recognized scientific societies.DiscussionThis approach is expected to improve the overall clinical assessments and to provide evidence

  16. A simple fracture energy prediction method for fiber network based on its morphological features extracted by X-ray tomography

    Huang, Xiang; Wang, Qinghui; Zhou, Wei; Li, Jingrong

    2013-01-01

    The fracture behavior of a novel porous metal fiber sintered sheet (PMFSS) was predicted using a semi-empirical method combining the knowledge of its morphological characteristics and micro-mechanical responses. The morphological characteristics were systematically summarized based on the analysis of the topologically identical skeleton representation extracted from the X-ray tomography images. The analytical model firstly proposed by Tan et al. [1] was further modified according to the experimental observations from both tensile tests of single fibers and sintered fiber sheets, which built the coupling of single fiber segment and fiber network in terms of fracture energy using a simple prediction method. The efficacy of the prediction model was verified by comparing the predicted results to the experimental measurements. The prediction error that arose at high porosity was analyzed through fiber orientation distribution. Moreover, the tensile fracture process evolving from single fiber segments at micro-scale to the global mechanical performance was investigated

  17. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

    Zhang, Kai; Long, Erping; Cui, Jiangtao; Zhu, Mingmin; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni

    2017-01-01

    Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model. PMID:28306716

  18. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network.

    Xiyang Liu

    Full Text Available Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI and employing a deep learning convolutional neural network (CNN. First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83% and a three-degree grading area (89.02%, 86.63%, and 90.75%, density (92.68%, 91.05%, and 93.94% and location (89.28%, 82.70%, and 93.08%. Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

  19. Discrete-feature modelling of the Aespoe Site: 1. Discrete-fracture network models for the repository scale

    Geier, J.E.; Thomas, A.L.

    1996-08-01

    This report describes the statistical derivation and partial validation of discrete-fracture network (DFN) models for the rock beneath the island of Aespoe in southeastern Sweden. The purpose was to develop DFN representations of the rock mass within a hypothetical, spent-fuel repository, located under Aespoe. Analyses are presented for four major lithologic types, with separate analyses of the rock within fracture zones, the rock excluding fracture zones, and all rock. Complete DFN models are proposed as descriptions of the rock mass in the near field. The procedure for validation, by comparison between actual and simulated packer tests, was found to be useful for discriminating among candidate DFN models. In particular, the validation approach was shown to be sensitive to a change in the fracture location (clustering) model, and to a change in the variance of single-fracture transmissivity. The proposed models are defined in terms of stochastic processes and statistical distributions, and thus are descriptive of the variability of the fracture system. This report includes discussion of the numerous sources of uncertainty in the models, including uncertainty that results from the variability of the natural system. 62 refs

  20. Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

    Salim Lahmiri

    2014-07-01

    Full Text Available This paper presents a forecasting model that integrates the discrete wavelet transform (DWT and backpropagation neural networks (BPNN for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-frequency and detail (high-frequency components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA model and the random walk (RW process.

  1. Cascading Failures and Recovery in Networks of Networks

    Havlin, Shlomo

    Network science have been focused on the properties of a single isolated network that does not interact or depends on other networks. In reality, many real-networks, such as power grids, transportation and communication infrastructures interact and depend on other networks. I will present a framework for studying the vulnerability and the recovery of networks of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes like certain locations play a role in two networks -multiplex. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system. I will present analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. I will show, that the general theory has many novel features that are not present in the classical network theory. When recovery of components is possible global spontaneous recovery of the networks and hysteresis phenomena occur and the theory suggests an optimal repairing strategy of system of systems. I will also show that interdependent networks embedded in space are significantly more vulnerable compared to non embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences.Thus, analyzing data of real network of networks is highly required to understand the system vulnerability. DTRA, ONR, Israel Science Foundation.

  2. NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-01-01

    is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino......β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method...... NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which...

  3. Networking

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  4. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

  5. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  6. Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs.

    Le, Nguyen-Quoc-Khanh; Ou, Yu-Yen

    2016-07-30

    Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists. We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron

  7. The Significance of Kinship for Medical Education: Reflections on the Use of a Bespoke Social Network to Support Learners' Professional Identities.

    Hatzipanagos, Stylianos; John, Bernadette; Chiu, Yuan-Li Tiffany

    2016-03-03

    Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. In this paper we report on the evaluation of an institutional social network-King's Social Harmonisation Project (KINSHIP)-established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King's College London, United Kingdom. Our evaluation focused on a study that examined students' needs and perceptions with regard to the provision of a cross-university platform. Data were collected from students, including those in the field of health and social care, in order to recommend a practical way forward to address current needs in this area. The findings indicate that the majority of the respondents were positive about using a social networking platform to develop their professional voice and profiles. Results suggest that timely promotion of the platform, emphasis on interface and learning design, and a clear identity are required in order to gain acceptance as the institutional social networking site. Empirical findings in this study project an advantage of an institutional social network such a KINSHIP over other social networks (eg, Facebook) because access is limited to staff and students and the site is mainly being used for academic purposes.

  8. Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

    Ge, Jing; Zhang, Guoping

    2015-01-01

    Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

  9. Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

    Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Grid Integration Group; Robinson, Matt [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering; Yasaei, Yasser [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Caudell, Thomas [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Martinez-Ramon, Manel [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Mammoli, Andrea [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering

    2016-07-01

    Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.

  10. A Legacy for IPY: The Global Snowflake Network (GSN) Together With Art and Ice, and Music and Ice; Unique new Features for Science Education.

    Wasilewski, P. J.

    2007-12-01

    The Global Snowflake Network (GSN) is a program that is simultaneously a science program and an education program. When the validation of the procedures (collection and identification of the type of snowflakes and the associated satellite image archive, as a serial record of a storm), is achieved, then the program becomes a scientific resource. This latter is the ultimate goal. That's why NASA has launched the Global Snowflake Network, a massive project that aims to involve the general public to "collect and classify" falling snowflakes. The data will be compiled into a massive database, along with satellite images, that will help climatologists and others who study climate-related phenomena gain a better understanding of wintry meteorology as they track various snowstorms around the globe. A great deal of information about the atmosphere dynamics and cloud microphysics can be derived from the serial collection and identification of the types of snow crystals and the degree of riming of the snow crystals during the progress of a snow storm. Forecasting winter weather depends in part on cloud physics, which deals with precipitation type, and if it happens to be snow- the crystal type, size, and density of the snowflake population. The History of Winter website will host the evolving snow and ice features for the IPY. Type "Global Snowflake Network" into the search engine (such as GOOGLE) and you will receive a demonstration of the operation of the preliminary GSN by the Indigenous community. The expeditions FINNMARK2007 and the POLAR Husky GoNorth 2007 expedition took the complement of Thermochrons with multimedia instructions for the Global Snowflake Network. This approach demonstrates the continuous Thermochron monitoring of expedition temperature and provides otherwise inaccessible snowflake information to NASA and others interested in the Polar region snow. In addition, reindeer herder and Ph.D. student, Inger Marie G. Eira, will incorporate the HOW, GSN

  11. Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

    Wible, Ryan S; Sutter, Thomas R

    2017-03-20

    The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

  12. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

    Bent Petersen

    Full Text Available UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

  13. NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-01-01

    β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Qtotal = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. Conclusion The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences. PMID:21152409

  14. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-11-30

    β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

  15. Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

    Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

    2018-04-01

    In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  16. ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

    L. Ding

    2018-04-01

    Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  17. Understanding Legacy Features with Featureous

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

  18. Entropy of network ensembles

    Bianconi, Ginestra

    2009-03-01

    In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

  19. Feature Article

    Home; Journals; Resonance – Journal of Science Education. Feature Article. Articles in Resonance – Journal of Science Education. Volume 1 Issue 1 January 1996 pp 80-85 Feature Article. What's New in Computers Windows 95 · Vijnan Shastri · More Details Fulltext PDF. Volume 1 Issue 1 January 1996 pp 86-89 Feature ...

  20. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  1. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  2. Myocardial deformation assessed by longitudinal strain. Chamber specific normative data for CMR-feature tracking from the German competence network for congenital heart defects

    Shang, Quanliang; Patel, Shivani; Danford, David A.; Kutty, Shelby; Steinmetz, Michael; Schuster, Andreas; Beerbaum, Philipp; Sarikouch, Samir

    2018-01-01

    Left ventricular two-dimensional global longitudinal strain (LS) is superior to ejection fraction (EF) as predictor of outcome. We provide reference data for atrial and ventricular global LS during childhood and adolescence by CMR feature tracking (FT). We prospectively enrolled 115 healthy subjects (56 male, mean age 12.4 ± 4.1 years) at a single institution. CMR consisted of standard two-dimensional steady-state free-precession acquisitions. CMR-FT was performed on ventricular horizontal long-axis images for derivation of right and left atrial (RA, LA) and right and left ventricular (RV, LV) peak global LS. End-diastolic volumes (EDVs) and EF were measured. Correlations were explored for LS with age, EDV and EF of each chamber. Mean±SD of LS (%) for RA, RV, LA and LV were 26.56±10.2, -17.96±5.4, 26.45±10.6 and -17.47±5, respectively. There was a positive correlation of LS in LA, LV, RA and RV with corresponding EF (all P<0.05); correlations with age were weak. Gender-wise differences were not significant for atrial and ventricular LS, strain rate and displacement. Inter- and intra-observer comparisons showed moderate agreements. Chamber-specific nomograms for paediatric atrial and ventricular LS are provided to serve as clinical reference, and to facilitate CMR-based deformation research. (orig.)

  3. Linkages Between Hemispheric and Regional Circulation Features Over the Past Millennium Inferred From a Network of Long Tree-Ring Chronologies in the Southwestern USA.

    Ni, F.; Hughes, M. K.; Funkhouser, G.

    2003-12-01

    Features of large-scale atmospheric/oceanic circulation at hemispheric and regional scale affect the many moisture-sensitive, well-replicated millennial length tree-ring records in the Western US that span all the last millennium. This tree-ring variability may be associated with the Pacific Decadal Oscillation (PDO), the Southern Oscillation Index (SOI) and the Southwest Trough Index, all of which directly influence climate variations in the American Southwest. 1000-year atmospheric/oceanic time series based in these associations were then linked with the observed and previously reconstructed summer Palmer Drought Severity Index (PDSI) in western US, providing a decadal to multi-century perspective on climate/circulation variability. Hemispheric and regional climate association tend to be stronger during sudden reversals from dry to wet which were not uncommon throughout the millennium, such as the 1970s PDO reversal which followed the 1950s drought, the 1610s wet interval that followed the 16th century mega drought, and the late 11th, early 12th centuries. Proxy and instrumental data suggest that significant regional anomalous dry (wet) periods over the last millennium (for example in the 1580s and 1950s) coincided with infrequent (frequent) short-wave trough activity. This probably teleconnected with cold sea surface temperature (SST) and high sea level pressure (SLP) over the eastern North Pacific that was enhanced by a greater number of in-phase cold-ENSO and PDO events.

  4. Myocardial deformation assessed by longitudinal strain. Chamber specific normative data for CMR-feature tracking from the German competence network for congenital heart defects

    Shang, Quanliang [University of Nebraska College of Medicine, Children' s Hospital and Medical Center, Division of Pediatric Cardiology, Omaha, NE (United States); Central South University, Department of Radiology, Second Xiangya Hospital, Changsha, Hunan Province (China); Patel, Shivani; Danford, David A.; Kutty, Shelby [University of Nebraska College of Medicine, Children' s Hospital and Medical Center, Division of Pediatric Cardiology, Omaha, NE (United States); Steinmetz, Michael [Georg-August-University and German Centre for Cardiovascular Research (DZHK, Partner Site), Department of Paediatric Cardiology, Goettingen (Germany); Schuster, Andreas [Georg-August-University and German Centre for Cardiovascular Research (DZHK, Partner Site), Department of Cardiology and Pulmonology, Goettingen (Germany); Beerbaum, Philipp; Sarikouch, Samir [Hanover Medical School, Hanover (Germany)

    2018-03-15

    Left ventricular two-dimensional global longitudinal strain (LS) is superior to ejection fraction (EF) as predictor of outcome. We provide reference data for atrial and ventricular global LS during childhood and adolescence by CMR feature tracking (FT). We prospectively enrolled 115 healthy subjects (56 male, mean age 12.4 ± 4.1 years) at a single institution. CMR consisted of standard two-dimensional steady-state free-precession acquisitions. CMR-FT was performed on ventricular horizontal long-axis images for derivation of right and left atrial (RA, LA) and right and left ventricular (RV, LV) peak global LS. End-diastolic volumes (EDVs) and EF were measured. Correlations were explored for LS with age, EDV and EF of each chamber. Mean±SD of LS (%) for RA, RV, LA and LV were 26.56±10.2, -17.96±5.4, 26.45±10.6 and -17.47±5, respectively. There was a positive correlation of LS in LA, LV, RA and RV with corresponding EF (all P<0.05); correlations with age were weak. Gender-wise differences were not significant for atrial and ventricular LS, strain rate and displacement. Inter- and intra-observer comparisons showed moderate agreements. Chamber-specific nomograms for paediatric atrial and ventricular LS are provided to serve as clinical reference, and to facilitate CMR-based deformation research. (orig.)

  5. ETMB-RBF: discrimination of metal-binding sites in electron transporters based on RBF networks with PSSM profiles and significant amino acid pairs.

    Ou, Yu-Yen; Chen, Shu-An; Wu, Sheng-Cheng

    2013-01-01

    Cellular respiration is the process by which cells obtain energy from glucose and is a very important biological process in living cell. As cells do cellular respiration, they need a pathway to store and transport electrons, the electron transport chain. The function of the electron transport chain is to produce a trans-membrane proton electrochemical gradient as a result of oxidation-reduction reactions. In these oxidation-reduction reactions in electron transport chains, metal ions play very important role as electron donor and acceptor. For example, Fe ions are in complex I and complex II, and Cu ions are in complex IV. Therefore, to identify metal-binding sites in electron transporters is an important issue in helping biologists better understand the workings of the electron transport chain. We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and significant amino acid pairs to identify metal-binding residues in electron transport proteins. We have selected a non-redundant set of 55 metal-binding electron transport proteins as our dataset. The proposed method can predict metal-binding sites in electron transport proteins with an average 10-fold cross-validation accuracy of 93.2% and 93.1% for metal-binding cysteine and histidine, respectively. Compared with the general metal-binding predictor from A. Passerini et al., the proposed method can improve over 9% of sensitivity, and 14% specificity on the independent dataset in identifying metal-binding cysteines. The proposed method can also improve almost 76% sensitivity with same specificity in metal-binding histidine, and MCC is also improved from 0.28 to 0.88. We have developed a novel approach based on PSSM profiles and significant amino acid pairs for identifying metal-binding sites from electron transport proteins. The proposed approach achieved a significant improvement with independent test set of metal-binding electron transport proteins.

  6. Uplift rates from a new high-density GPS network in Palmer Land indicate significant late Holocene ice loss in the southwestern Weddell Sea

    Wolstencroft, Martin; King, Matt A.; Whitehouse, Pippa L.; Bentley, Michael J.; Nield, Grace A.; King, Edward C.; McMillan, Malcolm; Shepherd, Andrew; Barletta, Valentina; Bordoni, Andrea; Riva, Riccardo E. M.; Didova, Olga; Gunter, Brian C.

    2015-10-01

    The measurement of ongoing ice-mass loss and associated melt water contribution to sea-level change from regions such as West Antarctica is dependent on a combination of remote sensing methods. A key method, the measurement of changes in Earth's gravity via the GRACE satellite mission, requires a potentially large correction to account for the isostatic response of the solid Earth to ice-load changes since the Last Glacial Maximum. In this study, we combine glacial isostatic adjustment modelling with a new GPS dataset of solid Earth deformation for the southern Antarctic Peninsula to test the current understanding of ice history in this region. A sufficiently complete history of past ice-load change is required for glacial isostatic adjustment models to accurately predict the spatial variation of ongoing solid Earth deformation, once the independently-constrained effects of present-day ice mass loss have been accounted for. Comparisons between the GPS data and glacial isostatic adjustment model predictions reveal a substantial misfit. The misfit is localized on the southwestern Weddell Sea, where current ice models under-predict uplift rates by approximately 2 mm yr-1. This under-prediction suggests that either the retreat of the ice sheet grounding line in this region occurred significantly later in the Holocene than currently assumed, or that the region previously hosted more ice than currently assumed. This finding demonstrates the need for further fieldwork to obtain direct constraints on the timing of Holocene grounding line retreat in the southwestern Weddell Sea and that GRACE estimates of ice sheet mass balance will be unreliable in this region until this is resolved.

  7. Genome Wide Expression Profiling of Cancer Cell Lines Cultured in Microgravity Reveals Significant Dysregulation of Cell Cycle and MicroRNA Gene Networks.

    Prasanna Vidyasekar

    Full Text Available Zero gravity causes several changes in metabolic and functional aspects of the human body and experiments in space flight have demonstrated alterations in cancer growth and progression. This study reports the genome wide expression profiling of a colorectal cancer cell line-DLD-1, and a lymphoblast leukemic cell line-MOLT-4, under simulated microgravity in an effort to understand central processes and cellular functions that are dysregulated among both cell lines. Altered cell morphology, reduced cell viability and an aberrant cell cycle profile in comparison to their static controls were observed in both cell lines under microgravity. The process of cell cycle in DLD-1 cells was markedly affected with reduced viability, reduced colony forming ability, an apoptotic population and dysregulation of cell cycle genes, oncogenes, and cancer progression and prognostic markers. DNA microarray analysis revealed 1801 (upregulated and 2542 (downregulated genes (>2 fold in DLD-1 cultures under microgravity while MOLT-4 cultures differentially expressed 349 (upregulated and 444 (downregulated genes (>2 fold under microgravity. The loss in cell proliferative capacity was corroborated with the downregulation of the cell cycle process as demonstrated by functional clustering of DNA microarray data using gene ontology terms. The genome wide expression profile also showed significant dysregulation of post transcriptional gene silencing machinery and multiple microRNA host genes that are potential tumor suppressors and proto-oncogenes including MIR22HG, MIR17HG and MIR21HG. The MIR22HG, a tumor-suppressor gene was one of the highest upregulated genes in the microarray data showing a 4.4 log fold upregulation under microgravity. Real time PCR validated the dysregulation in the host gene by demonstrating a 4.18 log fold upregulation of the miR-22 microRNA. Microarray data also showed dysregulation of direct targets of miR-22, SP1, CDK6 and CCNA2.

  8. Interconnected networks

    2016-01-01

    This volume provides an introduction to and overview of the emerging field of interconnected networks which include multi layer or multiplex networks, as well as networks of networks. Such networks present structural and dynamical features quite different from those observed in isolated networks. The presence of links between different networks or layers of a network typically alters the way such interconnected networks behave – understanding the role of interconnecting links is therefore a crucial step towards a more accurate description of real-world systems. While examples of such dissimilar properties are becoming more abundant – for example regarding diffusion, robustness and competition – the root of such differences remains to be elucidated. Each chapter in this topical collection is self-contained and can be read on its own, thus making it also suitable as reference for experienced researchers wishing to focus on a particular topic.

  9. Feature Extraction

    CERN. Geneva

    2015-01-01

    Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

  10. Solar Features

    National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes a variety of solar feature datasets contributed by a number of national and private solar observatories located worldwide.

  11. Site Features

    U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

  12. Research on NGN network control technology

    Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

    2004-04-01

    Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

  13. Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects

    It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...

  14. A π-π 3D network of tetranuclear μ2/μ3-carbonato Dy(III) bis-pyrazolylpyridine clusters showing single molecule magnetism features.

    Gass, Ian A; Moubaraki, Boujemaa; Langley, Stuart K; Batten, Stuart R; Murray, Keith S

    2012-02-18

    2,6-Di(pyrazole-3-yl)pyridine, 3-bpp, forms a porous (4(9)·6(6)) π-π mediated 3D network of trigonal pyramidal [Dy(III)(4)] carbonato-bridged complexes, with hexagonal channels comprising 54% of the unit cell volume, the material displaying slow magnetisation reversal. This journal is © The Royal Society of Chemistry 2012

  15. Accurately Identifying New QoS Violation Driven by High-Distributed Low-Rate Denial of Service Attacks Based on Multiple Observed Features

    Jian Kang

    2015-01-01

    Full Text Available We propose using multiple observed features of network traffic to identify new high-distributed low-rate quality of services (QoS violation so that detection accuracy may be further improved. For the multiple observed features, we choose F feature in TCP packet header as a microscopic feature and, P feature and D feature of network traffic as macroscopic features. Based on these features, we establish multistream fused hidden Markov model (MF-HMM to detect stealthy low-rate denial of service (LDoS attacks hidden in legitimate network background traffic. In addition, the threshold value is dynamically adjusted by using Kaufman algorithm. Our experiments show that the additive effect of combining multiple features effectively reduces the false-positive rate. The average detection rate of MF-HMM results in a significant 23.39% and 44.64% improvement over typical power spectrum density (PSD algorithm and nonparametric cumulative sum (CUSUM algorithm.

  16. The significance of small streams

    Wohl, Ellen

    2017-09-01

    Headwaters, defined here as first- and secondorder streams, make up 70%‒80% of the total channel length of river networks. These small streams exert a critical influence on downstream portions of the river network by: retaining or transmitting sediment and nutrients; providing habitat and refuge for diverse aquatic and riparian organisms; creating migration corridors; and governing connectivity at the watershed-scale. The upstream-most extent of the channel network and the longitudinal continuity and lateral extent of headwaters can be difficult to delineate, however, and people are less likely to recognize the importance of headwaters relative to other portions of a river network. Consequently, headwaters commonly lack the legal protections accorded to other portions of a river network and are more likely to be significantly altered or completely obliterated by land use.

  17. New Approach of Feature Extraction Method Based on the Raw Form and his Skeleton for Gujarati Handwritten Digits using Neural Networks Classifier

    K. Moro

    2014-12-01

    Full Text Available This paper presents an optical character recognition (OCR system for Gujarati handwritten digits. One may find so much of work for latin writing, arabic, chines, etc. but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work we have proposed a method of feature extraction based on the raw form of the character and his skeleton and we have shown the advantage of using this method over other approaches mentioned in this article.

  18. Input significance analysis: feature ranking through synaptic weights ...

    a selected dataset taken from the UCI Machine Learning Repository and in an online environment and lastly to attest the FR results by using another selected dataset taken from the same source and in the same environment. There are three groups of experiments conducted to accomplish these goals. The results are ...

  19. Adverse Outcome Pathway Networks II: Network Analytics.

    Villeneuve, Daniel L; Angrish, Michelle M; Fortin, Marie C; Katsiadaki, Ioanna; Leonard, Marc; Margiotta-Casaluci, Luigi; Munn, Sharon; O'Brien, Jason M; Pollesch, Nathan L; Smith, L Cody; Zhang, Xiaowei; Knapen, Dries

    2018-02-28

    Toxicological responses to stressors are more complex than the simple one biological perturbation to one adverse outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present paper introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using two example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses, or previously undefined emergent patterns of response, are introduced. Along with a companion article (Knapen et al. part I), these concepts set the stage for development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. Collectively, this work addresses one of the major themes identified through a SETAC Horizon Scanning effort focused on advancing the AOP framework. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  20. Network motif frequency vectors reveal evolving metabolic network organisation.

    Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

    2015-01-01

    At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

  1. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

    Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

    2009-07-01

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

  2. Study on Magneto-Hydro-Dynamics Disturbance Signal Feature Classification Using Improved S-Transform Algorithm and Radial Basis Function Neural Network

    Nan YU

    2014-09-01

    Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.

  3. Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

    Lazri, Mourad; Ameur, Soltane

    2018-05-01

    A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

  4. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.)

    Samecka-Cymerman, A., E-mail: sameckaa@biol.uni.wroc.p [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Stankiewicz, A.; Kolon, K. [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Kempers, A.J. [Department of Environmental Sciences, Radboud University of Nijmegen, Toernooiveld, 6525 ED Nijmegen (Netherlands)

    2009-07-15

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Olesnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wroclaw to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. - Once trained, SOFM could be used in the future to recognize types of pollution.

  5. Multiscale deep features learning for land-use scene recognition

    Yuan, Baohua; Li, Shijin; Li, Ning

    2018-01-01

    The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

  6. Featuring animacy

    Elizabeth Ritter

    2015-01-01

    Full Text Available Algonquian languages are famous for their animacy-based grammatical properties—an animacy based noun classification system and direct/inverse system which gives rise to animacy hierarchy effects in the determination of verb agreement. In this paper I provide new evidence for the proposal that the distinctive properties of these languages is due to the use of participant-based features, rather than spatio-temporal ones, for both nominal and verbal functional categories (Ritter & Wiltschko 2009, 2014. Building on Wiltschko (2012, I develop a formal treatment of the Blackfoot aspectual system that assumes a category Inner Aspect (cf. MacDonald 2008, Travis 1991, 2010. Focusing on lexical aspect in Blackfoot, I demonstrate that the classification of both nouns (Seinsarten and verbs (Aktionsarten is based on animacy, rather than boundedness, resulting in a strikingly different aspectual system for both categories. 

  7. Impact of PON deployment on metro networks

    Poirrier, Julien; Herviou, Fabrice; Barboule, Hélène; Moignard, Maryse

    2009-01-01

    FTTH or FTTC, depending on countries and areas, will be the key technology for operators to differentiate themselves from competitors and win market share. Such a disruptive evolution of the access network should be supported by a significant re-design of the higher network layers. In the present paper, the required features of these new WDM networks are presented. Capacity and cost are the two obvious drivers. But versatility will be crucial to cope with an uncertain context (tedious prediction of traffic, regulation and services) and with very diverse population densities. Finally we also address how PON could benefit from mature WDM technologies to ease the global network design.

  8. A link prediction method for heterogeneous networks based on BP neural network

    Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

    2018-04-01

    Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

  9. Genome-Scale Co-Expression Network Comparison across Escherichia coli and Salmonella enterica Serovar Typhimurium Reveals Significant Conservation at the Regulon Level of Local Regulators Despite Their Dissimilar Lifestyles

    Zarrineh, Peyman; Sánchez-Rodríguez, Aminael; Hosseinkhan, Nazanin; Narimani, Zahra; Marchal, Kathleen; Masoudi-Nejad, Ali

    2014-01-01

    Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica. PMID:25101984

  10. Neural network tracking and extension of positive tracking periods

    Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

    2004-04-01

    Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

  11. Network Ecology and Adolescent Social Structure.

    McFarland, Daniel A; Moody, James; Diehl, David; Smith, Jeffrey A; Thomas, Reuben J

    2014-12-01

    Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

  12. FEATURES ROAD SAFETY AUDIT

    L. Abramova

    2015-07-01

    Full Text Available Development of the road network, increasing motorization of the population significantly increase the risk of accidents. Experts in the field of traffic are developing methods to reduce the probability of accidents. The ways of solving the problems of road safety audit at various stages of the «life» of roads are considered.

  13. SOCIAL NETWORK EFFECTS ON ROMANTIC RELATIONSHIP

    Fatma CAN

    2015-06-01

    Full Text Available The main objective of the study was to obtain information about social network variables in order to predict the relational commitment of married individuals and people having dating relationships. For this purpose, social network analysis has been carried out on 134 people having dating relationship and 154 married individuals and then Relationship Stability Scale, Subjective Norm Scale and Social Network Feature Survey prepared by the researcher were used. The results indicated that the approval of the closest social network member and the level of enjoyment of each other’s social network members had the best predictive value for relationship satisfaction and the investment to the relationship. The results also demonstrated that, approval of the social network had a negative impact on the level of the quality of alternatives and it showed that social networks were seen as a barrier function to have alternative relationships. Furthermore, by dividing social network members into two groups, for the dating group, the approval of the social network was the most significant variable for commitment but in the married group, the need for social network approval was not an important criteria because of having their relatioship already confirmed legally. When social network members were categorised and examined, the closest social network members did not differ by sex, but were varied in terms of relationship types. In the flirt group, one of their friends among his/her social network and their partners’ social network was specified as the closest social network member whereas in the married group, the closest social network member among his/her social network was their mother while it was their sibling among partner’s social network.

  14. Modeling the citation network by network cosmology.

    Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

    2015-01-01

    Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

  15. Linear network theory

    Sander, K F

    1964-01-01

    Linear Network Theory covers the significant algebraic aspect of network theory, with minimal reference to practical circuits. The book begins the presentation of network analysis with the exposition of networks containing resistances only, and follows it up with a discussion of networks involving inductance and capacity by way of the differential equations. Classification and description of certain networks, equivalent networks, filter circuits, and network functions are also covered. Electrical engineers, technicians, electronics engineers, electricians, and students learning the intricacies

  16. Functional abilities and cognitive decline in adult and aging intellectual disabilities. Psychometric validation of an Italian version of the Alzheimer's Functional Assessment Tool (AFAST): analysis of its clinical significance with linear statistics and artificial neural networks.

    De Vreese, L P; Gomiero, T; Uberti, M; De Bastiani, E; Weger, E; Mantesso, U; Marangoni, A

    2015-04-01

    (a) A psychometric validation of an Italian version of the Alzheimer's Functional Assessment Tool scale (AFAST-I), designed for informant-based assessment of the degree of impairment and of assistance required in seven basic daily activities in adult/elderly people with intellectual disabilities (ID) and (suspected) dementia; (b) a pilot analysis of its clinical significance with traditional statistical procedures and with an artificial neural network. AFAST-I was administered to the professional caregivers of 61 adults/seniors with ID with a mean age (± SD) of 53.4 (± 7.7) years (36% with Down syndrome). Internal consistency (Cronbach's α coefficient), inter/intra-rater reliabilities (intra-class coefficients, ICC) and concurrent, convergent and discriminant validity (Pearson's r coefficients) were computed. Clinical significance was probed by analysing the relationships among AFAST-I scores and the Sum of Cognitive Scores (SCS) and the Sum of Social Scores (SOS) of the Dementia Questionnaire for Persons with Intellectual Disabilities (DMR-I) after standardisation of their raw scores in equivalent scores (ES). An adaptive artificial system (AutoContractive Maps, AutoCM) was applied to all the variables recorded in the study sample, aimed at uncovering which variable occupies a central position and supports the entire network made up of the remaining variables interconnected among themselves with different weights. AFAST-I shows a high level of internal homogeneity with a Cronbach's α coefficient of 0.92. Inter-rater and intra-rater reliabilities were also excellent with ICC correlations of 0.96 and 0.93, respectively. The results of the analyses of the different AFAST-I validities all go in the expected direction: concurrent validity (r=-0.87 with ADL); convergent validity (r=0.63 with SCS; r=0.61 with SOS); discriminant validity (r=0.21 with the frequency of occurrence of dementia-related Behavioral Excesses of the Assessment for Adults with Developmental

  17. Coding visual features extracted from video sequences.

    Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

    2014-05-01

    Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

  18. Multimodal Feature Learning for Video Captioning

    Sujin Lee

    2018-01-01

    Full Text Available Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD and Microsoft Research Video-to-Text (MSR-VTT, demonstrate the performance of the proposed model.

  19. Method for assessing reliability of a network considering probabilistic safety assessment

    Cepin, M.

    2005-01-01

    A method for assessment of reliability of the network is developed, which uses the features of the fault tree analysis. The method is developed in a way that the increase of the network under consideration does not require significant increase of the model. The method is applied to small examples of network consisting of a small number of nodes and a small number of their connections. The results give the network reliability. They identify equipment, which is to be carefully maintained in order that the network reliability is not reduced, and equipment, which is a candidate for redundancy, as this would improve network reliability significantly. (author)

  20. Improving the Robustness of Deep Neural Networks via Stability Training

    Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

    2016-01-01

    In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

  1. Engineering features of ISX

    Lousteau, D.C.; Jernigan, T.C.; Schaffer, M.J.; Hussung, R.O.

    1975-01-01

    ISX, an Impurity Study Experiment, is presently being designed at Oak Ridge National Laboratory as a joint scientific effort between ORNL and General Atomic Company. ISX is a moderate size tokamak dedicated to the study of impurity production, diffusion, and control. The significant engineering features of this device are discussed

  2. Artificial neural network based approach to transmission lines protection

    Joorabian, M.

    1999-05-01

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  3. Introduction to computer networking

    Robertazzi, Thomas G

    2017-01-01

    This book gives a broad look at both fundamental networking technology and new areas that support it and use it. It is a concise introduction to the most prominent, recent technological topics in computer networking. Topics include network technology such as wired and wireless networks, enabling technologies such as data centers, software defined networking, cloud and grid computing and applications such as networks on chips, space networking and network security. The accessible writing style and non-mathematical treatment makes this a useful book for the student, network and communications engineer, computer scientist and IT professional. • Features a concise, accessible treatment of computer networking, focusing on new technological topics; • Provides non-mathematical introduction to networks in their most common forms today;< • Includes new developments in switching, optical networks, WiFi, Bluetooth, LTE, 5G, and quantum cryptography.

  4. Method and system for mesh network embedded devices

    Wang, Ray (Inventor)

    2009-01-01

    A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

  5. Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

    Dilger, Samantha K N; Uthoff, Johanna; Judisch, Alexandra; Hammond, Emily; Mott, Sarah L; Smith, Brian J; Newell, John D; Hoffman, Eric A; Sieren, Jessica C

    2015-10-01

    Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore this hypothesis, we have developed expanded quantitative CT feature extraction techniques, including volumetric Laws texture energy measures for the parenchyma and nodule, border descriptors using ray-casting and rubber-band straightening, histogram features characterizing densities, and global lung measurements. Using stepwise forward selection and leave-one-case-out cross-validation, a neural network was used for classification. When applied to 50 nodules (22 malignant and 28 benign) from high-resolution CT scans, 52 features (8 nodule, 39 parenchymal, and 5 global) were statistically significant. Nodule-only features yielded an area under the ROC curve of 0.918 (including nodule size) and 0.872 (excluding nodule size). Performance was improved through inclusion of parenchymal (0.938) and global features (0.932). These results show a trend toward increased performance when the parenchyma is included, coupled with the large number of significant parenchymal features that support our hypothesis: the pulmonary parenchyma is influenced differentially by malignant versus benign nodules, assisting CAD-based nodule characterizations.

  6. Noise-enhanced categorization in a recurrently reconnected neural network

    Monterola, Christopher; Zapotocky, Martin

    2005-01-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails

  7. Noise-enhanced categorization in a recurrently reconnected neural network

    Monterola, Christopher; Zapotocky, Martin

    2005-03-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.

  8. Facial expression recognition based on improved deep belief networks

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

  9. features using RBF-SA

    Rafael do Espírito Santo

    2006-01-01

    Full Text Available We present in this work a new type of classes discriminator based upon nonlinear and combinational optimization techniques: radial basis functions-simulated annealing (RBF-SA. The combinational optimization method is used here as a preestimation of some parameters of the network classifier. We compare the classifier performance with and without pre-estimation. For training the classifiers, adopting the leave-one-out procedure, we have used case examples such as mammographic masses (malignant and benign. The classifier is trained with shape factors and edge-sharpness measures extracted from 57 regions of interest (ROI (37 malignant and 20 benign, manually delineated, that describe mammographic masses and tumor features in terms of polygonal models for shape factors (compactness [CC], Fourier description [FF], fractional concavity [FCC] and speculated index [SI] and edge sharpness-acutance (A . The classifier performance is compared in terms of the area under the receive operating characteristic (ROC curve – (A. Higher values of A correspond to a better performance of classifier. Experiments with mammographic tumor and masses show that the best result of 0.9776 is obtained with RBF-SA when RBF parameters such as centers and spread matrix are pre-estimated, which is significantly better than the results obtained with no pre-estimation or only pre-estimation of the RBF centers, which are, 0.7071 and 0.9552 respectively.

  10. Online feature selection with streaming features.

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  11. Fitness networks for real world systems via modified preferential attachment

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

    2017-05-01

    Complex networks are virtually ubiquitous, and the Barabási and Albert model (BA model) has became an acknowledged standard for the modelling of these systems. The so-called BA model is a kind of preferential attachment growth model based on the intuitive premise that popularity is attractive. However, preferential attachment alone is insufficient to describe the diversity of complex networks observed in the real world. In this paper we first use the accuracy of a link prediction method, as a metric for network fitness. The link prediction method predicts the occurrence of links consistent with preferential attachment, the performance of this link prediction scheme is then a natural measure of the ;preferential-attachment-likeness; of a given network. We then propose several modification methods and modified BA models to construct networks which more accurately describe the fitness properties of real networks. We find that all features assortativity, degree distribution and rich-club formation can play significant roles for the network construction and eventual structure. Moreover, link sparsity and the size of a network are key factors for network reconstruction. In addition, we find that the structure of the network which is limited by geographic location (nodes are embedded in a Euclidean space and connectivity is correlated with distances) differs from other typical networks. In social networks, we observe that the high school contact network has similar structure as the friends network and so we speculate that the contact behaviours can reflect real friendships.

  12. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Min-Joo Kang

    Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

  13. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

  14. Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

    Ly, Cheng

    2015-12-01

    Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.

  15. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    Soufan, Othman

    2012-09-01

    Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

  16. Video Scene Parsing with Predictive Feature Learning

    Jin, Xiaojie; Li, Xin; Xiao, Huaxin; Shen, Xiaohui; Lin, Zhe; Yang, Jimei; Chen, Yunpeng; Dong, Jian; Liu, Luoqi; Jie, Zequn; Feng, Jiashi; Yan, Shuicheng

    2016-01-01

    In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \\textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to ...

  17. Persistent homology of complex networks

    Horak, Danijela; Maletić, Slobodan; Rajković, Milan

    2009-01-01

    Long-lived topological features are distinguished from short-lived ones (considered as topological noise) in simplicial complexes constructed from complex networks. A new topological invariant, persistent homology, is determined and presented as a parameterized version of a Betti number. Complex networks with distinct degree distributions exhibit distinct persistent topological features. Persistent topological attributes, shown to be related to the robust quality of networks, also reflect the deficiency in certain connectivity properties of networks. Random networks, networks with exponential connectivity distribution and scale-free networks were considered for homological persistency analysis

  18. Infrared image enhancement with learned features

    Fan, Zunlin; Bi, Duyan; Ding, Wenshan

    2017-11-01

    Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

  19. Sparsity in Model Gene Regulatory Networks

    Zagorski, M.

    2011-01-01

    We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

  20. Environment Aware Cellular Networks

    Ghazzai, Hakim

    2015-02-01

    The unprecedented rise of mobile user demand over the years have led to an enormous growth of the energy consumption of wireless networks as well as the greenhouse gas emissions which are estimated currently to be around 70 million tons per year. This significant growth of energy consumption impels network companies to pay huge bills which represent around half of their operating expenditures. Therefore, many service providers, including mobile operators, are looking for new and modern green solutions to help reduce their expenses as well as the level of their CO2 emissions. Base stations are the most power greedy element in cellular networks: they drain around 80% of the total network energy consumption even during low traffic periods. Thus, there is a growing need to develop more energy-efficient techniques to enhance the green performance of future 4G/5G cellular networks. Due to the problem of traffic load fluctuations in cellular networks during different periods of the day and between different areas (shopping or business districts and residential areas), the base station sleeping strategy has been one of the main popular research topics in green communications. In this presentation, we present several practical green techniques that provide significant gains for mobile operators. Indeed, combined with the base station sleeping strategy, these techniques achieve not only a minimization of the fossil fuel consumption but also an enhancement of mobile operator profits. We start with an optimized cell planning method that considers varying spatial and temporal user densities. We then use the optimal transport theory in order to define the cell boundaries such that the network total transmit power is reduced. Afterwards, we exploit the features of the modern electrical grid, the smart grid, as a new tool of power management for cellular networks and we optimize the energy procurement from multiple energy retailers characterized by different prices and pollutant

  1. Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells.

    Hongbo Shi

    Full Text Available Myocardial infarction (MI is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA-TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA-TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1 was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

  2. Scalable Lunar Surface Networks and Adaptive Orbit Access

    Wang, Xudong

    2015-01-01

    Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.

  3. Green mobile networks a networking perspective

    Ansari, Nirwan

    2016-01-01

    Combines the hot topics of energy efficiency and next generation mobile networking, examining techniques and solutions. Green communications is a very hot topic. Ever increasing mobile network bandwidth rates significantly impacts on operating costs due to aggregate network energy consumption. As such, design on 4G networks and beyond has increasingly started to focus on 'energy efficiency' or so-called 'green' networks. Many techniques and solutions have been proposed to enhance the energy efficiency of mobile networks, yet no book has provided an in-depth analysis of the energy consumption issues in mobile networks nor offers detailed theories, tools and solutions for solving the energy efficiency problems.

  4. Analysis on the Content, Features and Formation of Motivation on Strategic Network%企业战略网络内涵、特征及形成动因分析

    刘宇; 张敬文; 阮平南

    2011-01-01

    在梳理战略网络理论相关研究的基础上,对战略网络内涵和特征进行重新界定,并对战略网络形成动因进行深入研究.%Based on the research of strategic network theory, this paper re - defines the content and characteristics of strategic network. At last, it studies the causes for the formation of strategic network.

  5. Networks of networks – An introduction

    Kenett, Dror Y.; Perc, Matjaž; Boccaletti, Stefano

    2015-01-01

    Graphical abstract: Interdependent network reciprocity. Only those blue cooperative domains that are initially present on both networks survive. Abstract: This is an introduction to the special issue titled “Networks of networks” that is in the making at Chaos, Solitons & Fractals. Recent research and reviews attest to the fact that networks of networks are the next frontier in network science [1–7]. Not only are interactions limited and thus inadequately described by well-mixed models, it is also a fact that the networks that should be an integral part of such models are often interconnected, thus making the processes that are unfolding on them interdependent. From the World economy and transportation systems to social media, it is clear that processes taking place in one network might significantly affect what is happening in many other networks. Within an interdependent system, each type of interaction has a certain relevance and meaning, so that treating all the links identically inevitably leads to information loss. Networks of networks, interdependent networks, or multilayer networks are therefore a much better and realistic description of such systems, and this Special Issue is devoted to their structure, dynamics and evolution, as well as to the study of emergent properties in multi-layered systems in general. Topics of interest include but are not limited to the spread of epidemics and information, percolation, diffusion, synchronization, collective behavior, and evolutionary games on networks of networks. Interdisciplinary work on all aspects of networks of networks, regardless of background and motivation, is very welcome.

  6. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

    Meng Fan-Bo

    2016-01-01

    Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

  7. Reconfigurable optical implementation of quantum complex networks

    Nokkala, J.; Arzani, F.; Galve, F.; Zambrini, R.; Maniscalco, S.; Piilo, J.; Treps, N.; Parigi, V.

    2018-05-01

    Network theory has played a dominant role in understanding the structure of complex systems and their dynamics. Recently, quantum complex networks, i.e. collections of quantum systems arranged in a non-regular topology, have been theoretically explored leading to significant progress in a multitude of diverse contexts including, e.g., quantum transport, open quantum systems, quantum communication, extreme violation of local realism, and quantum gravity theories. Despite important progress in several quantum platforms, the implementation of complex networks with arbitrary topology in quantum experiments is still a demanding task, especially if we require both a significant size of the network and the capability of generating arbitrary topology—from regular to any kind of non-trivial structure—in a single setup. Here we propose an all optical and reconfigurable implementation of quantum complex networks. The experimental proposal is based on optical frequency combs, parametric processes, pulse shaping and multimode measurements allowing the arbitrary control of the number of the nodes (optical modes) and topology of the links (interactions between the modes) within the network. Moreover, we also show how to simulate quantum dynamics within the network combined with the ability to address its individual nodes. To demonstrate the versatility of these features, we discuss the implementation of two recently proposed probing techniques for quantum complex networks and structured environments.

  8. Multivariate correlation analysis technique based on euclidean distance map for network traffic characterization

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Qing, Sihan; Susilo, Willy; Wang, Guilin; Liu, Dongmei

    2011-01-01

    The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches

  9. Spinoff 2001: Special Millennium Feature

    2001-01-01

    For the past 43 years, NASA has devoted its facilities, labor force, and expertise to sharing the abundance of technology developments used for its missions with the nation's industries. These countless technologies have not only successfully contributed to the growth of the U.S. economy, but also to the quality of life on Earth. For the past 25 years, NASA's Spinoff publication has brought attention to thousands of technologies, products, and services that were developed as a direct result of commercial partnerships between NASA and the private business sector. Many of these exciting technologies included advances in ceramics, computer technology, fiber optics, and remote sensing. New and ongoing research at the NASA field centers covers a full spectrum of technologies that will provide numerous advantages for the future, many of which have made significant strides in the commercial market. The NASA Commercial Technology Network plays a large role in transferring this progress. By applying NASA technologies such as data communication, aircraft de-icing technologies, and innovative materials to everyday functions, American consumers and the national economy benefit. Moving forward into the new millennium, these new technologies will further advance our country's position as the world leader in scientific and technical innovation. These cutting-edge innovations represent the investment of the U.S. citizen in the Space Program. Some of these technologies are highlighted in Spinoff 2001, an example of NASA's commitment to technology transfer and commercialization assistance. This year's issue spotlights the commercial technology efforts of NASA's John F. Kennedy Space Center. Kennedy's extensive network of commercial technology opportunities has enabled them to become a leader in technology transfer outreach. This kind of leadership is exemplified through Kennedy's recent partnership with the State of Florida, working toward the development of the Space Experiment

  10. Detecting Novelty and Significance

    Ferrari, Vera; Bradley, Margaret M.; Codispoti, Maurizio; Lang, Peter J.

    2013-01-01

    Studies of cognition often use an “oddball” paradigm to study effects of stimulus novelty and significance on information processing. However, an oddball tends to be perceptually more novel than the standard, repeated stimulus as well as more relevant to the ongoing task, making it difficult to disentangle effects due to perceptual novelty and stimulus significance. In the current study, effects of perceptual novelty and significance on ERPs were assessed in a passive viewing context by presenting repeated and novel pictures (natural scenes) that either signaled significant information regarding the current context or not. A fronto-central N2 component was primarily affected by perceptual novelty, whereas a centro-parietal P3 component was modulated by both stimulus significance and novelty. The data support an interpretation that the N2 reflects perceptual fluency and is attenuated when a current stimulus matches an active memory representation and that the amplitude of the P3 reflects stimulus meaning and significance. PMID:19400680

  11. Significant NRC Enforcement Actions

    Nuclear Regulatory Commission — This dataset provides a list of Nuclear Regulartory Commission (NRC) issued significant enforcement actions. These actions, referred to as "escalated", are issued by...

  12. Effective Feature Preprocessing for Time Series Forecasting

    Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

    2006-01-01

    Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

  13. Heuristic urban transportation network design method, a multilayer coevolution approach

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

    2017-08-01

    The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

  14. Multiresolution wavelet-ANN model for significant wave height forecasting.

    Deka, P.C.; Mandal, S.; Prahlada, R.

    Hybrid wavelet artificial neural network (WLNN) has been applied in the present study to forecast significant wave heights (Hs). Here Discrete Wavelet Transformation is used to preprocess the time series data (Hs) prior to Artificial Neural Network...

  15. Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

    Ma, Xiaoke; Sun, Penggang; Wang, Yu

    2018-04-01

    Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

  16. Integration of a network aware traffic generation device into a computer network emulation platform

    Von Solms, S

    2014-07-01

    Full Text Available Flexible, open source network emulation tools can provide network researchers with significant benefits regarding network behaviour and performance. The evaluation of these networks can benefit greatly from the integration of realistic, network...

  17. Bayesian Networks An Introduction

    Koski, Timo

    2009-01-01

    Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni

  18. Unsupervised Feature Subset Selection

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  19. Individual Search and Social Networks

    Sanjeev Goyal; Stephanie Rosenkranz; Utz Weitzel; Vincent Buskens

    2014-01-01

    The explosion in online social networks motivates an enquiry into their structure and their welfare effects. A central feature of these networks is information sharing: online social networks lower the cost of getting information from others. These lower costs affect the attractiveness of individual search vis-a-vis a reliance on social networks. The paper reports the findings of an experiment on these effects. Our experiment shows that online networks can have large effects. Information acqu...

  20. Internet marketing global features

    Rakita Branko

    2005-01-01

    Full Text Available Business environment incessantly bringing a yard of a new challenges to market entities. One of the greatest challenges that companies faced during the last couple of decades was development of information systems as well as a large scale usage of world's greatest computer network - Internet - no matter how big they were or what their activities included. There are many papers about Internet, its development, social and economic significance. However, there is no paper or article written by a local author that systematically and thoroughly treats marketing importance of Internet. This paper presents potentials and business importance of internet marketing in new millennium. It considers internet as a communication, trade and distribution channel. In addition, the paper highlights research potentials of Internet.

  1. No-reference image quality assessment based on statistics of convolution feature maps

    Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

    2018-04-01

    We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

  2. Features of MCNP6

    Goorley, T.; James, M.; Booth, T.; Brown, F.; Bull, J.; Cox, L.J.; Durkee, J.; Elson, J.; Fensin, M.; Forster, R.A.; Hendricks, J.; Hughes, H.G.; Johns, R.; Kiedrowski, B.; Martz, R.; Mashnik, S.; McKinney, G.; Pelowitz, D.; Prael, R.; Sweezy, J.

    2016-01-01

    Highlights: • MCNP6 is simply and accurately described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of these two computer codes. • MCNP6 is the result of six years of effort by the MCNP5 and MCNPX code development teams. • These groups of people, residing in Los Alamos National Laboratory’s X Computational Physics Division, Monte Carlo Codes Group (XCP-3) and Nuclear Engineering and Nonproliferation Division, Radiation Transport Modeling Team (NEN-5) respectively, have combined their code development efforts to produce the next evolution of MCNP. • While maintenance and major bug fixes will continue for MCNP5 1.60 and MCNPX 2.7.0 for upcoming years, new code development capabilities only will be developed and released in MCNP6. • In fact, the initial release of MCNP6 contains numerous new features not previously found in either code. • These new features are summarized in this document. • Packaged with MCNP6 is also the new production release of the ENDF/B-VII.1 nuclear data files usable by MCNP. • The high quality of the overall merged code, usefulness of these new features, along with the desire in the user community to start using the merged code, have led us to make the first MCNP6 production release: MCNP6 version 1. • High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, our automated nightly software debugger tests, the underlying high quality nuclear and atomic databases, and significant testing by many beta testers. - Abstract: MCNP6 can be described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of these two computer codes. MCNP6 is the result of six years of effort by the MCNP5 and MCNPX code development teams. These groups of people, residing in Los Alamos National Laboratory’s X Computational Physics Division, Monte Carlo Codes Group (XCP-3) and Nuclear Engineering and

  3. Unique Features of Mobile Commerce

    DING Xiaojun; IIJIMA Junichi; HO Sho

    2004-01-01

    While the market potentials and impacts of web-based e-commerce are still in the ascendant, the advances in wireless technologies and mobile networks have brought about a new business opportunity and research attention, what is termed mobile commerce. Commonly, mobile commerce is considered to be another new application of existing web-based e-commerce onto wireless networks, but as an independent business area, mobile commerce has its own advantages and challenges as opposed to traditional e-commerce applications. This paper focuses on exploring the unique features of mobile commerce as. Compared with traditional e-commerce. Also, there are still some limitations arisen in m-commerce in contrast to web-based e-commerce. Finally, current state of mobile commerce in Japan is presented in brief, with an introduction of several cases involving mobile commerce applications in today 's marketplace.

  4. Network Transformations in Economy

    Bolychev O.

    2014-09-01

    Full Text Available In the context of ever-increasing market competition, networked interactions play a special role in the economy. The network form of entrepreneurship is increasingly viewed as an effective organizational structure to create a market value embedded in innovative business solutions. The authors study the characteristics of a network as an economic category and emphasize certain similarities between Rus sian and international approaches to identifying interactions of economic systems based on the network principle. The paper focuses on the types of networks widely used in the economy. The authors analyze the transformation of business networks along two lines: from an intra- to an inter-firm network and from an inter-firm to an inter-organizational network. The possible forms of network formation are described depending on the strength of connections and the type of integration. The drivers and reasons behind process of transition from a hierarchical model of the organizational structure to a network type are identified. The authors analyze the advantages of creating inter-firm networks and discuss the features of inter-organizational networks as compares to inter-firm ones. The article summarizes the reasons for and advantages of participation in inter-rganizational networks and identifies the main barriers to the formation of inter-organizational network.

  5. Untangle network security

    El-Bawab, Abd El-Monem A

    2014-01-01

    If you are a security engineer or a system administrator and want to secure your server infrastructure with the feature-rich Untangle, this book is for you. For individuals who want to start their career in the network security field, this book would serve as a perfect companion to learn the basics of network security and how to implement it using Untangle NGFW.

  6. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

    Yang Yu

    2017-01-01

    Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.

  7. Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).

    Cascianelli, S; Tranfaglia, C; Fravolini, M L; Bianconi, F; Minestrini, M; Nuvoli, S; Tambasco, N; Dottorini, M E; Palumbo, B

    2017-01-01

    The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. 123 I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using 123 I-FP-CIT brain SPET data, a classification tree (CIT), was applied. CIT is an automatic classifier composed of a set of logical rules, organized as a decision tree to produce an optimised threshold based classification of data to provide discriminative cut-off values. We applied a CIT to 123 I-FP-CIT brain SPET semiquantitave data, to obtain cut-off values of radiopharmaceutical uptake ratios in caudate nuclei and putamina with the aim to diagnose PD versus other conditions. We retrospectively investigated 187 patients undergoing 123 I-FP-CIT brain SPET (Millenium VG, G.E.M.S.) with semiquantitative analysis performed with Basal Ganglia (BasGan) V2 software according to EANM guidelines; among them 113 resulted affected by PD (PD group) and 74 (N group) by other non parkinsonian conditions, such as Essential Tremor and drug-induced PD. PD group included 113 subjects (60M and 53F of age: 60-81yrs) having Hoehn and Yahr score (HY): 0.5-1.5; Unified Parkinson Disease Rating Scale (UPDRS) score: 6-38; N group included 74 subjects (36M and 38 F range of age 60-80 yrs). All subjects were clinically followed for at least 6-18 months to confirm the diagnosis. To examinate data obtained by using CIT, for each of the 1,000 experiments carried out, 10% of patients were randomly selected as the CIT training set, while the remaining 90% validated the trained CIT, and the percentage of the validation data correctly classified in the two groups of patients was computed. The expected performance of an "average

  8. Fusion of shallow and deep features for classification of high-resolution remote sensing images

    Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

    2018-02-01

    Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

  9. Classification of interstitial lung disease patterns with topological texture features

    Huber, Markus B.; Nagarajan, Mahesh; Leinsinger, Gerda; Ray, Lawrence A.; Wismüller, Axel

    2010-03-01

    Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

  10. Person Re-Identification by Camera Correlation Aware Feature Augmentation.

    Chen, Ying-Cong; Zhu, Xiatian; Zheng, Wei-Shi; Lai, Jian-Huang

    2018-02-01

    The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state

  11. Impact of load-related neural processes on feature binding in visuospatial working memory.

    Nicole A Kochan

    Full Text Available BACKGROUND: The capacity of visual working memory (WM is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this

  12. How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

    Chen, Heng; Chen, Xinying

    2018-01-01

    Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system. PMID:29489837

  13. Benford's Law Applies to Online Social Networks.

    Golbeck, Jennifer

    2015-01-01

    Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

  14. Benford's Law Applies to Online Social Networks.

    Jennifer Golbeck

    Full Text Available Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal, we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

  15. vhv supply networks, problems of network structure

    Raimbault, J

    1966-04-01

    The present and future power requirements of the Paris area and the structure of the existing networks are discussed. The various limitations that will have to be allowed for to lay down the structure of a regional transmission network leading in the power of the large national transmission network to within the Paris built up area are described. The theoretical solution that has been adopted, and the features of its final achievement, which is planned for about the year 2000, and the intermediate stages are given. The problem of the structure of the National Power Transmission network which is to supply the regional network was studied. To solve this problem, a 730 kV voltage network will have to be introduced.

  16. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  17. Proctographic features of anismus.

    Halligan, S; Bartram, C I; Park, H J; Kamm, M A

    1995-12-01

    To document the proctographic features of anismus at evacuation proctography and determine the optimum radiologic measurements for diagnosis. Twenty-four patients with anismus according to clinical and multiple physiologic criteria were examined with evacuation proctography. Structural and functional measurements were compared with those of a group of 20 asymptomatic subjects. No significant difference between patients and control subjects was found with respect to pelvic descent, rectocele, or any anorectal angle measurement. In patients with anismus, initiation of evacuation was prolonged (median, 9 vs 3 seconds for control subjects; P anismus should be abandoned. Patients with anismus demonstrate delayed initiation of evacuation, which is also prolonged and incomplete. Incomplete evacuation after 30 seconds is highly suggestive of anismus.

  18. Linking structural features of protein complexes and biological function.

    Sowmya, Gopichandran; Breen, Edmond J; Ranganathan, Shoba

    2015-09-01

    Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions. © 2015 The Protein Society.

  19. Three-Class Mammogram Classification Based on Descriptive CNN Features

    M. Mohsin Jadoon

    2017-01-01

    Full Text Available In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases. In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW and convolutional neural network-curvelet transform (CNN-CT. An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE. In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT, while in the second method discrete curvelet transform (DCT is used. In both methods, dense scale invariant feature (DSIFT for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN. Softmax layer and support vector machine (SVM layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

  20. Feature Selection by Reordering

    Jiřina, Marcel; Jiřina jr., M.

    2005-01-01

    Roč. 2, č. 1 (2005), s. 155-161 ISSN 1738-6438 Institutional research plan: CEZ:AV0Z10300504 Keywords : feature selection * data reduction * ordering of features Subject RIV: BA - General Mathematics

  1. Significant Tsunami Events

    Dunbar, P. K.; Furtney, M.; McLean, S. J.; Sweeney, A. D.

    2014-12-01

    Tsunamis have inflicted death and destruction on the coastlines of the world throughout history. The occurrence of tsunamis and the resulting effects have been collected and studied as far back as the second millennium B.C. The knowledge gained from cataloging and examining these events has led to significant changes in our understanding of tsunamis, tsunami sources, and methods to mitigate the effects of tsunamis. The most significant, not surprisingly, are often the most devastating, such as the 2011 Tohoku, Japan earthquake and tsunami. The goal of this poster is to give a brief overview of the occurrence of tsunamis and then focus specifically on several significant tsunamis. There are various criteria to determine the most significant tsunamis: the number of deaths, amount of damage, maximum runup height, had a major impact on tsunami science or policy, etc. As a result, descriptions will include some of the most costly (2011 Tohoku, Japan), the most deadly (2004 Sumatra, 1883 Krakatau), and the highest runup ever observed (1958 Lituya Bay, Alaska). The discovery of the Cascadia subduction zone as the source of the 1700 Japanese "Orphan" tsunami and a future tsunami threat to the U.S. northwest coast, contributed to the decision to form the U.S. National Tsunami Hazard Mitigation Program. The great Lisbon earthquake of 1755 marked the beginning of the modern era of seismology. Knowledge gained from the 1964 Alaska earthquake and tsunami helped confirm the theory of plate tectonics. The 1946 Alaska, 1952 Kuril Islands, 1960 Chile, 1964 Alaska, and the 2004 Banda Aceh, tsunamis all resulted in warning centers or systems being established.The data descriptions on this poster were extracted from NOAA's National Geophysical Data Center (NGDC) global historical tsunami database. Additional information about these tsunamis, as well as water level data can be found by accessing the NGDC website www.ngdc.noaa.gov/hazard/

  2. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

    Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

    2012-12-05

    Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

  3. Screening for Plant Features

    Heijden, van der G.W.A.M.; Polder, G.

    2015-01-01

    In this chapter, an overview of different plant features is given, from (sub)cellular to canopy level. A myriad of methods is available to measure these features using image analysis, and often, multiple methods can be used to measure the same feature. Several criteria are listed for choosing a

  4. Identification of Hadronically-Decaying W Bosons and Top Quarks Using High-Level Features as Input to Boosted Decision Trees and Deep Neural Networks in ATLAS at $\\sqrt{s}$ = 13 TeV

    The ATLAS collaboration

    2017-01-01

    The application of boosted decision trees and deep neural networks to the identification of hadronically-decaying W bosons and top quarks using high-level jet observables as inputs is investigated using Monte Carlo simulations. In the case of both boosted decision trees and deep neural networks, the use of machine learning techniques is found to improve the background rejection with respect to simple reference single jet substructure and mass taggers. Linear correlations between the resulting classifiers and the substructure variables are also presented.

  5. Identification of Hadronically-Decaying W Boson Top Quarks Using High-Level Features as Input to Boosted Decision Trees and Deep Neural Networks in ATLAS at #sqrt{s} = 13 TeV

    Nitta, Tatsumi; The ATLAS collaboration

    2017-01-01

    The application of boosted decision trees and deep neural networks to the identification of hadronically-decaying W bosons and top quarks using high-level jet observables as inputs is investigated using Monte Carlo simulations. In the case of both boosted decision trees and deep neural networks, the use of machine learning techniques is found to improve the background rejection with respect to simple reference single jet substructure and mass taggers. Linear correlations between the resulting classifiers and the substructure variables are also presented.

  6. Link prediction in weighted networks

    Wind, David Kofoed; Mørup, Morten

    2012-01-01

    Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network...... is to infer presence of edges, but that simpler models are better at inferring the actual weights....

  7. Neural networks and particle physics

    Peterson, Carsten

    1993-01-01

    1. Introduction : Structure of the Central Nervous System Generics2. Feed-forward networks, Perceptions, Function approximators3. Self-organisation, Feature Maps4. Feed-back Networks, The Hopfield model, Optimization problems, Feed-back, Networks, Deformable templates, Graph bisection

  8. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

  9. Characteristics of group networks in the KOSPI and the KOSDAQ

    Kim, Kyungsik; Ko, Jeung-Su; Yi, Myunggi

    2012-02-01

    We investigate the main feature of group networks in the KOSPI and KOSDAQ of Korean financial markets and analyze daily cross-correlations between price fluctuations for the 5-year time period from 2006 to 2010. We discuss the stabilities by undressing the market-wide effect using the Markowitz multi-factor model and the network-based approach. In particular we ascertain the explicit list of significant firms in the few largest eigenvectors from the undressed correlation matrix. Finally, we show the structure of group correlation by applying a network-based approach. In addition, the relation between market capitalizations and businesses is examined.

  10. Testing Significance Testing

    Joachim I. Krueger

    2018-04-01

    Full Text Available The practice of Significance Testing (ST remains widespread in psychological science despite continual criticism of its flaws and abuses. Using simulation experiments, we address four concerns about ST and for two of these we compare ST’s performance with prominent alternatives. We find the following: First, the 'p' values delivered by ST predict the posterior probability of the tested hypothesis well under many research conditions. Second, low 'p' values support inductive inferences because they are most likely to occur when the tested hypothesis is false. Third, 'p' values track likelihood ratios without raising the uncertainties of relative inference. Fourth, 'p' values predict the replicability of research findings better than confidence intervals do. Given these results, we conclude that 'p' values may be used judiciously as a heuristic tool for inductive inference. Yet, 'p' values cannot bear the full burden of inference. We encourage researchers to be flexible in their selection and use of statistical methods.

  11. Safety significance evaluation system

    Lew, B.S.; Yee, D.; Brewer, W.K.; Quattro, P.J.; Kirby, K.D.

    1991-01-01

    This paper reports that the Pacific Gas and Electric Company (PG and E), in cooperation with ABZ, Incorporated and Science Applications International Corporation (SAIC), investigated the use of artificial intelligence-based programming techniques to assist utility personnel in regulatory compliance problems. The result of this investigation is that artificial intelligence-based programming techniques can successfully be applied to this problem. To demonstrate this, a general methodology was developed and several prototype systems based on this methodology were developed. The prototypes address U.S. Nuclear Regulatory Commission (NRC) event reportability requirements, technical specification compliance based on plant equipment status, and quality assurance assistance. This collection of prototype modules is named the safety significance evaluation system

  12. Predicting significant torso trauma.

    Nirula, Ram; Talmor, Daniel; Brasel, Karen

    2005-07-01

    Identification of motor vehicle crash (MVC) characteristics associated with thoracoabdominal injury would advance the development of automatic crash notification systems (ACNS) by improving triage and response times. Our objective was to determine the relationships between MVC characteristics and thoracoabdominal trauma to develop a torso injury probability model. Drivers involved in crashes from 1993 to 2001 within the National Automotive Sampling System were reviewed. Relationships between torso injury and MVC characteristics were assessed using multivariate logistic regression. Receiver operating characteristic curves were used to compare the model to current ACNS models. There were a total of 56,466 drivers. Age, ejection, braking, avoidance, velocity, restraints, passenger-side impact, rollover, and vehicle weight and type were associated with injury (p < 0.05). The area under the receiver operating characteristic curve (83.9) was significantly greater than current ACNS models. We have developed a thoracoabdominal injury probability model that may improve patient triage when used with ACNS.

  13. Gas revenue increasingly significant

    Megill, R.E.

    1991-01-01

    This paper briefly describes the wellhead prices of natural gas compared to crude oil over the past 70 years. Although natural gas prices have never reached price parity with crude oil, the relative value of a gas BTU has been increasing. It is one of the reasons that the total amount of money coming from natural gas wells is becoming more significant. From 1920 to 1955 the revenue at the wellhead for natural gas was only about 10% of the money received by producers. Most of the money needed for exploration, development, and production came from crude oil. At present, however, over 40% of the money from the upstream portion of the petroleum industry is from natural gas. As a result, in a few short years natural gas may become 50% of the money revenues generated from wellhead production facilities

  14. Learning Networks, Networked Learning

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  15. Disrupted reward and cognitive control networks contribute to anhedonia in depression.

    Gong, Liang; He, Cancan; Zhang, Haisan; Zhang, Hongxing; Zhang, Zhijun; Xie, Chunming

    2018-08-01

    Neuroimaging studies have identified that anhedonia, a core feature of major depressive disorder (MDD), is associated with dysfunction in reward and cognitive control processing. However, it is still not clear how the reward network (β-network) and the cognitive control network (δ-network) are linked to biased anhedonia in MDD patients. Sixty-eight MDD patients and 64 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. A 2*2 ANCOVA analysis was used to explore the differences in the nucleus accumbens-based, voxelwise functional connectivity (FC) between the groups. Then, the β- and δ-networks were constructed, and the FC intensities were compared within and between theβ- and δ-networks across all subjects. Multiple linear regression analyses were also employed to investigate the relationships between the neural features of the β- and δ-networks and anhedonia in MDD patients. Compared to the CN subjects, the MDD patients showed synergistic functional decoupling in both the β- and δ-networks, as well as decreased FC intensities in the intra- and inter- β- and δ-networks. In addition, the FC in both the β- and δ-networks was significantly correlated with anhedonia severity in the MDD patients. Importantly, the integrated neural features of the β- and δ-networks could more precisely predict anhedonic symptoms. These findings initially demonstrated that the imbalance between β- and δ-network activity successfully predicted anhedonia severity and suggested that the neural features of both the β- and δ-networks could represent a fundamental mechanism that underlies anhedonia in MDD patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. The influence of passenger flow on the topology characteristics of urban rail transit networks

    Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong

    2017-05-01

    Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.

  17. Multilevel method for modeling large-scale networks.

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  18. Tumor significant dose

    Supe, S.J.; Nagalaxmi, K.V.; Meenakshi, L.

    1983-01-01

    In the practice of radiotherapy, various concepts like NSD, CRE, TDF, and BIR are being used to evaluate the biological effectiveness of the treatment schedules on the normal tissues. This has been accepted as the tolerance of the normal tissue is the limiting factor in the treatment of cancers. At present when various schedules are tried, attention is therefore paid to the biological damage of the normal tissues only and it is expected that the damage to the cancerous tissues would be extensive enough to control the cancer. Attempt is made in the present work to evaluate the concent of tumor significant dose (TSD) which will represent the damage to the cancerous tissue. Strandquist in the analysis of a large number of cases of squamous cell carcinoma found that for the 5 fraction/week treatment, the total dose required to bring about the same damage for the cancerous tissue is proportional to T/sup -0.22/, where T is the overall time over which the dose is delivered. Using this finding the TSD was defined as DxN/sup -p/xT/sup -q/, where D is the total dose, N the number of fractions, T the overall time p and q are the exponents to be suitably chosen. The values of p and q are adjusted such that p+q< or =0.24, and p varies from 0.0 to 0.24 and q varies from 0.0 to 0.22. Cases of cancer of cervix uteri treated between 1978 and 1980 in the V. N. Cancer Centre, Kuppuswamy Naidu Memorial Hospital, Coimbatore, India were analyzed on the basis of these formulations. These data, coupled with the clinical experience, were used for choice of a formula for the TSD. Further, the dose schedules used in the British Institute of Radiology fraction- ation studies were also used to propose that the tumor significant dose is represented by DxN/sup -0.18/xT/sup -0.06/

  19. Inferring general relations between network characteristics from specific network ensembles.

    Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

    2012-01-01

    Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

  20. In-network adaptation of SHVC video in software-defined networks

    Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos

    2016-04-01

    Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and