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

CSIR Research Space (South Africa)

Mzila, P

2013-07-01

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

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

Science.gov (United States)

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.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

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.

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

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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.

Identifying significant environmental features using feature recognition.

Science.gov (United States)

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...

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

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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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

2016-05-01

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

Metastable Features of Economic Networks and Responses to Exogenous Shocks.

Directory of Open Access Journals (Sweden)

Ali Hosseiny

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

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

Science.gov (United States)

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.

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

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.

GalaxyGAN: Generative Adversarial Networks for recovery of galaxy features

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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)

A keyword spotting model using perceptually significant energy features

Science.gov (United States)

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.

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

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

Features of complex networks in a free-software operating system

International Nuclear Information System (INIS)

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.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

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

NARCIS (Netherlands)

Drugan, Madalina M.; Wiering, Marco A.

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

Topological Embedding Feature Based Resource Allocation in Network Virtualization

Directory of Open Access Journals (Sweden)

Hongyan Cui

2014-01-01

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

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

Wu, Jie; Xie, Si-ya; Shi, Xin-bao; Chen, Yao-wen

2017-11-01

In this paper, a novel framework, named as global-local feature attention network with reranking strategy (GLAN-RS), is presented for image captioning task. Rather than only adopting unitary visual information in the classical models, GLAN-RS explores the attention mechanism to capture local convolutional salient image maps. Furthermore, we adopt reranking strategy to adjust the priority of the candidate captions and select the best one. The proposed model is verified using the Microsoft Common Objects in Context (MSCOCO) benchmark dataset across seven standard evaluation metrics. Experimental results show that GLAN-RS significantly outperforms the state-of-the-art approaches, such as multimodal recurrent neural network (MRNN) and Google NIC, which gets an improvement of 20% in terms of BLEU4 score and 13 points in terms of CIDER score.

Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

Science.gov (United States)

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.

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

CSIR Research Space (South Africa)

Naidoo, Tyrone

2015-09-01

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

D3.5 Report on ECO social network integration features

NARCIS (Netherlands)

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.

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

Science.gov (United States)

Lopes, U K; Valiati, J F

2017-10-01

It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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

1998-12-01

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

Cumulative Significance of Hyporheic Exchange and Biogeochemical Processing in River Networks

Science.gov (United States)

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

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

Wang, Xun-Heng, E-mail: xhwang@hdu.edu.cn [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China); School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096 (China); Li, Lihua [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China)

2015-05-15

Highlights: • Temporal patterns within ICNs provide new way to investigate ADHD brains. • ADHD exhibits enhanced temporal activities within and between ICNs. • Network-wise ALFF influences functional connectivity between ICNs. • Univariate patterns within ICNs are correlated to behavior scores. - Abstract: Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores. Materials and methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space. Using general linear regression, 20 ICNs were taken as spatial templates to analyze the time-courses of ICNs for each subject. Amplitude of low frequency fluctuations (ALFFs) were computed as univariate temporal features within ICNs. Pearson correlation coefficients and node strengths were computed as bivariate temporal features between ICNs. Additional correlation analysis was performed between temporal features of ICNs and behavior scores. Results: ADHD exhibited more activated network-wise ALFF than normal controls in attention and default mode-related network. Enhanced functional connectivities between ICNs were found in ADHD. The network-wise ALFF within ICNs might influence the functional connectivity between ICNs. The temporal pattern within posterior default mode network (pDMN) was positively correlated to inattentive scores. The subcortical network, fusiform-related DMN and attention-related networks were negatively correlated to Intelligence Quotient (IQ) scores. Conclusion: The temporal low frequency oscillations of ICNs in boys with ADHD were more activated than normal controls during resting state; the temporal features within ICNs could

Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

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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.

Breast image feature learning with adaptive deconvolutional networks

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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).

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

DEFF Research Database (Denmark)

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

1998-01-01

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

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

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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.

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

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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.

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

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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.

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

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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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

International Nuclear Information System (INIS)

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

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

Science.gov (United States)

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

2017-03-01

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

Identification of significant features by the Global Mean Rank test.

Science.gov (United States)

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.

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

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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.

Feature selection based on SVM significance maps for classification of dementia

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

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

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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.

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

Science.gov (United States)

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.

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

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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.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

The Significant Social Networks of Women Who Have Resided in Shelters

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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.

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

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.

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

AlShahrani, Mona

2015-05-24

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

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

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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.

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

Science.gov (United States)

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.

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

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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.

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

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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.

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

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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.

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

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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

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

International Nuclear Information System (INIS)

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)

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

Science.gov (United States)

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

2017-05-01

Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× cross-validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.

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

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-07-01

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

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

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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.

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

Science.gov (United States)

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.

ADHD classification using bag of words approach on network features

Science.gov (United States)

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

2012-02-01

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

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.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

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.

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

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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.

Network motif frequency vectors reveal evolving metabolic network organisation.

Science.gov (United States)

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.

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

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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

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-11-30

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

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

Science.gov (United States)

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).

Input significance analysis: feature selection through synaptic ...

African Journals Online (AJOL)

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

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

Science.gov (United States)

Chen, Chi-Wen; Lin, Chiun-Sin

2014-07-01

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

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

Science.gov (United States)

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

2012-01-01

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

A link prediction method for heterogeneous networks based on BP neural network

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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

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

OpenAIRE

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,...

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

Directory of Open Access Journals (Sweden)

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.

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

National Research Council Canada - National Science Library

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...

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

Science.gov (United States)

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

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

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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.

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

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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.

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

Science.gov (United States)

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

2017-05-15

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

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

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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.

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

International Nuclear Information System (INIS)

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

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

DEFF Research Database (Denmark)

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

2014-01-01

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

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

Science.gov (United States)

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

2014-04-01

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

Fourth international conference on Networks & Communications

CERN Document Server

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.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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)

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

Science.gov (United States)

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

2014-03-01

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

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Pilar Fuster-Parra

2016-05-01

Full Text Available The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players, the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN and two special types of BNs, the Bayesian classifiers: naive Bayes (NB and tree augmented naive Bayes (TAN. BNs are selected as they are able to produce probability estimates rather than predictions. BN results show that the antecessors (the “top” features ranked are the team members’ expectations and their attraction to the social aspects of the task. The main node is formed by the cooperative behaviors, the consequences ranked at the BN bottom (ratified by the TAN trees and the instantiations made, the roles assigned to the members and their survival inside the same team. These results should help managers to determine contents and priorities when they have to face team-building actions.

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

Science.gov (United States)

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.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

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.

Fifth International Conference on Networks & Communications

CERN Document Server

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.

A neural network model to predict lung radiation-induced pneumonitis

International Nuclear Information System (INIS)

Chen Shifeng; Zhou Sumin; Zhang Junan; Yin Fangfang; Marks, Lawrence B.; Das, Shiva K.

2007-01-01

A feed-forward neural network was investigated to predict the occurrence of lung radiation-induced Grade 2+ pneumonitis. The database consisted of 235 patients with lung cancer treated using radiotherapy, of whom 34 were diagnosed with Grade 2+ pneumonitis at follow-up. The network was constructed using an algorithm that alternately grew and pruned it, starting from the smallest possible network, until a satisfactory solution was found. The weights and biases of the network were computed using the error back-propagation approach. Momentum and variable leaning techniques were used to speed convergence. Using the growing/pruning approach, the network selected features from 66 dose and 27 non-dose variables. During network training, the 235 patients were randomly split into ten groups of approximately equal size. Eight groups were used to train the network, one group was used for early stopping training to prevent overfitting, and the remaining group was used as a test to measure the generalization capability of the network (cross-validation). Using this methodology, each of the ten groups was considered, in turn, as the test group (ten-fold cross-validation). For the optimized network constructed with input features selected from dose and non-dose variables, the area under the receiver operating characteristics (ROC) curve for cross-validated testing was 0.76 (sensitivity: 0.68, specificity: 0.69). For the optimized network constructed with input features selected only from dose variables, the area under the ROC curve for cross-validation was 0.67 (sensitivity: 0.53, specificity: 0.69). The difference between these two areas was statistically significant (p=0.020), indicating that the addition of non-dose features can significantly improve the generalization capability of the network. A network for prospective testing was constructed with input features selected from dose and non-dose variables (all data were used for training). The optimized network architecture

Network features and pathway analyses of a signal transduction cascade

Directory of Open Access Journals (Sweden)

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.

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

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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

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

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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

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

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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.

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

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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.

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

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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

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

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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.

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

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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.

Multimodal Feature Learning for Video Captioning

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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.

Neural network tracking and extension of positive tracking periods

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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.

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

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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.

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

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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

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

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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.

The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.

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St-Yves, Ghislain; Naselaris, Thomas

2017-06-20

We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: "where" parameters that characterize the location and extent of pooling over visual features, and "what" parameters that characterize tuning to visual features. The "where" parameters are analogous to classical receptive fields, while "what" parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation. We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep

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

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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.

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

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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.

BP network for atorvastatin effect evaluation from ultrasound images features classification

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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.

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

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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.

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

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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.

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

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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.

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

DEFF Research Database (Denmark)

Dalgaard, Jens; Pena, Jose; Kocka, Tomas

2004-01-01

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

Patch layout generation by detecting feature networks

KAUST Repository

Cao, Yuanhao

2015-02-01

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

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

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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.

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

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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.

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

Science.gov (United States)

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.

A framework for online social networking features

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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.

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

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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.

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

Science.gov (United States)

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...

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

Science.gov (United States)

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...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

Economic Features of the Internet and Network Neutrality

OpenAIRE

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...

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

International Nuclear Information System (INIS)

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.

Facial expression recognition based on improved deep belief networks

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

Network Ecology and Adolescent Social Structure.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

Science.gov (United States)

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

New approach to ECG's features recognition involving neural network

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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…

Regular Network Class Features Enhancement Using an Evolutionary Synthesis Algorithm

Directory of Open Access Journals (Sweden)

O. G. Monahov

2014-01-01

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

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

Science.gov (United States)

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

Artificial neural network based approach to transmission lines protection

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

Accurately Identifying New QoS Violation Driven by High-Distributed Low-Rate Denial of Service Attacks Based on Multiple Observed Features

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Young-Chon Kim

2011-05-01

Full Text Available Although the conventional duty cycle MAC protocols for Wireless Sensor Networks (WSNs such as RMAC perform well in terms of saving energy and reducing end-to-end delivery latency, they were designed independently and require an extra routing protocol in the network layer to provide path information for the MAC layer. In this paper, we propose a new cross-layer duty cycle MAC protocol with data forwarding supporting a pipeline feature (P-MAC for WSNs. P-MAC first divides the whole network into many grades around the sink. Each node identifies its grade according to its logical hop distance to the sink and simultaneously establishes a sleep/wakeup schedule using the grade information. Those nodes in the same grade keep the same schedule, which is staggered with the schedule of the nodes in the adjacent grade. Then a variation of the RTS/CTS handshake mechanism is used to forward data continuously in a pipeline fashion from the higher grade to the lower grade nodes and finally to the sink. No extra routing overhead is needed, thus increasing the network scalability while maintaining the superiority of duty-cycling. The simulation results in OPNET show that P-MAC has better performance than S-MAC and RMAC in terms of packet delivery latency and energy efficiency.

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

International Nuclear Information System (INIS)

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.

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

Directory of Open Access Journals (Sweden)

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.

Cascading Failures and Recovery in Networks of Networks

Science.gov (United States)

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.

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

African Journals Online (AJOL)

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 ...

Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

Science.gov (United States)

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

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

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Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

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

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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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

International Nuclear Information System (INIS)

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.

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

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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.

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

Directory of Open Access Journals (Sweden)

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

European TSO Network (ETSON) as Important Part of International Nuclear Safety Knowledge Networks

International Nuclear Information System (INIS)

Teske, Hartmuth; Dierschow, Frank; Eibl-Schwäger, Carla

2014-01-01

Conclusion: • Nuclear networks, including ETSON, are fulfilling their respective and defined specific aims; • Common are such objectives like: – exchange of information; – transfer of knowledge; – support education and training; – collaborate effectively together; – promote coordination and – support harmonization. • Further endeavor are needed to: – Make more significant efforts in promoting the opportunities of collaborative actions; – Use more active the existing features of the modern network tools; – Combine regional or global networks with national web-based knowledge resources by developing and maintaining further the National Nuclear Regulatory Portals. • Nuclear safety and security networks are effectively contributing to build and sustain needed capacities and capabilities

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

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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.

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

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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/.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

Directory of Open Access Journals (Sweden)

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.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

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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.

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

International Nuclear Information System (INIS)

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

Coding visual features extracted from video sequences.

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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.

Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

DEFF Research Database (Denmark)

Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro

2010-01-01

mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...

Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

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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.

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

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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.

Feature-based RNN target recognition

Science.gov (United States)

Bakircioglu, Hakan; Gelenbe, Erol

1998-09-01

Detection and recognition of target signatures in sensory data obtained by synthetic aperture radar (SAR), forward- looking infrared, or laser radar, have received considerable attention in the literature. In this paper, we propose a feature based target classification methodology to detect and classify targets in cluttered SAR images, that makes use of selective signature data from sensory data, together with a neural network technique which uses a set of trained networks based on the Random Neural Network (RNN) model (Gelenbe 89, 90, 91, 93) which is trained to act as a matched filter. We propose and investigate radial features of target shapes that are invariant to rotation, translation, and scale, to characterize target and clutter signatures. These features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate and validate the proposed method, and to calculate Receiver Operating Characteristics which illustrate the performance of the proposed algorithm.

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

Directory of Open Access Journals (Sweden)

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%.

Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

Science.gov (United States)

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.

Method for assessing reliability of a network considering probabilistic safety assessment

International Nuclear Information System (INIS)

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)

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

Science.gov (United States)

Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

2018-03-04

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

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

International Nuclear Information System (INIS)

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

Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks

International Nuclear Information System (INIS)

Evander, Eva; Holst, Holger; Jaerund, Andreas; Wollmer, Per; Edenbrandt, Lars; Ohlsson, Mattias; Aastroem, Karl

2003-01-01

The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1,087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had undergone both scintigraphy and pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. The performance of the neural network in interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71-0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77-0.90). This increase was statistically significant (P=0.022). The performance increased to 0.87 (0.81-0.93) when all perfusion and ventilation images were used, and the increase in performance from 0.79 to 0.87 was also statistically significant (P=0.016). The automated method presented here for the interpretation of lung scintigrams shows a significant

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

Science.gov (United States)

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.

Applying Physical-Layer Network Coding in Wireless Networks

Directory of Open Access Journals (Sweden)

Liew SoungChang

2010-01-01

Full Text Available A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11. This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.

Neutral theory of chemical reaction networks

International Nuclear Information System (INIS)

Lee, Sang Hoon; Holme, Petter; Minnhagen, Petter; Bernhardsson, Sebastian; Kim, Beom Jun

2012-01-01

To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has been subjected to biological evolution (the chemical reaction network of metabolism in living cells) and one that has not (the atmospheric planetary chemical reaction networks). Their degree distributions are shown to share the very same neutral system-independent features. The shape of the broad distributions is to a large extent controlled by a single parameter, the network size. From this perspective, there is little difference between atmospheric and metabolic networks; they are just different sizes of the same random assembling network. In other words, the shape of the degree distribution is a neutral characteristic feature and has no functional or evolutionary implications in itself; it is not a matter of life and death. (paper)

Disrupted reward and cognitive control networks contribute to anhedonia in depression.

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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.

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Recent advances on failure and recovery in networks of networks

International Nuclear Information System (INIS)

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.

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

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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.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Persistent homology of complex networks

International Nuclear Information System (INIS)

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

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

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Baojun Zhao

2018-03-01

Full Text Available With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP.

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

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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.

Enhanced memory performance thanks to neural network assortativity

International Nuclear Information System (INIS)

Franciscis, S. de; Johnson, S.; Torres, J. J.

2011-01-01

The behaviour of many complex dynamical systems has been found to depend crucially on the structure of the underlying networks of interactions. An intriguing feature of empirical networks is their assortativity--i.e., the extent to which the degrees of neighbouring nodes are correlated. However, until very recently it was difficult to take this property into account analytically, most work being exclusively numerical. We get round this problem by considering ensembles of equally correlated graphs and apply this novel technique to the case of attractor neural networks. Assortativity turns out to be a key feature for memory performance in these systems - so much so that for sufficiently correlated topologies the critical temperature diverges. We predict that artificial and biological neural systems could significantly enhance their robustness to noise by developing positive correlations.

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

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.

Impact of load-related neural processes on feature binding in visuospatial working memory.

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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

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

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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

Applying Gradient Descent in Convolutional Neural Networks

Science.gov (United States)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

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

Directory of Open Access Journals (Sweden)

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.

Modeling the interdependent network based on two-mode networks

Science.gov (United States)

An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

2017-10-01

Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

Slow feature analysis: unsupervised learning of invariances.

Science.gov (United States)

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Adverse Outcome Pathway Networks II: Network Analytics.

Science.gov (United States)

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.

Impact of PON deployment on metro networks

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Science.gov (United States)

Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

Modeling the citation network by network cosmology.

Science.gov (United States)

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.

An automated approach to network features of protein structure ensembles

Science.gov (United States)

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

Entropy of network ensembles

Science.gov (United States)

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.

A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

Directory of Open Access Journals (Sweden)

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.

Deep-learning derived features for lung nodule classification with limited datasets

Science.gov (United States)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

2015-02-01

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

Noise-enhanced categorization in a recurrently reconnected neural network

International Nuclear Information System (INIS)

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

Noise-enhanced categorization in a recurrently reconnected neural network

Science.gov (United States)

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.

Network-induced oscillatory behavior in material flow networks and irregular business cycles

Science.gov (United States)

Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas

2004-11-01

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units—and linear chains of these units—behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

Science.gov (United States)

Tzirakis, Panagiotis; Trigeorgis, George; Nicolaou, Mihalis A.; Schuller, Bjorn W.; Zafeiriou, Stefanos

2017-12-01

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

Discriminating Induced-Microearthquakes Using New Seismic Features

Science.gov (United States)

Mousavi, S. M.; Horton, S.

2016-12-01

We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

Multivariate correlation analysis technique based on euclidean distance map for network traffic characterization

NARCIS (Netherlands)

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

Improving the Robustness of Deep Neural Networks via Stability Training

OpenAIRE

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...

Delay tolerant networks

CERN Document Server

Gao, Longxiang; Luan, Tom H

2015-01-01

This brief presents emerging and promising communication methods for network reliability via delay tolerant networks (DTNs). Different from traditional networks, DTNs possess unique features, such as long latency and unstable network topology. As a result, DTNs can be widely applied to critical applications, such as space communications, disaster rescue, and battlefield communications. The brief provides a complete investigation of DTNs and their current applications, from an overview to the latest development in the area. The core issue of data forward in DTNs is tackled, including the importance of social characteristics, which is an essential feature if the mobile devices are used for human communication. Security and privacy issues in DTNs are discussed, and future work is also discussed.

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

Directory of Open Access Journals (Sweden)

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.

Dynamic hydro-climatic networks in pristine and regulated rivers

Science.gov (United States)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes

A New Feature Selection Algorithm Based on the Mean Impact Variance

Directory of Open Access Journals (Sweden)

Weidong Cheng

2014-01-01

Full Text Available The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2 the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA algorithm and a back propagation (BP network is constructed, and (4 the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.

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

Science.gov (United States)

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.

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

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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

Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

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Feng Cun-qian

2015-12-01

Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

Sparsity in Model Gene Regulatory Networks

International Nuclear Information System (INIS)

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)

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

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Chen Xing

2016-01-01

Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.

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

Institute of Scientific and Technical Information of China (English)

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.

Patch layout generation by detecting feature networks

KAUST Repository

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

Breast Cancer Detection with Reduced Feature Set

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Ahmet Mert

2015-01-01

Full Text Available This paper explores feature reduction properties of independent component analysis (ICA on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC dataset is reduced to one-dimensional feature vector computing an independent component (IC. The original data with 30 features and reduced one feature (IC are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN, artificial neural network (ANN, radial basis function neural network (RBFNN, and support vector machine (SVM. The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations and partitioning (20%–40% methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC curve with its criterion values including area under curve (AUC and 95% confidential interval (CI. This represents an improvement in diagnostic decision support system, while reducing computational complexity.

Spatiotemporal Features for Asynchronous Event-based Data

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Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

Science.gov (United States)

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

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

Science.gov (United States)

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.

Inferring general relations between network characteristics from specific network ensembles.

Science.gov (United States)

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.

Research on NGN network control technology

Science.gov (United States)

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.

Video Scene Parsing with Predictive Feature Learning

OpenAIRE

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 ...

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

Science.gov (United States)

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

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

NARCIS (Netherlands)

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

Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction.

Science.gov (United States)

Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard

2016-04-01

The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

International Nuclear Information System (INIS)

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

The Social Origins of Networks and Diffusion.

Science.gov (United States)

Centola, Damon

2015-03-01

Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.

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

Science.gov (United States)

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.

Infrared image enhancement with learned features

Science.gov (United States)

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.

A Gamma Memory Neural Network for System Identification

Science.gov (United States)

Motter, Mark A.; Principe, Jose C.

1992-01-01

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

Science.gov (United States)

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

Optical-Correlator Neural Network Based On Neocognitron

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Directory of Open Access Journals (Sweden)

Jie Hu

2018-01-01

Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

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

Science.gov (United States)

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

No-reference image quality assessment based on statistics of convolution feature maps

Science.gov (United States)

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.

Social networking in online support groups for health: how online social networking benefits patients.

Science.gov (United States)

Chung, Jae Eun

2014-01-01

An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

ECG Identification System Using Neural Network with Global and Local Features

Science.gov (United States)

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

2016-01-01

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

vhv supply networks, problems of network structure

Energy Technology Data Exchange (ETDEWEB)

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.

Network configuration of global R&D networks

DEFF Research Database (Denmark)

Hansen, Zaza Nadja Lee; Srai, Jagjit Singh

2011-01-01

, network configuration of global R&D has tended to focus on strategic elements with limited attention given operational effectiveness, or to interfaces with downstream manufacturing operations. Within OM literature, the drivers of configuration of global networks within, engineering, production, supply...... to R&D networks emerged, e.g. product features were more prominent in R&D networks. Furthermore, the study has shown extensive interaction with other operations, including many downstream manufacturing operations. By extending the OM configuration concepts to the configuration of R&D networks......Companies are increasingly globalising their R&D activities, both within the firms and with external partners, with consequent implications for their interaction with manufacturing operations. Previous research in R&D networks has focused on coordination, governance and support elements. However...

Multi-dimension feature fusion for action recognition

Science.gov (United States)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Generic Physiological Features as Predictors of Player Experience

DEFF Research Database (Denmark)

Martínez, Héctor Pérez; Garbarino, Maurizio; Yannakakis, Georgios N.

2011-01-01

This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two...

Face recognition via Gabor and convolutional neural network

Science.gov (United States)

Lu, Tongwei; Wu, Menglu; Lu, Tao

2018-04-01

In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

Science.gov (United States)

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

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

International Nuclear Information System (INIS)

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

Scalable Lunar Surface Networks and Adaptive Orbit Access

Science.gov (United States)

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.

The International Trade Network: weighted network analysis and modelling

International Nuclear Information System (INIS)

Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K

2008-01-01

Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN

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

Science.gov (United States)

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

Neural Networks Relating Alloy Composition, Microstructure, and Tensile Properties of α/ β-Processed TIMETAL 6-4

Science.gov (United States)

Collins, Peter C.; Koduri, Santhosh; Welk, Brian; Tiley, Jaimie; Fraser, Hamish L.

2013-03-01

Bayesian neural networks have been developed, which relate composition, microstructure, and tensile properties of the alloy TIMETAL 6-4 (nominal composition: Ti-6Al-4V (wt pct) after thermomechanical processing (TMP) in the two-phase ( α + β)-phase field. The developed networks are able to make interpolative predictions of properties within the ranges of composition and microstructural features that are in the population of the database used for training and testing of the networks. In addition, the neural networks have been used to conduct virtual experiments which permit the functional dependencies of properties on composition and microstructural features to be determined. In this way, it is shown that in the microstructural condition resulting from TMP in the two-phase ( α + β) phase field, the most significant contribution to strength is from solid solution strengthening, with microstructural features apparently influencing the balance of a number of properties.

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

Science.gov (United States)

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

Friend suggestion in social network based on user log

Science.gov (United States)

Kaviya, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.

2017-11-01

Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.

Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach.

Science.gov (United States)

Jin, Huiyuan; Liu, Haitao

2016-01-01

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

Science.gov (United States)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

Target recognition based on convolutional neural network

Science.gov (United States)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

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

OpenAIRE

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...

A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

KAUST Repository

Werfelmann, Robert

2018-05-24

Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

Science.gov (United States)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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

Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks.

Science.gov (United States)

Kaewprag, Pacharmon; Newton, Cheryl; Vermillion, Brenda; Hyun, Sookyung; Huang, Kun; Machiraju, Raghu

2017-07-05

We develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers. We present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features. From the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers. Given the strong adverse effect of pressure ulcers

Coding Local and Global Binary Visual Features Extracted From Video Sequences

Science.gov (United States)

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.

Mesh networks: an optimum solution for AMR

Energy Technology Data Exchange (ETDEWEB)

Mimno, G.

2003-12-01

Characteristics of mesh networks and the advantage of using them in automatic meter reading equipment (AMR) are discussed. Mesh networks are defined as being similar to a fishing net made of knots and links. In mesh networks the knots represent meter sites and the links are the radio paths between the meter sites and the neighbourhood concentrator. In mesh networks any knot in the communications chain can link to any other and the optimum path is calculated by the network by hopping from meter to meter until the radio message reaches a concentrator. This mesh communications architecture is said to be vastly superior to many older types of radio-based meter reading technologies; its main advantage is that it not only significantly improves the economics of fixed network deployment, but also supports time-of-use metering, remote disconnect services and advanced features, such as real-time pricing, demand response, and other efficiency measures, providing a better return on investment and reliability.

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

Science.gov (United States)

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.

Hierarchy Measure for Complex Networks

Science.gov (United States)

Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

2012-01-01

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

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

Directory of Open Access Journals (Sweden)

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 % .

Multimodal Hyper-connectivity Networks for MCI Classification.

Science.gov (United States)

Li, Yang; Gao, Xinqiang; Jie, Biao; Yap, Pew-Thian; Kim, Min-Jeong; Wee, Chong-Yaw; Shen, Dinggang

2017-09-01

Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

Reconfigurable optical implementation of quantum complex networks

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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. �

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

OpenAIRE

Bao, Ruying; Liang, Sihang; Wang, Qingcan

2018-01-01

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations are intentionally added to the original inputs to fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to capture the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping b...

Robust visual tracking via multiscale deep sparse networks

Science.gov (United States)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Optical Access Networks

Science.gov (United States)

Zheng, Jun; Ansari, Nirwan

2005-01-01

have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. Scope of Contributions This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis Manuscript Submission To submit to this special issue, follow the normal procedure for submission to JON, indicating ``Optical Access Networks feature' in the ``Comments' field of the online submission form. For all other questions relating to this feature issue, please send an e-mail to jon@osa.org, subject line ``Optical Access Networks' Additional information can be found on the JON website: http://www.osa-jon.org/submission/. Submission Deadline: 1 June 2005

Chinese Writing of Deaf or Hard-of-hearing Students and Normal-hearing Peers from Complex Network Approach

Directory of Open Access Journals (Sweden)

Huiyuan Jin

2016-11-01

Full Text Available Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts, because sign language is the primary communicative skills for many deaf people. The current body of research only covers the detailed linguistic features of deaf or hard-of-hearing students. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students’ exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.

Advanced network programming principles and techniques : network application programming with Java

CERN Document Server

Ciubotaru, Bogdan

2013-01-01

Answering the need for an accessible overview of the field, this text/reference presents a manageable introduction to both the theoretical and practical aspects of computer networks and network programming. Clearly structured and easy to follow, the book describes cutting-edge developments in network architectures, communication protocols, and programming techniques and models, supported by code examples for hands-on practice with creating network-based applications. Features: presents detailed coverage of network architectures; gently introduces the reader to the basic ideas underpinning comp

Three-Class Mammogram Classification Based on Descriptive CNN Features

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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.

Neural networks and particle physics

CERN Document Server

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

Cross-modal integration of lexical-semantic features during word processing: evidence from oscillatory dynamics during EEG.

Directory of Open Access Journals (Sweden)

Markus J van Ackeren

Full Text Available In recent years, numerous studies have provided converging evidence that word meaning is partially stored in modality-specific cortical networks. However, little is known about the mechanisms supporting the integration of this distributed semantic content into coherent conceptual representations. In the current study we aimed to address this issue by using EEG to look at the spatial and temporal dynamics of feature integration during word comprehension. Specifically, participants were presented with two modality-specific features (i.e., visual or auditory features such as silver and loud and asked to verify whether these two features were compatible with a subsequently presented target word (e.g., WHISTLE. Each pair of features described properties from either the same modality (e.g., silver, tiny  =  visual features or different modalities (e.g., silver, loud  =  visual, auditory. Behavioral and EEG data were collected. The results show that verifying features that are putatively represented in the same modality-specific network is faster than verifying features across modalities. At the neural level, integrating features across modalities induces sustained oscillatory activity around the theta range (4-6 Hz in left anterior temporal lobe (ATL, a putative hub for integrating distributed semantic content. In addition, enhanced long-range network interactions in the theta range were seen between left ATL and a widespread cortical network. These results suggest that oscillatory dynamics in the theta range could be involved in integrating multimodal semantic content by creating transient functional networks linking distributed modality-specific networks and multimodal semantic hubs such as left ATL.

Neural Network Classifiers for Local Wind Prediction.

Science.gov (United States)

Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

2004-05-01

This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

Directory of Open Access Journals (Sweden)

Taosheng Xu

Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

Species turnover and geographic distance in an urban river network

DEFF Research Database (Denmark)

Rouquette, James R.; Dallimer, Martin; Armsworth, Paul R.

2013-01-01

AimUnderstanding the relationships between species turnover, environmental features and the geographic distance between sites can provide important insights into the processes driving species diversity. This is particularly relevant where the effective distance between sites may be a function...... patterns of species turnover and to determine whether these patterns differ between different taxonomic groups. LocationSheffield area, UK. MethodsAquatic (macroinvertebrates, diatoms) and terrestrial (birds, plants, butterflies) organisms were surveyed at 41 sites across an urban river network. We...... of the geographic distance measures, although network distance remained significant for birds and some plant groups after removing the effect of environmental distance. Water-dispersed and neophyte plant groups were significantly related to network and flow distance. Main conclusionsThe results suggest that aquatic...

Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition.

Science.gov (United States)

Lin, Tsung-Yu; RoyChowdhury, Aruni; Maji, Subhransu

2017-07-04

We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 84.5% and 91.3% per-image accuracy on the Caltech-UCSD birds [66], NABirds [63], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.

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

International Nuclear Information System (INIS)

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

Windows 2012 Server network security securing your Windows network systems and infrastructure

CERN Document Server

Rountree, Derrick

2013-01-01

Windows 2012 Server Network Security provides the most in-depth guide to deploying and maintaining a secure Windows network. The book drills down into all the new features of Windows 2012 and provides practical, hands-on methods for securing your Windows systems networks, including: Secure remote access Network vulnerabilities and mitigations DHCP installations configuration MAC filtering DNS server security WINS installation configuration Securing wired and wireless connections Windows personal firewall

On the origin of distribution patterns of motifs in biological networks

Directory of Open Access Journals (Sweden)

Lesk Arthur M

2008-08-01

Full Text Available Abstract Background Inventories of small subgraphs in biological networks have identified commonly-recurring patterns, called motifs. The inference that these motifs have been selected for function rests on the idea that their occurrences are significantly more frequent than random. Results Our analysis of several large biological networks suggests, in contrast, that the frequencies of appearance of common subgraphs are similar in natural and corresponding random networks. Conclusion Indeed, certain topological features of biological networks give rise naturally to the common appearance of the motifs. We therefore question whether frequencies of occurrences are reasonable evidence that the structures of motifs have been selected for their functional contribution to the operation of networks.

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Hasan A. A. Al-Rawi

2014-01-01

Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.

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

Directory of Open Access Journals (Sweden)

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

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

Science.gov (United States)

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

Machine learning spatial geometry from entanglement features

Science.gov (United States)

You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang

2018-02-01

Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).

Characteristics of group networks in the KOSPI and the KOSDAQ

Science.gov (United States)

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.

Transmission in Optically Transparent Core Networks

Science.gov (United States)

Kilper, Dan; Jensen, Rich; Petermann, Klaus; Karasek, Miroslav

2007-03-01

Call for Papers: Transmission in Optically Transparent Core Networks Guest Feature Editors Dan Kilper and Rich Jensen, Coordinating Associate Editors Klaus Petermann and Miroslav Karasek, Guest Feature Editors Submission deadline: 15 June 2007 Optically transparent networks in which optical transport signals are routed uninterrupted through multiple nodes have long been viewed as an important evolutionary step in fiber optic communications. More than a decade of research and development on transparent network technologies together with the requisite traffic growth has culminated in the recent deployment of commercial optically transparent systems. Although many of the traditional research goals of optical transmission remain important, optical transparency introduces new challenges. Greater emphasis is placed on system efficiency and control. The goal of minimizing signal terminations, which has been pursued through increasing reach and channel capacity, also can be realized through wavelength routing techniques. Rather than bounding system operation by rigid engineering rules, the physical layer is controlled and managed by automation tools. Many static signal impairments become dynamic due to network reconfiguration and transient fault events. Recently new directions in transmission research have emerged to address transparent networking problems. This special issue of the Journal of Optical Networking will examine the technologies and theory underpinning transmission in optically transparent core networks, including both metropolitan and long haul systems. Scope of Submission The special issue editors are soliciting high-quality original research papers related to transmission in optically transparent core networks. Although this does not include edge networks such as access or enterprise networks, core networks that have access capabilities will be considered in scope as will topics related to the interworking between core and edge networks. The core network

Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

Science.gov (United States)

Carnegie, Nicole Bohme

2018-01-30

Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Chinese Sentence Classification Based on Convolutional Neural Network

Science.gov (United States)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

On Applications of Disruption Tolerant Networking to Optical Networking in Space

Science.gov (United States)

Hylton, Alan Guy; Raible, Daniel E.; Juergens, Jeffrey; Iannicca, Dennis

2012-01-01

The integration of optical communication links into space networks via Disruption Tolerant Networking (DTN) is a largely unexplored area of research. Building on successful foundational work accomplished at JPL, we discuss a multi-hop multi-path network featuring optical links. The experimental test bed is constructed at the NASA Glenn Research Center featuring multiple Ethernet-to-fiber converters coupled with free space optical (FSO) communication channels. The test bed architecture models communication paths from deployed Mars assets to the deep space network (DSN) and finally to the mission operations center (MOC). Reliable versus unreliable communication methods are investigated and discussed; including reliable transport protocols, custody transfer, and fragmentation. Potential commercial applications may include an optical communications infrastructure deployment to support developing nations and remote areas, which are unburdened with supporting an existing heritage means of telecommunications. Narrow laser beam widths and control of polarization states offer inherent physical layer security benefits with optical communications over RF solutions. This paper explores whether or not DTN is appropriate for space-based optical networks, optimal payload sizes, reliability, and a discussion on security.

Do-it-yourself networks: a novel method of generating weighted networks.

Science.gov (United States)

Shanafelt, D W; Salau, K R; Baggio, J A

2017-11-01

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

Generating description with multi-feature fusion and saliency maps of image

Science.gov (United States)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

Coding Local and Global Binary Visual Features Extracted From Video Sequences.

Science.gov (United States)

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

Linking structural features of protein complexes and biological function.

Science.gov (United States)

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.

Interconnected networks

CERN Document Server

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.

Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

Directory of Open Access Journals (Sweden)

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.

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

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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.

Real-time hypothesis driven feature extraction on parallel processing architectures

DEFF Research Database (Denmark)

Granmo, O.-C.; Jensen, Finn Verner

2002-01-01

the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

Classification of interstitial lung disease patterns with topological texture features

Science.gov (United States)

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.

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Qingshan Liu

2017-12-01

Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

Predicting Hidden Links in Supply Networks

Directory of Open Access Journals (Sweden)

A. Brintrup

2018-01-01

Full Text Available Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP method to infer supplier interdependencies using the manufacturer’s incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i number of existing supplier links, (ii overlaps between supplier product portfolios, (iii product outsourcing associations, and (iv likelihood of buyers purchasing from two suppliers together. Naïve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximised by (i and (iii. The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area.

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

Science.gov (United States)

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; van Uden, Inge W M; Sanchez, Clara I; Litjens, Geert; de Leeuw, Frank-Erik; van Ginneken, Bram; Marchiori, Elena; Platel, Bram

2017-07-11

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

Bayesian Networks An Introduction

CERN Document Server

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

Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation

Directory of Open Access Journals (Sweden)

Yeqiang Shu

2012-01-01

Full Text Available The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results.

Cascaded Subpatch Networks for Effective CNNs.

Science.gov (United States)

Jiang, Xiaoheng; Pang, Yanwei; Sun, Manli; Li, Xuelong

2017-05-12

Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size Hx W (typically, H is small and is equal to W, e.g., H is 5 or 7 ). Generally, the size of the filter is equal to the size Hx W of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size hx w is smaller than Hx W . The proposed subpatch filter consists of two subsequent filters. The first one is a linear filter of spatial size hx w and is aimed at extracting features from spatial domain. The second one is of spatial size 1x 1 and is used for strengthening the connection between different input feature channels and for reducing the number of parameters. The subpatch filter convolves with the input patch and the resulting network is called a subpatch network. Taking the output of one subpatch network as input, we further repeat constructing subpatch networks until the output contains only one neuron in spatial domain. These subpatch networks form a new network called the cascaded subpatch network (CSNet). The feature layer generated by CSNet is called the csconv layer. For the whole input image, we construct a deep neural network by stacking a sequence of csconv layers. Experimental results on five benchmark data sets demonstrate the effectiveness and compactness of the proposed CSNet. For example, our CSNet reaches a test error of 5.68% on the CIFAR10 data set without model averaging. To the best of our knowledge, this is the best result ever obtained on the CIFAR10 data set.

Method and system for mesh network embedded devices

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

Exploiting The Brain’s Network Structure in Identifying ADHD

Directory of Open Access Journals (Sweden)

Soumyabrata eDey

2012-11-01

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

Fabric defect detection based on visual saliency using deep feature and low-rank recovery

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Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

2018-04-01

Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

Evolving phenotypic networks in silico.

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François, Paul

2014-11-01

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

In-network adaptation of SHVC video in software-defined networks

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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

Organization of excitable dynamics in hierarchical biological networks.

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Mark Müller-Linow

Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

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Xinbo Ai

2014-11-01

Full Text Available Topological measures are crucial to describe, classify and understand complex networks. Lots of measures are proposed to characterize specific features of specific networks, but the relationships among these measures remain unclear. Taking into account that pulling networks from different domains together for statistical analysis might provide incorrect conclusions, we conduct our investigation with data observed from the same network in the form of simultaneously measured time series. We synthesize a transfer entropy-based framework to quantify the relationships among topological measures, and then to provide a holistic scenario of these measures by inferring a drive-response network. Techniques from Symbolic Transfer Entropy, Effective Transfer Entropy, and Partial Transfer Entropy are synthesized to deal with challenges such as time series being non-stationary, finite sample effects and indirect effects. We resort to kernel density estimation to assess significance of the results based on surrogate data. The framework is applied to study 20 measures across 2779 records in the Technology Exchange Network, and the results are consistent with some existing knowledge. With the drive-response network, we evaluate the influence of each measure by calculating its strength, and cluster them into three classes, i.e., driving measures, responding measures and standalone measures, according to the network communities.

Benford's Law Applies to Online Social Networks.

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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.

Benford's Law Applies to Online Social Networks.

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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.

Features fusion based approach for handwritten Gujarati character recognition

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Ankit Sharma

2017-02-01

Full Text Available Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN, Support Vector Machine (SVM and Naive Bayes (NB classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

Application of a neural network for reflectance spectrum classification

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Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

Facial expression recognition in the wild based on multimodal texture features

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Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

2016-11-01

Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

DEFF Research Database (Denmark)

Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

2008-01-01

is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology......Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...

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

DEFF Research Database (Denmark)

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...

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

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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.

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

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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

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

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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.

A new feature constituting approach to detection of vocal fold pathology

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Hariharan, M.; Polat, Kemal; Yaacob, Sazali

2014-08-01

In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.

MODA: an efficient algorithm for network motif discovery in biological networks.

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Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali

2009-10-01

In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

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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.

Research on improving image recognition robustness by combining multiple features with associative memory

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Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

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

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

2017-01-01

In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, η p 2  = 0.070) and p = 0.001 (β = - 0.264, η p 2  = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

Network structure and institutional complexity in an ecology of water management games

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Mark Lubell

2014-12-01

Full Text Available Social-ecological systems are governed by a complex of ecology of games featuring multiple actors, policy institutions, and issues, and not just single institutions operating in isolation. We update Long's (1958 ecology of games to analyze the coordinating roles of actors and institutions in the context of the ecology of water management games in San Francisco Bay, California. The ecology of games is operationalized as a bipartite network with actors participating in institutions, and exponential random graph models are used to test hypotheses about the structural features of the network. We found that policy coordination is facilitated mostly by federal and state agencies and collaborative institutions that span geographic boundaries. Network configurations associated with closure show the most significant departures from the predicted model values, consistent with the Berardo and Scholz (2010 "risk hypothesis" that closure is important for solving cooperation problems.

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

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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.

Significance of connective tissue diseases features in pulmonary fibrosis

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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.

An Algorithm Based on the Self-Organized Maps for the Classification of Facial Features

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Gheorghe Gîlcă

2015-12-01

Full Text Available This paper deals with an algorithm based on Self Organized Maps networks which classifies facial features. The proposed algorithm can categorize the facial features defined by the input variables: eyebrow, mouth, eyelids into a map of their grouping. The groups map is based on calculating the distance between each input vector and each output neuron layer , the neuron with the minimum distance being declared winner neuron. The network structure consists of two levels: the first level contains three input vectors, each having forty-one values, while the second level contains the SOM competitive network which consists of 100 neurons. The proposed system can classify facial features quickly and easily using the proposed algorithm based on SOMs.

Multiplex congruence network of natural numbers.

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Yan, Xiao-Yong; Wang, Wen-Xu; Chen, Guan-Rong; Shi, Ding-Hua

2016-03-31

Congruence theory has many applications in physical, social, biological and technological systems. Congruence arithmetic has been a fundamental tool for data security and computer algebra. However, much less attention was devoted to the topological features of congruence relations among natural numbers. Here, we explore the congruence relations in the setting of a multiplex network and unveil some unique and outstanding properties of the multiplex congruence network. Analytical results show that every layer therein is a sparse and heterogeneous subnetwork with a scale-free topology. Counterintuitively, every layer has an extremely strong controllability in spite of its scale-free structure that is usually difficult to control. Another amazing feature is that the controllability is robust against targeted attacks to critical nodes but vulnerable to random failures, which also differs from ordinary scale-free networks. The multi-chain structure with a small number of chain roots arising from each layer accounts for the strong controllability and the abnormal feature. The multiplex congruence network offers a graphical solution to the simultaneous congruences problem, which may have implication in cryptography based on simultaneous congruences. Our work also gains insight into the design of networks integrating advantages of both heterogeneous and homogeneous networks without inheriting their limitations.

SOCIAL NETWORK EFFECTS ON ROMANTIC RELATIONSHIP

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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.

Fall Detection Using Smartphone Audio Features.

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Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

Cut Based Method for Comparing Complex Networks.

Science.gov (United States)

Liu, Qun; Dong, Zhishan; Wang, En

2018-03-23

Revealing the underlying similarity of various complex networks has become both a popular and interdisciplinary topic, with a plethora of relevant application domains. The essence of the similarity here is that network features of the same network type are highly similar, while the features of different kinds of networks present low similarity. In this paper, we introduce and explore a new method for comparing various complex networks based on the cut distance. We show correspondence between the cut distance and the similarity of two networks. This correspondence allows us to consider a broad range of complex networks and explicitly compare various networks with high accuracy. Various machine learning technologies such as genetic algorithms, nearest neighbor classification, and model selection are employed during the comparison process. Our cut method is shown to be suited for comparisons of undirected networks and directed networks, as well as weighted networks. In the model selection process, the results demonstrate that our approach outperforms other state-of-the-art methods with respect to accuracy.

A Novel Connectionist Network for Solving Long Time-Lag Prediction Tasks

Science.gov (United States)

Johnson, Keith; MacNish, Cara

Traditional Recurrent Neural Networks (RNNs) perform poorly on learning tasks involving long time-lag dependencies. More recent approaches such as LSTM and its variants significantly improve on RNNs ability to learn this type of problem. We present an alternative approach to encoding temporal dependencies that associates temporal features with nodes rather than state values, where the nodes explicitly encode dependencies over variable time delays. We show promising results comparing the network's performance to LSTM variants on an extended Reber grammar task.

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

KAUST Repository

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