Roughness induced transition and heat transfer augmentation in hypersonic environments

NASA Astrophysics Data System (ADS)

Wassel, A. T.; Shih, W. C. L.; Courtney, J. F.

Boundary layer transition and surface heating distributions on graphite, fine weave carbon-carbon, and metallic nosetip materials were derived from surface temperature responses measured in nitrogen environments during both free-flight and track-guided testing in hypersonic environments. Innovative test procedures were developed, and heat transfer results were validated against established theory through experiments using a super-smooth tungsten model. Quantitative definitions of mean transition front locations were established by deriving heat flux distributions from measured temperatures, and comparisons made with existing nosetip transition correlations. Qualitative transition locations were inferred directly from temperature distributions to investigate preferred orientations on fine weave nosetips. Levels of roughness augmented heat transfer were generally shown to be below values predicted by state-of-the-art methods.

Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

PubMed

Witoonchart, Peerajak; Chongstitvatana, Prabhas

2017-08-01

In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Effects of a Dynamic Spectrum Access Overlay in LTE-Advanced Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juan D. Deaton; Ryan E. lrwin; Luiz A. DaSilva

As early as 2014, wireless network operators spectral capacity will be overwhelmed by a data tsunami brought on by new devices and applications. To augment spectral capacity, operators could deploy a Dynamic Spectrum Access (DSA) overlay. In the light of the many planned Long Term Evolution (LTE) network deployments, the affects of a DSA overlay have not been fully considered into the existing LTE standards. Coalescing many different aspects of DSA, this paper develops the Spectrum Accountability (SA) framework. The SA framework defines specific network element functionality, protocol interfaces, and signaling flow diagrams for LTE to support service requests andmore » enforce rights of responsibilities of primary and secondary users, respectively. We also include a network simulation to quantify the benefits of using DSA channels to augment capacity. Based on our simulation we show that, network operators can benefit up to %40 increase in operating capacity when sharing DSA bands to augment spectral capacity. With our framework, this paper could serve as an guide in developing future LTE network standards that include DSA.« less

Chest x-ray generation and data augmentation for cardiovascular abnormality classification

NASA Astrophysics Data System (ADS)

Madani, Ali; Moradi, Mehdi; Karargyris, Alexandros; Syeda-Mahmood, Tanveer

2018-03-01

Medical imaging datasets are limited in size due to privacy issues and the high cost of obtaining annotations. Augmentation is a widely used practice in deep learning to enrich the data in data-limited scenarios and to avoid overfitting. However, standard augmentation methods that produce new examples of data by varying lighting, field of view, and spatial rigid transformations do not capture the biological variance of medical imaging data and could result in unrealistic images. Generative adversarial networks (GANs) provide an avenue to understand the underlying structure of image data which can then be utilized to generate new realistic samples. In this work, we investigate the use of GANs for producing chest X-ray images to augment a dataset. This dataset is then used to train a convolutional neural network to classify images for cardiovascular abnormalities. We compare our augmentation strategy with traditional data augmentation and show higher accuracy for normal vs abnormal classification in chest X-rays.

Experimental Investigation of Roughness Effects on Transition Onset and Turbulent Heating Augmentation on a Hemisphere at Mach 6 and Mach 10

NASA Technical Reports Server (NTRS)

Hollis, Brian R.

2017-01-01

An experimental investigation of the effects of distributed surface roughness on boundary-layer transition and turbulent heating has been conducted. Hypersonic wind tunnel testing was performed using hemispherical models with surface roughness patterns simulating those produced by heat shield ablation. Global aeroheating and transition onset data were obtained using phosphor thermography at Mach 6 and Mach 10 over a range of roughness heights and free stream Reynolds numbers sufficient to produce laminar, transitional and turbulent flow. Upstream movement of the transition onset location and increasing heating augmentation over predicted smooth-wall levels were observed with both increasing roughness heights and increasing free stream Reynolds numbers. The experimental heating data are presented herein, as are comparisons to smooth-wall heat transfer distributions from computational flow-field simulations. The transition onset data are also tabulated, and correlations of these data are presented.

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

NASA Astrophysics Data System (ADS)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

Supersonic/Hypersonic Correlations for In-Cavity Transition and Heating Augmentation

NASA Technical Reports Server (NTRS)

Everhart, Joel L.

2011-01-01

Laminar-entry cavity heating data with a non-laminar boundary layer exit flow have been retrieved from the database developed at Mach 6 and 10 in air on large flat plate models for the Space Shuttle Return-To-Flight Program. Building on previously published fully laminar and fully turbulent analysis methods, new descriptive correlations of the in-cavity floor-averaged heating and endwall maximum heating have been developed for transitional-to-turbulent exit flow. These new local-cavity correlations provide the expected flow and geometry conditions for transition onset; they provide the incremental heating augmentation induced by transitional flow; and, they provide the transitional-to-turbulent exit cavity length. Furthermore, they provide an upper application limit for the previously developed fully-laminar heating correlations. An example is provided that demonstrates simplicity of application. Heating augmentation factors of 12 and 3 above the fully laminar values are shown to exist on the cavity floor and endwall, respectively, if the flow exits in fully tripped-to-turbulent boundary layer state. Cavity floor heating data in geometries installed on the windward surface of 0.075-scale Shuttle wind tunnel models have also been retrieved from the boundary layer transition database developed for the Return-To-Flight Program. These data were independently acquired at Mach 6 and Mach 10 in air, and at Mach 6 in CF4. The correlation parameters for the floor-averaged heating have been developed and they offer an exceptionally positive comparison to previously developed laminar-cavity heating correlations. Non-laminar increments have been extracted from the Shuttle data and they fall on the newly developed transitional in-cavity correlations, and they are bounded by the 95% correlation prediction limits. Because the ratio of specific heats changes along the re-entry trajectory, turning angle into a cavity and boundary layer flow properties may be affected, raising concerns regarding the application validity of the heating augmentation predictions.

Advanced intellect-augmentation techniques

NASA Technical Reports Server (NTRS)

Engelbart, D. C.

1972-01-01

User experience in applying our augmentation tools and techniques to various normal working tasks within our center is described so as to convey a subjective impression of what it is like to work in an augmented environment. It is concluded that working-support, computer-aid systems for augmenting individuals and teams, are undoubtedly going to be widely developed and used. A very special role in this development is seen for multi-access computer networks.

Enhancing Education through Mobile Augmented Reality

ERIC Educational Resources Information Center

Joan, D. R. Robert

2015-01-01

In this article, the author has discussed about the Mobile Augmented Reality and enhancing education through it. The aim of the present study was to give some general information about mobile augmented reality which helps to boost education. Purpose of the current study reveals the mobile networks which are used in the institution campus as well…

Perceptions of the roles of social networking in simulation augmented medical education and training.

PubMed

Martin, Rob; Rojas, David; Cheung, Jeffrey J H; Weber, Bryce; Kapralos, Bill; Dubrowski, Adam

2013-01-01

Simulation-augmented education and training (SAET) is an expensive educational tool that may be facilitated through social networking technologies or Computer Supported Collaborative Learning (CSCL). This study examined the perceptions of medical undergraduates participating in SAET for knot tying skills to identify perceptions and barriers to implementation of social networking technologies within a broader medical education curriculum. The majority of participants (89%) found CSCL aided their learning of the technical skill and identified privacy and accessibility as major barriers to the tools implementation.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Augmented Lagrange Hopfield network for solving economic dispatch problem in competitive environment

NASA Astrophysics Data System (ADS)

Vo, Dieu Ngoc; Ongsakul, Weerakorn; Nguyen, Khai Phuc

2012-11-01

This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem in the competitive environment. The proposed ALHN is a continuous Hopfield network with its energy function based on augmented Lagrange function for efficiently dealing with constrained optimization problems. The ALHN method can overcome the drawbacks of the conventional Hopfield network such as local optimum, long computational time, and linear constraints. The proposed method is used for solving the ED problem with two revenue models of revenue based on payment for power delivered and payment for reserve allocated. The proposed ALHN has been tested on two systems of 3 units and 10 units for the two considered revenue models. The obtained results from the proposed methods are compared to those from differential evolution (DE) and particle swarm optimization (PSO) methods. The result comparison has indicated that the proposed method is very efficient for solving the problem. Therefore, the proposed ALHN could be a favorable tool for ED problem in the competitive environment.

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

Two Stage Data Augmentation for Low Resourced Speech Recognition (Author’s Manuscript)

DTIC Science & Technology

2016-09-12

speech recognition, deep neural networks, data augmentation 1. Introduction When training data is limited—whether it be audio or text—the obvious...Schwartz, and S. Tsakalidis, “Enhancing low resource keyword spotting with au- tomatically retrieved web documents,” in Interspeech, 2015, pp. 839–843. [2...and F. Seide, “Feature learning in deep neural networks - a study on speech recognition tasks,” in International Conference on Learning Representations

Performance Evaluation of a SLA Negotiation Control Protocol for Grid Networks

NASA Astrophysics Data System (ADS)

Cergol, Igor; Mirchandani, Vinod; Verchere, Dominique

A framework for an autonomous negotiation control protocol for service delivery is crucial to enable the support of heterogeneous service level agreements (SLAs) that will exist in distributed environments. We have first given a gist of our augmented service negotiation protocol to support distinct service elements. The augmentations also encompass related composition of the services and negotiation with several service providers simultaneously. All the incorporated augmentations will enable to consolidate the service negotiation operations for telecom networks, which are evolving towards Grid networks. Furthermore, our autonomous negotiation protocol is based on a distributed multi-agent framework to create an open market for Grid services. Second, we have concisely presented key simulation results of our work in progress. The results exhibit the usefulness of our negotiation protocol for realistic scenarios that involves different background traffic loading, message sizes and traffic flow asymmetry between background and negotiation traffics.

NASA Communications Augmentation network

NASA Technical Reports Server (NTRS)

Omidyar, Guy C.; Butler, Thomas E.; Laios, Straton C.

1990-01-01

The NASA Communications (Nascom) Division of the Mission Operations and Data Systems Directorate (MO&DSD) is to undertake a major initiative to develop the Nascom Augmentation (NAUG) network to achieve its long-range service objectives for operational data transport to support the Space Station Freedom Program, the Earth Observing System (EOS), and other projects. The NAUG is the Nascom ground communications network being developed to accommodate the operational traffic of the mid-1990s and beyond. The NAUG network development will be based on the Open Systems Interconnection Reference Model (OSI-RM). This paper describes the NAUG network architecture, subsystems, topology, and services; addresses issues of internetworking the Nascom network with other elements of the Space Station Information System (SSIS); discusses the operations environment. This paper also notes the areas of related research and presents the current conception of how the network will provide broadband services in 1998.

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

NASA Technical Reports Server (NTRS)

Soloway, Don; Haley, Pam

2004-01-01

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

On the Latent Variable Interpretation in Sum-Product Networks.

PubMed

Peharz, Robert; Gens, Robert; Pernkopf, Franz; Domingos, Pedro

2017-10-01

One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

PubMed

Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

2017-02-01

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

Wetting transitions on patterned surfaces with diffuse interaction potentials embedded in a Young-Laplace formulation

NASA Astrophysics Data System (ADS)

Pashos, G.; Kokkoris, G.; Papathanasiou, A. G.; Boudouvis, A. G.

2016-01-01

The Minimum Energy Paths (MEPs) of wetting transitions on pillared surfaces are computed with the Young-Laplace equation, augmented with a pressure term that accounts for liquid-solid interactions. The interactions are smoothed over a short range from the solid phase, therefore facilitating the numerical solution of problems concerning wetting on complex surface patterns. The patterns may include abrupt geometric features, e.g., arrays of rectangular pillars, where the application of the unmodified Young-Laplace is not practical. The MEPs are obtained by coupling the augmented Young-Laplace with the modified string method from which the energy barriers of wetting transitions are eventually extracted. We demonstrate the method on a wetting transition that is associated with the breakdown of superhydrophobic behavior, i.e., the transition from the Cassie-Baxter state to the Wenzel state, taking place on a superhydrophobic pillared surface. The computed energy barriers quantify the resistance of the system to these transitions and therefore, they can be used to evaluate superhydrophobic performance or provide guidelines for optimal pattern design.

Design and Test of Pseudorandom Number Generator Using a Star Network of Lorenz Oscillators

NASA Astrophysics Data System (ADS)

Cho, Kenichiro; Miyano, Takaya

We have recently developed a chaos-based stream cipher based on augmented Lorenz equations as a star network of Lorenz subsystems. In our method, the augmented Lorenz equations are used as a pseudorandom number generator. In this study, we propose a new method based on the augmented Lorenz equations for generating binary pseudorandom numbers and evaluate its security using the statistical tests of SP800-22 published by the National Institute for Standards and Technology in comparison with the performances of other chaotic dynamical models used as binary pseudorandom number generators. We further propose a faster version of the proposed method and evaluate its security using the statistical tests of TestU01 published by L’Ecuyer and Simard.

Augmenting groundwater monitoring networks near landfills with slurry cutoff walls.

PubMed

Hudak, Paul F

2004-01-01

This study investigated the use of slurry cutoff walls in conjunction with monitoring wells to detect contaminant releases from a solid waste landfill. The 50 m wide by 75 m long landfill was oriented oblique to regional groundwater flow in a shallow sand aquifer. Computer models calculated flow fields and the detection capability of six monitoring networks, four including a 1 m wide by 50 m long cutoff wall at various positions along the landfill's downgradient boundaries and upgradient of the landfill. Wells were positioned to take advantage of convergent flow induced downgradient of the cutoff walls. A five-well network with no cutoff wall detected 81% of contaminant plumes originating within the landfill's footprint before they reached a buffer zone boundary located 50 m from the landfill's downgradient corner. By comparison, detection efficiencies of networks augmented with cutoff walls ranged from 81 to 100%. The most efficient network detected 100% of contaminant releases with four wells, with a centrally located, downgradient cutoff wall. In general, cutoff walls increased detection efficiency by delaying transport of contaminant plumes to the buffer zone boundary, thereby allowing them to increase in size, and by inducing convergent flow at downgradient areas, thereby funneling contaminant plumes toward monitoring wells. However, increases in detection efficiency were too small to offset construction costs for cutoff walls. A 100% detection efficiency was also attained by an eight-well network with no cutoff wall, at approximately one-third the cost of the most efficient wall-augmented network.

Image Augmentation for Object Image Classification Based On Combination of Pre-Trained CNN and SVM

NASA Astrophysics Data System (ADS)

Shima, Yoshihiro

2018-04-01

Neural networks are a powerful means of classifying object images. The proposed image category classification method for object images combines convolutional neural networks (CNNs) and support vector machines (SVMs). A pre-trained CNN, called Alex-Net, is used as a pattern-feature extractor. Alex-Net is pre-trained for the large-scale object-image dataset ImageNet. Instead of training, Alex-Net, pre-trained for ImageNet is used. An SVM is used as trainable classifier. The feature vectors are passed to the SVM from Alex-Net. The STL-10 dataset are used as object images. The number of classes is ten. Training and test samples are clearly split. STL-10 object images are trained by the SVM with data augmentation. We use the pattern transformation method with the cosine function. We also apply some augmentation method such as rotation, skewing and elastic distortion. By using the cosine function, the original patterns were left-justified, right-justified, top-justified, or bottom-justified. Patterns were also center-justified and enlarged. Test error rate is decreased by 0.435 percentage points from 16.055% by augmentation with cosine transformation. Error rates are increased by other augmentation method such as rotation, skewing and elastic distortion, compared without augmentation. Number of augmented data is 30 times that of the original STL-10 5K training samples. Experimental test error rate for the test 8k STL-10 object images was 15.620%, which shows that image augmentation is effective for image category classification.

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

PubMed

Zhang, J W; Rangan, A V

2015-04-01

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

PubMed

Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng

2015-03-01

This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Performance Of The IEEE 802.15.4 Protocol As The Marker Of Augmented Reality In Museum

NASA Astrophysics Data System (ADS)

Kurniawan Saputro, Adi; Sumpeno, Surya; Hariadi, Mochamad

2018-04-01

Museum is a place to keep the historic objects and historical education center to introduce the nation’s culture. Utilizing technology in a museum to become a smart city is a challenge. Internet of thing (IOT) is a technological advance in Information and communication (ICT) that can be applied in the museum The current ICT development is not only a transmission medium, but Augmented Reality technology is also being developed. Currently, Augmented Reality technology creates virtual objects into the real world using markers or images. In this study, researcher used signals to make virtual objects appear in the real world using the IEEE 802.14.5 protocol replacing the Augmented Reality marker. RSSI and triangulation are used as a substitute microlocation for AR objects. The result is the performance of Wireless Sensor Network could be used for data transmission in the museum. LOS research at a distance of 15 meters with 1000 ms delay found 1.4% error rate and NLOS with 2.3% error rate. So it can be concluded that utilization technology (IOT) using signal wireless sensor network as a replace for marker augmented reality can be used in museum

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

FireFly: reconfigurable optical wireless networking data centers

NASA Astrophysics Data System (ADS)

Kavehrad, Mohsen; Deng, Peng; Gupta, H.; Longtin, J.; Das, S. R.; Sekar, V.

2017-01-01

We explore a novel, free-space optics based approach for building data center interconnects. Data centers (DCs) are a critical piece of today's networked applications in both private and public sectors. The key factors that have driven this trend are economies of scale, reduced management costs, better utilization of hardware via statistical multiplexing, and the ability to elastically scale applications in response to changing workload patterns. A robust DC network fabric is fundamental to the success of DCs and to ensure that the network does not become a bottleneck for high-performance applications. In this context, DC network design must satisfy several goals: high performance (e.g., high throughput and low latency), low equipment and management cost, robustness to dynamic traffic patterns, incremental expandability to add new servers or racks, and other practical concerns such as cabling complexity, and power and cooling costs. Current DC network architectures do not seem to provide a satisfactory solution, with respect to the above requirements. In particular, traditional static (wired) networks are either overprovisioned or oversubscribed. Recent works have tried to overcome the above limitations by augmenting a static (wired) "core" with some flexible links (RF-wireless or optical). These augmented architectures show promise, but offer only incremental improvement in performance. Specifically, RFwireless based augmented solutions also offer only limited performance improvement, due to inherent interference and range constraints of RF links. This paper explores an alternative design point—a fully flexible and all-wireless DC interrack network based on free-space optical (FSO) links. We call this FireFly as in; Free-space optical Inter-Rack nEtwork with high FLexibilitY. We will present our designs and tests using various configurations that can help the performance and reliability of the FSO links.

Nonequilibrium transitions in complex networks: A model of social interaction

NASA Astrophysics Data System (ADS)

Klemm, Konstantin; Eguíluz, Víctor M.; Toral, Raúl; San Miguel, Maxi

2003-02-01

We analyze the nonequilibrium order-disorder transition of Axelrod’s model of social interaction in several complex networks. In a small-world network, we find a transition between an ordered homogeneous state and a disordered state. The transition point is shifted by the degree of spatial disorder of the underlying network, the network disorder favoring ordered configurations. In random scale-free networks the transition is only observed for finite size systems, showing system size scaling, while in the thermodynamic limit only ordered configurations are always obtained. Thus, in the thermodynamic limit the transition disappears. However, in structured scale-free networks, the phase transition between an ordered and a disordered phase is restored.

Molecular phylogenetic trees - On the validity of the Goodman-Moore augmentation algorithm

NASA Technical Reports Server (NTRS)

Holmquist, R.

1979-01-01

A response is made to the reply of Nei and Tateno (1979) to the letter of Holmquist (1978) supporting the validity of the augmentation algorithm of Moore (1977) in reconstructions of nucleotide substitutions by means of the maximum parsimony principle. It is argued that the overestimation of the augmented numbers of nucleotide substitutions (augmented distances) found by Tateno and Nei (1978) is due to an unrepresentative data sample and that it is only necessary that evolution be stochastically uniform in different regions of the phylogenetic network for the augmentation method to be useful. The importance of the average value of the true distance over all links is explained, and the relative variances of the true and augmented distances are calculated to be almost identical. The effects of topological changes in the phylogenetic tree on the augmented distance and the question of the correctness of ancestral sequences inferred by the method of parsimony are also clarified.

The Interaction of Child-Parent Shared Reading with an Augmented Reality (AR) Picture Book and Parents' Conceptions of AR Learning

ERIC Educational Resources Information Center

Cheng, Kun-Hung; Tsai, Chin-Chung

2016-01-01

Following a previous study (Cheng & Tsai, 2014. "Computers & Education"), this study aimed to probe the interaction of child-parent shared reading with the augmented reality (AR) picture book in more depth. A series of sequential analyses were thus conducted to infer the behavioral transition diagrams and visualize the continuity…

Brain network dynamics in the human articulatory loop.

PubMed

Nishida, Masaaki; Korzeniewska, Anna; Crone, Nathan E; Toyoda, Goichiro; Nakai, Yasuo; Ofen, Noa; Brown, Erik C; Asano, Eishi

2017-08-01

The articulatory loop is a fundamental component of language function, involved in the short-term buffer of auditory information followed by its vocal reproduction. We characterized the network dynamics of the human articulatory loop, using invasive recording and stimulation. We measured high-gamma activity 70-110 Hz recorded intracranially when patients with epilepsy either only listened to, or listened to and then reproduced two successive tones by humming. We also conducted network analyses, and analyzed behavioral responses to cortical stimulation. Presentation of the initial tone elicited high-gamma augmentation bilaterally in the superior-temporal gyrus (STG) within 40ms, and in the precentral and inferior-frontal gyri (PCG and IFG) within 160ms after sound onset. During presentation of the second tone, high-gamma augmentation was reduced in STG but enhanced in IFG. The task requiring tone reproduction further enhanced high-gamma augmentation in PCG during and after sound presentation. Event-related causality (ERC) analysis revealed dominant flows within STG immediately after sound onset, followed by reciprocal interactions involving PCG and IFG. Measurement of cortico-cortical evoked-potentials (CCEPs) confirmed connectivity between distant high-gamma sites in the articulatory loop. High-frequency stimulation of precentral high-gamma sites in either hemisphere induced speech arrest, inability to control vocalization, or forced vocalization. Vocalization of tones was accompanied by high-gamma augmentation over larger extents of PCG. Bilateral PCG rapidly and directly receives feed-forward signals from STG, and may promptly initiate motor planning including sub-vocal rehearsal for short-term buffering of auditory stimuli. Enhanced high-gamma augmentation in IFG during presentation of the second tone may reflect high-order processing of the tone sequence. The articulatory loop employs sustained reciprocal propagation of neural activity across a network of cortical sites with strong neurophysiological connectivity. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Frequency-selective augmenting responses by short-term synaptic depression in cat neocortex

PubMed Central

Houweling, Arthur R; Bazhenov, Maxim; Timofeev, Igor; Grenier, François; Steriade, Mircea; Sejnowski, Terrence J

2002-01-01

Thalamic stimulation at frequencies between 5 and 15 Hz elicits incremental or ‘augmenting’ cortical responses. Augmenting responses can also be evoked in cortical slices and isolated cortical slabs in vivo. Here we show that a realistic network model of cortical pyramidal cells and interneurones including short-term plasticity of inhibitory and excitatory synapses replicates the main features of augmenting responses as obtained in isolated slabs in vivo. Repetitive stimulation of synaptic inputs at frequencies around 10 Hz produced postsynaptic potentials that grew in size and carried an increasing number of action potentials resulting from the depression of inhibitory synaptic currents. Frequency selectivity was obtained through the relatively weak depression of inhibitory synapses at low frequencies, and strong depression of excitatory synapses together with activation of a calcium-activated potassium current at high frequencies. This network resonance is a consequence of short-term synaptic plasticity in a network of neurones without intrinsic resonances. These results suggest that short-term plasticity of cortical synapses could shape the dynamics of synchronized oscillations in the brain. PMID:12122156

Factors That Affect Faculty Attitudes toward Adoption of Technology-Rich Blended Learning

ERIC Educational Resources Information Center

Moukali, Khalid Hussain

2012-01-01

Universities worldwide are transitioning to blended learning where technology is used to enhance and augment traditional face-to-face instruction. Investigation of how well blended learning strategies are accepted and adopted in multicultural settings is needed to facilitate this transition. This study investigated factors and barriers that…

Technology for Transition and Postsecondary Success: Supporting Executive Function

ERIC Educational Resources Information Center

Hayes, Gillian; Hosaflook, Stephen

2015-01-01

This six-page (tri-fold) laminated reference guide by Gillian Hayes and Stephen Hosaflook focuses on readily available tools for augmenting and supporting the development of executive function skills, such as time and task management, organization, and self-regulation. These skills are crucial for accomplishing a variety of transition-related…

Augmenting the Refutation Text Effect with Analogies and Graphics

ERIC Educational Resources Information Center

Danielson, Robert W.; Sinatra, Gale M.; Kendeou, Panayiota

2016-01-01

Refutation texts have been shown to be effective at promoting knowledge revision. It has been suggested that refutation texts are most effective when the misconception and the correct information are co-activated and integrated with causal networks that support the correct information. We explored two augmentations to a refutation text that might…

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

The Effects of a Dynamic Spectrum Access Overlay in LTE-Advanced Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juan D. Deaton; Ryan E. Irwin; Luiz A. DaSilva

As early as 2014, mobile network operators’ spectral capacity will be overwhelmed by the demand brought on by new devices and applications. To augment capacity and meet this demand, operators may choose to deploy a Dynamic Spectrum Access (DSA) overlay. The signaling and functionality required by such an overlay have not yet been fully considered in the architecture of the planned Long Term Evolution Advanced (LTE+) networks. This paper presents a Spectrum Accountability framework to be integrated into LTE+ architectures, defining specific element functionality, protocol interfaces, and signaling flow diagrams required to enforce the rights and responsibilities of primary andmore » secondary users. We also quantify, through integer programs, the benefits of using DSA channels to augment capacity under a scenario in which LTE+ network can opportunistically use TV and GSM spectrum. The framework proposed here may serve as a guide in the development of future LTE+ network standards that account for DSA.« less

Academics and Social Networking Sites: Benefits, Problems and Tensions in Professional Engagement with Online Networking

ERIC Educational Resources Information Center

Jordan, Katy; Weller, Martin

2018-01-01

The web has had a profound effect on the ways people interact, with online social networks arguably playing an important role in changing or augmenting how we connect with others. However, uptake of online social networking by the academic community varies, and needs to be understood. This paper presents an independent, novel analysis of a…

Safety models incorporating graph theory based transit indicators.

PubMed

Quintero, Liliana; Sayed, Tarek; Wahba, Mohamed M

2013-01-01

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Understanding transit ridership demand for a multi-destination, multimodal transit network in an American metropolitan area : lessons for increasing choice ridership while maintaining transit dependent ridership.

DOT National Transportation Integrated Search

2012-01-01

This study examines the factors underlying transit demand in the multi-destination, integrated bus and rail transit network for Atlanta, Georgia. Atlanta provides an opportunity to explore the consequences of a multi-destination transit network for b...

Security Sector Reform: A Case Study Approach to Transition and Capacity Building

DTIC Science & Technology

2010-01-01

African Unity in Cotonou , Benin, and agreed on a ceasefire effective on August 1, 1993, as well as the establishment of a National Transitional...Observer Mission in Liberia (UNOMIL), which augmented ECOMOG in implementing the Cotonou Agreement. UNOMIL’s initial mandate included both a...transitional government, comprised of the three warring parties, to prepare for general elections. 50 Despite the optimism surrounding Cotonou , an uneasy

Augmented Performance Environment for Enhancing Interagency Coordination in Stability, Security, Transition, and Reconstruction (SSTR) Operations: Phase 2

DTIC Science & Technology

2010-12-01

U.S. Army Research Institute for the Behavioral and Social Sciences Research Report 1934 Augmented performance...release; distribution is unlimited. U.S. Army Research Institute for the Behavioral and Social Sciences Department of the Army Deputy...distribution of reports to: U.S. Army Research Institute for the Behavioral and Social Sciences, Attn: DAPE-ARI-ZXM, 2511 Jefferson Davis Highway, Arlington

NASA/SPAN and DOE/ESnet-DECnet transition strategy for DECnet OSI/phase 5

NASA Technical Reports Server (NTRS)

Porter, Linda; Demar, Phil

1991-01-01

The technical issues are examined involved with the transition of very large DECnet networks from DECnet phase IV protocols to DECnet OSI/Phase V protocols. The networks involved are the NASA's Science Internet (NSI-DECnet) and the DOE's Energy Science network (ESnet-DECnet). These networks, along with the many universities and research institutions connected to them, combine to form a single DECnet network containing more than 20,000 transitions and crossing numerous organizational boundaries. Discussion of transition planning, including decisions about Phase V naming, addressing, and routing are presented. Also discussed are transition issues related to the use of non-DEC routers in the network.

Traffic sign classification with dataset augmentation and convolutional neural network

NASA Astrophysics Data System (ADS)

Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

2018-04-01

This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

NASA Technical Reports Server (NTRS)

Shin, Yoonghyun; Calise, Anthony J.; Motter, Mark A.

2005-01-01

This paper summarizes the application of two adaptive approaches to autopilot design, and presents an evaluation and comparison of the two approaches in simulation for an unmanned aerial vehicle. One approach employs two-stage dynamic inversion and the other employs feedback dynamic inversions based on a command augmentation system. Both are augmented with neural network based adaptive elements. The approaches permit adaptation to both parametric uncertainty and unmodeled dynamics, and incorporate a method that permits adaptation during periods of control saturation. Simulation results for an FQM-117B radio controlled miniature aerial vehicle are presented to illustrate the performance of the neural network based adaptation.

The design of IPv6's transitional scheme in university

NASA Astrophysics Data System (ADS)

Li, Biqing; Li, Zhao

2017-05-01

According to the current network environment of campus, the specific scheme of network transition is proposed, which has conducted detailed analyses for the basic concepts, the types of address, the necessary technology for transition and the agreement and principle of transition. According to the tunneling technology of IPv6, the IPv4 network and IPv6 network can communicate with each other, and the network of whole campus can operate well.

Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations.

PubMed

Lewis, Joanne M; DiGiacomo, Michelle; Currow, David C; Davidson, Patricia M

2014-01-01

Lower socioeconomic populations live and die in contexts that render them vulnerable to poorer health and wellbeing. Contexts of care at the end of life are overwhelmingly determined by the capacity and nature of formal and informal networks and relations to support care. To date, studies exploring the nature of networks and relations of support in lower socioeconomic populations at the end of life are absent. This qualitative study sought to identify the nature of individual, community and civic networks and relations that defined the contexts of care for this group. Semi-structured qualitative interviews were conducted with 16 patients and 6 informal carers who identified that they had social and economic needs and were from a lower socioeconomic area. A social capital questionnaire identifying individual, community and civic networks and relations formed the interview guide. Interviews were audio-taped, transcribed and analysed using framework analysis. Participants identified that individual and community networks and relations of support were mainly inadequate to meet care needs. Specifically, data revealed: (1) individual (informal caregivers) networks and relations were small and fragile due to the nature of conflict and crisis; (2) community trust and engagement was limited and shifted by illness and caregiving; (3) and formal care services were inconsistent and provided limited practical support. Some transitions in community relations for support were noted. Levels of civic and government engagement and support were overall positive and enabled access to welfare resources. Networks and relations of support are essential for ensuring quality end of life care is achieved. Lower socioeconomic groups are at a distinct disadvantage where these networks and relations are limited, as they lack the resources necessary to augment these gaps. Understanding of the nature of assets and limitations, in networks and relations of support, is necessary to inform interventions to improve end of life care for lower socioeconomic populations.

Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations

PubMed Central

2014-01-01

Background Lower socioeconomic populations live and die in contexts that render them vulnerable to poorer health and wellbeing. Contexts of care at the end of life are overwhelmingly determined by the capacity and nature of formal and informal networks and relations to support care. To date, studies exploring the nature of networks and relations of support in lower socioeconomic populations at the end of life are absent. This qualitative study sought to identify the nature of individual, community and civic networks and relations that defined the contexts of care for this group. Methods Semi-structured qualitative interviews were conducted with 16 patients and 6 informal carers who identified that they had social and economic needs and were from a lower socioeconomic area. A social capital questionnaire identifying individual, community and civic networks and relations formed the interview guide. Interviews were audio-taped, transcribed and analysed using framework analysis. Results Participants identified that individual and community networks and relations of support were mainly inadequate to meet care needs. Specifically, data revealed: (1) individual (informal caregivers) networks and relations were small and fragile due to the nature of conflict and crisis; (2) community trust and engagement was limited and shifted by illness and caregiving; (3) and formal care services were inconsistent and provided limited practical support. Some transitions in community relations for support were noted. Levels of civic and government engagement and support were overall positive and enabled access to welfare resources. Conclusion Networks and relations of support are essential for ensuring quality end of life care is achieved. Lower socioeconomic groups are at a distinct disadvantage where these networks and relations are limited, as they lack the resources necessary to augment these gaps. Understanding of the nature of assets and limitations, in networks and relations of support, is necessary to inform interventions to improve end of life care for lower socioeconomic populations. PMID:24959101

Transition Strategies for Adolescents and Young Adults Who Use AAC. AAC Series

ERIC Educational Resources Information Center

McNaughton, David B., Ed.; Beukelman, David R., Ed.

2010-01-01

To make a smooth transition to a fulfilling, self-determined adult life, young people who use Augmentative-Alternative Communication (AAC) need effective services that meet their individual needs and make the most of advances in technology. Professionals will provide these critical supports with the help of this book, the first complete guide to…

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network

NASA Astrophysics Data System (ADS)

Funabashi, Masatoshi

We investigate the possible role of intermittent chaotic dynamics called chaotic itinerancy, in interaction with nonsupervised learnings that reinforce and weaken the neural connection depending on the dynamics itself. We first performed hierarchical stability analysis of the Chaotic Neural Network model (CNN) according to the structure of invariant subspaces. Irregular transition between two attractor ruins with positive maximum Lyapunov exponent was triggered by the blowout bifurcation of the attractor spaces, and was associated with riddled basins structure. We secondly modeled two autonomous learnings, Hebbian learning and spike-timing-dependent plasticity (STDP) rule, and simulated the effect on the chaotic itinerancy state of CNN. Hebbian learning increased the residence time on attractor ruins, and produced novel attractors in the minimum higher-dimensional subspace. It also augmented the neuronal synchrony and established the uniform modularity in chaotic itinerancy. STDP rule reduced the residence time on attractor ruins, and brought a wide range of periodicity in emerged attractors, possibly including strange attractors. Both learning rules selectively destroyed and preserved the specific invariant subspaces, depending on the neuron synchrony of the subspace where the orbits are situated. Computational rationale of the autonomous learning is discussed in connectionist perspective.

Fiia: A Model-Based Approach to Engineering Collaborative Augmented Reality

NASA Astrophysics Data System (ADS)

Wolfe, Christopher; Smith, J. David; Phillips, W. Greg; Graham, T. C. Nicholas

Augmented reality systems often involve collaboration among groups of people. While there are numerous toolkits that aid the development of such augmented reality groupware systems (e.g., ARToolkit and Groupkit), there remains an enormous gap between the specification of an AR groupware application and its implementation. In this chapter, we present Fiia, a toolkit which simplifies the development of collaborative AR applications. Developers specify the structure of their applications using the Fiia modeling language, which abstracts details of networking and provides high-level support for specifying adapters between the physical and virtual world. The Fiia.Net runtime system then maps this conceptual model to a runtime implementation. We illustrate Fiia via Raptor, an augmented reality application used to help small groups collaboratively prototype video games.

Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

NASA Astrophysics Data System (ADS)

Fang, Le-Heng; Lin, Wei; Luo, Qiang

In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

Wireless Augmented Reality Communication System

NASA Technical Reports Server (NTRS)

Agan, Martin (Inventor); Devereaux, Ann (Inventor); Jedrey, Thomas (Inventor)

2015-01-01

A portable unit is for video communication to select a user name in a user name network. A transceiver wirelessly accesses a communication network through a wireless connection to a general purpose node coupled to the communication network. A user interface can receive user input to log on to a user name network through the communication network. The user name network has a plurality of user names, at least one of the plurality of user names is associated with a remote portable unit, logged on to the user name network and available for video communication.

Wireless Augmented Reality Communication System

NASA Technical Reports Server (NTRS)

Jedrey, Thomas (Inventor); Agan, Martin (Inventor); Devereaux, Ann (Inventor)

2017-01-01

A portable unit is for video communication to select a user name in a user name network. A transceiver wirelessly accesses a communication network through a wireless connection to a general purpose node coupled to the communication network. A user interface can receive user input to log on to a user name network through the communication network. The user name network has a plurality of user names, at least one of the plurality of user names is associated with a remote portable unit, logged on to the user name network and available for video communication.

On Location Learning: Authentic Applied Science with Networked Augmented Realities

NASA Astrophysics Data System (ADS)

Rosenbaum, Eric; Klopfer, Eric; Perry, Judy

2007-02-01

The learning of science can be made more like the practice of science through authentic simulated experiences. We have created a networked handheld Augmented Reality environment that combines the authentic role-playing of Augmented Realities and the underlying models of Participatory Simulations. This game, known as Outbreak @ The Institute, is played across a university campus where players take on the roles of doctors, medical technicians, and public health experts to contain a disease outbreak. Players can interact with virtual characters and employ virtual diagnostic tests and medicines. They are challenged to identify the source and prevent the spread of an infectious disease that can spread among real and/or virtual characters according to an underlying model. In this paper, we report on data from three high school classes who played the game. We investigate students' perception of the authenticity of the game in terms of their personal embodiment in the game, their experience playing different roles, and their understanding of the dynamic model underlying the game.

Raising the IQ in full-text searching via intelligent querying

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kero, R.; Russell, L.; Swietlik, C.

1994-11-01

Current Information Retrieval (IR) technologies allow for efficient access to relevant information, provided that user selected query terms coincide with the specific linguistical choices made by the authors whose works constitute the text-base. Therefore, the challenge is to enhance the limited searching capability of state-of-the-practice IR. This can be done either with augmented clients that overcome current server searching deficiencies, or with added capabilities that can augment searching algorithms on the servers. The technology being investigated is that of deductive databases, with a set of new techniques called cooperative answering. This technology utilizes semantic networks to allow for navigation betweenmore » possible query search term alternatives. The augmented search terms are passed to an IR engine and the results can be compared. The project utilizes the OSTI Environment, Safety and Health Thesaurus to populate the domain specific semantic network and the text base of ES&H related documents from the Facility Profile Information Management System as the domain specific search space.« less

Passenger flow analysis of Beijing urban rail transit network using fractal approach

NASA Astrophysics Data System (ADS)

Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia

2018-04-01

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Extending the Ground Force Network: Aerial Layer Networking

DTIC Science & Technology

2013-04-25

Additionally aerial layer networks are envisioned to augment the Global Information Grid ( GIG ) access, which is currently provided by the surface...frequencies such as HF, VHF, and UHF. This enabled ground forces to establish tactical wide area networks (WAN) and permitted entry to the GIG ...PRC-117G. Both systems are unique in their overall mission sets, but both provide tactical users access to the WAN and GIG . Self-forming and self

Mars Science Laboratory Heatshield Aerothermodynamics: Design and Reconstruction

NASA Technical Reports Server (NTRS)

Edquist, Karl T.; Hollis, Brian R.; Johnston, Christopher O.; Bose, Deepak; White, Todd R.; Mahzari, Milad

2013-01-01

The Mars Science Laboratory heatshield was designed to withstand a fully turbulent heat pulse based on test results and computational analysis on a pre-flight design trajectory. Instrumentation on the flight heatshield measured in-depth temperatures in the thermal protection system. The data indicate that boundary layer transition occurred at 5 of 7 thermocouple locations prior to peak heating. Data oscillations at 3 pressure measurement locations may also indicate transition. This paper presents the heatshield temperature and pressure data, possible explanations for the timing of boundary layer transition, and a qualitative comparison of reconstructed and computational heating on the as-flown trajectory. Boundary layer Reynolds numbers that are typically used to predict transition are compared to observed transition at various heatshield locations. A uniform smooth-wall transition Reynolds number does not explain the timing of boundary layer transition observed during flight. A roughness-based Reynolds number supports the possibility of transition due to discrete or distributed roughness elements on the heatshield. However, the distributed roughness height would have needed to be larger than the pre-flight assumption. The instrumentation confirmed the predicted location of maximum turbulent heat flux near the leeside shoulder. The reconstructed heat flux at that location is bounded by smooth-wall turbulent calculations on the reconstructed trajectory, indicating that augmentation due to surface roughness probably did not occur. Turbulent heating on the downstream side of the heatshield nose exceeded smooth-wall computations, indicating that roughness may have augmented heating. The stagnation region also experienced heating that exceeded computational levels, but shock layer radiation does not fully explain the differences.

Detecting phase transitions in a neural network and its application to classification of syndromes in traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Chen, J.; Xi, G.; Wang, W.

2008-02-01

Detecting phase transitions in neural networks (determined or random) presents a challenging subject for phase transitions play a key role in human brain activity. In this paper, we detect numerically phase transitions in two types of random neural network(RNN) under proper parameters.

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

PubMed

Gong, Yubing; Wang, Baoying; Xie, Huijuan

2016-12-01

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate A p of STDP and become strongest at a certain A p value, and the A p value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when A p increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Transitional care in clinical networks for young people with juvenile idiopathic arthritis: current situation and challenges.

PubMed

Cruikshank, Mary; Foster, Helen E; Stewart, Jane; Davidson, Joyce E; Rapley, Tim

2016-04-01

Clinical networks for paediatric and adolescent rheumatology are evolving, and their effect and role in the transition process between paediatric and adult services are unknown. We therefore explored the experiences of those involved to try and understand this further. Health professionals, young people with juvenile idiopathic arthritis and their families were recruited via five national health service paediatric and adolescent rheumatology specialist centres and networks across the UK. Seventy participants took part in focus groups and one-to-one interviews. Data was analysed using coding, memoing and mapping techniques to identify features of transitional services across the sector. Variation and inequities in transitional care exist. Although transition services in networks are evolving, development has lagged behind other areas with network establishment focusing more on access to paediatric rheumatology multidisciplinary teams. Challenges include workforce shortfalls, differences in service priorities, standards and healthcare infrastructures, and managing the legacy of historic encounters. Providing equitable high-quality clinically effective services for transition across the UK has a long way to go. There is a call from within the sector for more protected time, staff and resources to develop transition roles and services, as well as streamlining of local referral pathways between paediatric and adult healthcare services. In addition, there is a need to support professionals in developing their understanding of transitional care in clinical networks, particularly around service design, organisational change and the interpersonal skills required for collaborative working. Key messages • Transitional care in clinical networks requires collaborative working and an effective interface with paediatric and adult rheumatology.• Professional centrism and historic encounters may affect collaborative relationships within clinical networks.• Education programmes need to support the development of interpersonal skills and change management, to facilitate professionals in networks delivering transitional care.

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

PubMed

Lakhani, Paras; Sundaram, Baskaran

2017-08-01

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. The datasets were split into training (68.0%), validation (17.1%), and test (14.9%). Two different DCNNs, AlexNet and GoogLeNet, were used to classify the images as having manifestations of pulmonary TB or as healthy. Both untrained and pretrained networks on ImageNet were used, and augmentation with multiple preprocessing techniques. Ensembles were performed on the best-performing algorithms. For cases where the classifiers were in disagreement, an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow. Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performance by using the DeLong method for statistical comparison of receiver operating characteristic curves. Results The best-performing classifier had an AUC of 0.99, which was an ensemble of the AlexNet and GoogLeNet DCNNs. The AUCs of the pretrained models were greater than that of the untrained models (P < .001). Augmenting the dataset further increased accuracy (P values for AlexNet and GoogLeNet were .03 and .02, respectively). The DCNNs had disagreement in 13 of the 150 test cases, which were blindly reviewed by a cardiothoracic radiologist, who correctly interpreted all 13 cases (100%). This radiologist-augmented approach resulted in a sensitivity of 97.3% and specificity 100%. Conclusion Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99. A radiologist-augmented approach for cases where there was disagreement among the classifiers further improved accuracy. © RSNA, 2017.

A Study of the IEEE 802.16 MAC Layer and its Utility in Augmenting the ADNS Architecture to Provide Adaptable Intra-Strike Group High-Speed Packet Switched Data, Imagery, and Voice Communications

DTIC Science & Technology

2005-09-01

This research explores the need for a high throughput, high speed network for use in a network centric wartime environment and how commercial...Automated Digital Network System (ADNS). This research explores the need for a high-throughput, high-speed network for use in a network centric ...1 C. DEPARTMENT OF DEFENSE (DOD) DESIRED END STATE ..............2 1. DOD Transformation to Network Centric Warfare (NCW) Operations

Network traffic behaviour near phase transition point

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-03-01

We explore packet traffic dynamics in a data network model near phase transition point from free flow to congestion. The model of data network is an abstraction of the Network Layer of the OSI (Open Systems Interconnect) Reference Model of packet switching networks. The Network Layer is responsible for routing packets across the network from their sources to their destinations and for control of congestion in data networks. Using the model we investigate spatio-temporal packets traffic dynamics near the phase transition point for various network connection topologies, and static and adaptive routing algorithms. We present selected simulation results and analyze them.

Collaborative Estimation in Distributed Sensor Networks

ERIC Educational Resources Information Center

Kar, Swarnendu

2013-01-01

Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication of sensory (e.g., audio, video, location) inputs. Though usually hidden from the user's sight, the engineering of these devices involves fierce tradeoffs between energy…

Use of Communication Resources in a Networked Collaborative Design Environment.

ERIC Educational Resources Information Center

Gay, Geri; Lentini, Marc

1995-01-01

Examines student use of a prototype networked collaborative design environment to support or augment learning about engineering design. Finds that students use the channels for a variety of activities to increase depth of communication, increase breadth of communication, and overcome technical difficulty. Suggests that students need multiple…

Communication Resource Use in a Networked Collaborative Design Environment.

ERIC Educational Resources Information Center

Gay, Geri; Lentini, Marc

The purpose of this exploratory study was to examine student use of a prototype networked collaborative design environment to support or augment learning about engineering design. The theoretical framework is based primarily on Vygotsky's social construction of knowledge and the belief that collaboration and communication are critical components…

SATWG networked quality function deployment

NASA Technical Reports Server (NTRS)

Brown, Don

1992-01-01

The initiative of this work is to develop a cooperative process for continual evolution of an integrated, time phased avionics technology plan that involves customers, technologists, developers, and managers. This will be accomplished by demonstrating a computer network technology to augment the Quality Function Deployment (QFD). All results are presented in viewgraph format.

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

Classification of teeth in cone-beam CT using deep convolutional neural network.

PubMed

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-01-01

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Explosive synchronization transitions in complex neural networks.

PubMed

Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai

2013-09-01

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

Explosive synchronization transitions in complex neural networks

NASA Astrophysics Data System (ADS)

Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai

2013-09-01

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

Transitions in Smokers’ Social Networks After Quit Attempts: A Latent Transition Analysis

PubMed Central

Smith, Rachel A.; Piper, Megan E.; Roberts, Linda J.; Baker, Timothy B.

2016-01-01

Introduction: Smokers’ social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers’ social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Methods: Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members’ smoking habits, within network smoking, smoking buddies, and romantic partners’ smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Results: Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants’ social networks to less contact with smokers and larger networks in years 2 and 3. Conclusions: In the years following a smoking-cessation attempt, smokers’ social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Implications: Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person’s social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. PMID:27613925

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Navigation Architecture For A Space Mobile Network

NASA Technical Reports Server (NTRS)

Valdez, Jennifer E.; Ashman, Benjamin; Gramling, Cheryl; Heckler, Gregory W.; Carpenter, Russell

2016-01-01

The Tracking and Data Relay Satellite System (TDRSS) Augmentation Service for Satellites (TASS) is a proposed beacon service to provide a global, space-based GPS augmentation service based on the NASA Global Differential GPS (GDGPS) System. The TASS signal will be tied to the GPS time system and usable as an additional ranging and Doppler radiometric source. Additionally, it will provide data vital to autonomous navigation in the near Earth regime, including space weather information, TDRS ephemerides, Earth Orientation Parameters (EOP), and forward commanding capability. TASS benefits include enhancing situational awareness, enabling increased autonomy, and providing near real-time command access for user platforms. As NASA Headquarters Space Communication and Navigation Office (SCaN) begins to move away from a centralized network architecture and towards a Space Mobile Network (SMN) that allows for user initiated services, autonomous navigation will be a key part of such a system. This paper explores how a TASS beacon service enables the Space Mobile Networking paradigm, what a typical user platform would require, and provides an in-depth analysis of several navigation scenarios and operations concepts.

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

NASA Astrophysics Data System (ADS)

Chebbi, A.; Bargaoui, Z. K.; da Conceição Cunha, M.

2012-12-01

Based on rainfall intensity-duration-frequency (IDF) curves, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimisation can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short and a long term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. So, it is proposed to virtually augment it by 25, 50, 100 and 160% which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

NASA Astrophysics Data System (ADS)

Chebbi, A.; Bargaoui, Z. K.; da Conceição Cunha, M.

2013-10-01

Based on rainfall intensity-duration-frequency (IDF) curves, fitted in several locations of a given area, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimization can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables, and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short- and a long-term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. Therefore, it is proposed to augment it by 25, 50, 100 and 160% virtually, which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

PubMed Central

Beck, Tim N.; Chikwem, Adaeze J.; Solanki, Nehal R.

2014-01-01

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals. PMID:25096367

Ferritin heavy chain is a negative regulator of ovarian cancer stem cell expansion and epithelial to mesenchymal transition

PubMed Central

Pisanu, Maria Elena; Faniello, Maria Concetta; Jakopin, Žiga; Chiarella, Emanuela; Giovannone, Emilia Dora; Mancini, Rita; Ciliberto, Gennaro

2016-01-01

Objectives Ferritin is the major intracellular iron storage protein essential for maintaining the cellular redox status. In recent years ferritin heavy chain (FHC) has been shown to be involved also in the control of cancer cell growth. Analysis of public microarray databases in ovarian cancer revealed a correlation between low FHC expression levels and shorter survival. To better understand the role of FHC in cancer, we have silenced the FHC gene in SKOV3 cells. Results FHC-KO significantly enhanced cell viability and induced a more aggressive behaviour. FHC-silenced cells showed increased ability to form 3D spheroids and enhanced expression of NANOG, OCT4, ALDH and Vimentin. These features were accompanied by augmented expression of SCD1, a major lipid metabolism enzyme. FHC apparently orchestrates part of these changes by regulating a network of miRNAs. Methods FHC-silenced and control shScr SKOV3 cells were monitored for changes in proliferation, migration, ability to propagate as 3D spheroids and for the expression of stem cell and epithelial-to-mesenchymal-transition (EMT) markers. The expression of three miRNAs relevant to spheroid formation or EMT was assessed by q-PCR. Conclusions In this paper we uncover a new function of FHC in the control of cancer stem cells. PMID:27566559

Modulation of the brain's functional network architecture in the transition from wake to sleep

PubMed Central

Larson-Prior, Linda J.; Power, Jonathan D.; Vincent, Justin L.; Nolan, Tracy S.; Coalson, Rebecca S.; Zempel, John; Snyder, Abraham Z.; Schlaggar, Bradley L.; Raichle, Marcus E.; Petersen, Steven E.

2013-01-01

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes. PMID:21854969

Use of NTRIP for optimizing the decoding algorithm for real-time data streams.

PubMed

He, Zhanke; Tang, Wenda; Yang, Xuhai; Wang, Liming; Liu, Jihua

2014-10-10

As a network transmission protocol, Networked Transport of RTCM via Internet Protocol (NTRIP) is widely used in GPS and Global Orbiting Navigational Satellite System (GLONASS) Augmentation systems, such as Continuous Operational Reference System (CORS), Wide Area Augmentation System (WAAS) and Satellite Based Augmentation Systems (SBAS). With the deployment of BeiDou Navigation Satellite system(BDS) to serve the Asia-Pacific region, there are increasing needs for ground monitoring of the BeiDou Navigation Satellite system and the development of the high-precision real-time BeiDou products. This paper aims to optimize the decoding algorithm of NTRIP Client data streams and the user authentication strategies of the NTRIP Caster based on NTRIP. The proposed method greatly enhances the handling efficiency and significantly reduces the data transmission delay compared with the Federal Agency for Cartography and Geodesy (BKG) NTRIP. Meanwhile, a transcoding method is proposed to facilitate the data transformation from the BINary EXchange (BINEX) format to the RTCM format. The transformation scheme thus solves the problem of handing real-time data streams from Trimble receivers in the BeiDou Navigation Satellite System indigenously developed by China.

Emergence of clustering in an acquaintance model without homophily

NASA Astrophysics Data System (ADS)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Phase transitions in semisupervised clustering of sparse networks

NASA Astrophysics Data System (ADS)

Zhang, Pan; Moore, Cristopher; Zdeborová, Lenka

2014-11-01

Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology. However, if we know the correct labels of some fraction α of the nodes, we can do better. We study the phase diagram of this semisupervised learning problem for networks generated by the stochastic block model. We use the cavity method and the associated belief propagation algorithm to study what accuracy can be achieved as a function of α . For k =2 groups, we find that the detectability transition disappears for any α >0 , in agreement with previous work. For larger k where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of α . This line ends in a critical point with a second-order transition, beyond which the accuracy is a continuous function of α . We demonstrate qualitatively similar transitions in two real-world networks.

Information cascade on networks

NASA Astrophysics Data System (ADS)

Hisakado, Masato; Mori, Shintaro

2016-05-01

In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barabási-Albert (BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters-herders and independents-and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct choices and a state in which most of them are wrong. The second is a transition of convergence speed. The information cascade transition prevails when herder effects are stronger than the super-normal transition. In the BA and fitness models, the critical point of the information cascade transition is the same as that of the random network model. However, the critical point of the super-normal transition disappears when these two models are used. In conclusion, the influence of networks is shown to only affect the convergence speed and not the information cascade transition. We are therefore able to conclude that the influence of hubs on voters' perceptions is limited.

Transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto model

NASA Astrophysics Data System (ADS)

Kundu, Prosenjit; Khanra, Pitambar; Hens, Chittaranjan; Pal, Pinaki

2017-11-01

We investigate transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto (SK) model on complex networks both analytically and numerically. We analytically derive self-consistent equations for group angular velocity and order parameter for the model in the thermodynamic limit. Using the self-consistent equations we investigate transition to synchronization in SK model on uncorrelated scale-free (SF) and Erdős-Rényi (ER) networks in detail. Depending on the degree distribution exponent (γ ) of SF networks and phase-frustration parameter, the population undergoes from first-order transition [explosive synchronization (ES)] to second-order transition and vice versa. In ER networks transition is always second order irrespective of the values of the phase-lag parameter. We observe that the critical coupling strength for the onset of synchronization is decreased by phase-frustration parameter in case of SF network where as in ER network, the phase-frustration delays the onset of synchronization. Extensive numerical simulations using SF and ER networks are performed to validate the analytical results. An analytical expression of critical coupling strength for the onset of synchronization is also derived from the self-consistent equations considering the vanishing order parameter limit.

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

PubMed

Han, Seokmin; Kang, Ho-Kyung; Jeong, Ja-Yeon; Park, Moon-Ho; Kim, Wonsik; Bang, Won-Chul; Seong, Yeong-Kyeong

2017-09-15

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

A deep learning framework for supporting the classification of breast lesions in ultrasound images

NASA Astrophysics Data System (ADS)

Han, Seokmin; Kang, Ho-Kyung; Jeong, Ja-Yeon; Park, Moon-Ho; Kim, Wonsik; Bang, Won-Chul; Seong, Yeong-Kyeong

2017-10-01

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

Transitions in Smokers' Social Networks After Quit Attempts: A Latent Transition Analysis.

PubMed

Bray, Bethany C; Smith, Rachel A; Piper, Megan E; Roberts, Linda J; Baker, Timothy B

2016-12-01

Smokers' social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers' social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members' smoking habits, within network smoking, smoking buddies, and romantic partners' smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants' social networks to less contact with smokers and larger networks in years 2 and 3. In the years following a smoking-cessation attempt, smokers' social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person's social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Augmented Quantum Yield of a 2D Monolayer Photodetector by Surface Plasmon Coupling.

PubMed

Bang, Seungho; Duong, Ngoc Thanh; Lee, Jubok; Cho, Yoo Hyun; Oh, Hye Min; Kim, Hyun; Yun, Seok Joon; Park, Chulho; Kwon, Min-Ki; Kim, Ja-Yeon; Kim, Jeongyong; Jeong, Mun Seok

2018-04-11

Monolayer (1L) transition metal dichalcogenides (TMDCs) are promising materials for nanoscale optoelectronic devices because of their direct band gap and wide absorption range (ultraviolet to infrared). However, 1L-TMDCs cannot be easily utilized for practical optoelectronic device applications (e.g., photodetectors, solar cells, and light-emitting diodes) because of their extremely low optical quantum yields (QYs). In this investigation, a high-gain 1L-MoS 2 photodetector was successfully realized, based on the surface plasmon (SP) of the Ag nanowire (NW) network. Through systematic optical characterization of the hybrid structure consisting of a 1L-MoS 2 and the Ag NW network, it was determined that a strong SP and strain relaxation effect influenced a greatly enhanced optical QY. The photoluminescence (PL) emission was drastically increased by a factor of 560, and the main peak was shifted to the neutral exciton of 1L-MoS 2 . Consequently, the overall photocurrent of the hybrid 1L-MoS 2 photodetector was observed to be 250 times better than that of the pristine 1L-MoS 2 photodetector. In addition, the photoresponsivity and photodetectivity of the hybrid photodetector were effectively improved by a factor of ∼1000. This study provides a new approach for realizing highly efficient optoelectronic devices based on TMDCs.

Research Networking Systems: The State of Adoption at Institutions Aiming to Augment Translational Research Infrastructure

PubMed Central

Obeid, Jihad S; Johnson, Layne M; Stallings, Sarah; Eichmann, David

2015-01-01

Fostering collaborations across multiple disciplines within and across institutional boundaries is becoming increasingly important with the growing emphasis on translational research. As a result, Research Networking Systems that facilitate discovery of potential collaborators have received significant attention by institutions aiming to augment their research infrastructure. We have conducted a survey to assess the state of adoption of these new tools at the Clinical and Translational Science Award (CTSA) funded institutions. Survey results demonstrate that most CTSA funded institutions have either already adopted or were planning to adopt one of several available research networking systems. Moreover a good number of these institutions have exposed or plan to expose the data on research expertise using linked open data, an established approach to semantic web services. Preliminary exploration of these publically-available data shows promising utility in assessing cross-institutional collaborations. Further adoption of these technologies and analysis of the data are needed, however, before their impact on cross-institutional collaboration in research can be appreciated and measured. PMID:26491707

Research Networking Systems: The State of Adoption at Institutions Aiming to Augment Translational Research Infrastructure.

PubMed

Obeid, Jihad S; Johnson, Layne M; Stallings, Sarah; Eichmann, David

Fostering collaborations across multiple disciplines within and across institutional boundaries is becoming increasingly important with the growing emphasis on translational research. As a result, Research Networking Systems that facilitate discovery of potential collaborators have received significant attention by institutions aiming to augment their research infrastructure. We have conducted a survey to assess the state of adoption of these new tools at the Clinical and Translational Science Award (CTSA) funded institutions. Survey results demonstrate that most CTSA funded institutions have either already adopted or were planning to adopt one of several available research networking systems. Moreover a good number of these institutions have exposed or plan to expose the data on research expertise using linked open data, an established approach to semantic web services. Preliminary exploration of these publically-available data shows promising utility in assessing cross-institutional collaborations. Further adoption of these technologies and analysis of the data are needed, however, before their impact on cross-institutional collaboration in research can be appreciated and measured.

The motor cortex: a network tuned to 7-14 Hz

PubMed Central

Castro-Alamancos, Manuel A.

2013-01-01

The neocortex or six layer cortex consists of at least 52 cytoarchitectonically distinct areas in humans, and similar areas can be distinguished in rodents. Each of these areas has a defining set of extrinsic connections, identifiable functional roles, a distinct laminar arrangement, etc. Thus, neocortex is extensively subdivided into areas of anatomical and functional specialization, but less is known about the specialization of cellular and network physiology across areas. The motor cortex appears to have a distinct propensity to oscillate in the 7–14 Hz frequency range. Augmenting responses, normal mu and beta oscillations, and abnormal oscillations or after discharges caused by enhancing excitation or suppressing inhibition are all expressed around this frequency range. The substrate for this activity may be an excitatory network that is unique to the motor cortex or that is more strongly suppressed in other areas, such as somatosensory cortex. Interestingly, augmenting responses are dependent on behavioral state. They are abolished during behavioral arousal. Here, I briefly review this evidence. PMID:23439785

Design of band-notched antenna with DG-CEBG

NASA Astrophysics Data System (ADS)

Jaglan, Naveen; Kanaujia, Binod Kumar; Gupta, Samir Dev; Srivastava, Shweta

2018-01-01

Ultra-wideband (UWB) disc monopole antenna with crescent shaped slot for double band-notched features is presented. Planned antenna discards worldwide interoperability for microwave access (WiMAX) band (3.3-3.6 GHz) and wireless local area network (WLAN) band (5-6 GHz). Defected ground compact electromagnetic band gap (DG-CEBG) designs are used to accomplish band notches in WiMAX and WLAN bands. Defected ground planes are utilised to achieve compactness in electromagnetic band gap (EBG) structures. The proposed WiMAX and WLAN DG-CEBG designs show a compactness of around 46% and 50%, respectively, over mushroom EBG structures. Parametric analyses of DG-CEBG design factors are carried out to control the notched frequencies. Stepwise notch transition from upper to lower frequencies is presented with incremental inductance augmentation. The proposed antenna is made-up on low-cost FR-4 substrate of complete extents as (42 × 50 × 1.6) mm3.Fabricated sample antenna shows excellent consistency in simulated and measured outcomes.

Addressing Control Research Issues Leading to Piloted Simulations in Support of the IFCS F-15

NASA Technical Reports Server (NTRS)

Napolitano, Marcello; Perhinschi, Mario; Campa, Giampiero; Seanor, Brad

2004-01-01

This report summarizes the research effort by a team of researchers at West Virginia University in support of the NASA Intelligent Flight Control System (IFCS) F-15 program. In particular, WVU researchers assisted NASA Dryden researchers in the following technical tasks leading to piloted simulation of the 'Gen_2' IFCS control laws. Task #1- Performance comparison of different neural network (NN) augmentation for the Dynamic Inversion (DI) -based VCAS 'Gen_2' control laws. Task #2- Development of safety monitor criteria for transition to research control laws with and without failure during flight test. Task #3- Fine-tuning of the 'Gen_2' control laws for cross-coupling reduction at post-failure conditions. Matlab/Simulink-based simulation codes were provided to the technical monitor on a regular basis throughout the duration of the project. Additional deliverables for the project were Power Point-based slides prepared for different project meetings. This document provides a description of the methodology and discusses the general conclusions from the simulation results.

Adaptable dialog architecture and runtime engine (AdaRTE): a framework for rapid prototyping of health dialog systems.

PubMed

Rojas-Barahona, L M; Giorgino, T

2009-04-01

Spoken dialog systems have been increasingly employed to provide ubiquitous access via telephone to information and services for the non-Internet-connected public. They have been successfully applied in the health care context; however, speech technology requires a considerable development investment. The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms, at the expense of adding even more complexity. This paper introduces a novel architecture for dialogue representation and interpretation, AdaRTE, which allows developers to lay out dialog interactions through a high-level formalism, offering both declarative and procedural features. AdaRTE's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies. It enhances augmented transition networks with dynamic behavior, and drives multiple back-end realizers, including VoiceXML. It has been especially targeted to the health care context, because of the great scale and the need for reducing the barrier to a widespread adoption of dialog systems.

Use of display technologies for augmented reality enhancement

NASA Astrophysics Data System (ADS)

Harding, Kevin

2016-06-01

Augmented reality (AR) is seen as an important tool for the future of user interfaces as well as training applications. An important application area for AR is expected to be in the digitization of training and worker instructions used in the Brilliant Factory environment. The transition of work instructions methods from printed pages in a book or taped to a machine to virtual simulations is a long step with many challenges along the way. A variety of augmented reality tools are being explored today for industrial applications that range from simple programmable projections in the work space to 3D displays and head mounted gear. This paper will review where some of these tool are today and some of the pros and cons being considered for the future worker environment.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

NASA Astrophysics Data System (ADS)

Xie, Huijuan; Gong, Yubing; Wang, Baoying

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin-Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

A universal indicator of critical state transitions in noisy complex networked systems

PubMed Central

Liang, Junhao; Hu, Yanqing; Chen, Guanrong; Zhou, Tianshou

2017-01-01

Critical transition, a phenomenon that a system shifts suddenly from one state to another, occurs in many real-world complex networks. We propose an analytical framework for exactly predicting the critical transition in a complex networked system subjected to noise effects. Our prediction is based on the characteristic return time of a simple one-dimensional system derived from the original higher-dimensional system. This characteristic time, which can be easily calculated using network data, allows us to systematically separate the respective roles of dynamics, noise and topology of the underlying networked system. We find that the noise can either prevent or enhance critical transitions, playing a key role in compensating the network structural defect which suffers from either internal failures or environmental changes, or both. Our analysis of realistic or artificial examples reveals that the characteristic return time is an effective indicator for forecasting the sudden deterioration of complex networks. PMID:28230166

Firing patterns transition and desynchronization induced by time delay in neural networks

NASA Astrophysics Data System (ADS)

Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

2018-06-01

We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

76 FR 23644 - Solicitation of Nominations for Members of the Transit Rail Advisory Committee for Safety (TRACS)

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-27

... augment the TRACS' existing knowledge base with professionals who have done academic research in the... TRACS. From that solicitation, 21 members were chosen, each representing a broad base of expertise...

National Airspace System : status of wide area augmentation system project

DOT National Transportation Integrated Search

1998-04-30

As a key element of its overall program for modernizing the National Airspace : System, the Federal Aviation Administration (FAA) is planning a transition from : ground- to satellite-based navigation by using satellite signals generated by : the Depa...

Research In Nonlinear Flight Control for Tiltrotor Aircraft Operating in the Terminal Area

NASA Technical Reports Server (NTRS)

Calise, A. J.; Rysdyk, R.

1996-01-01

The research during the first year of the effort focused on the implementation of the recently developed combination of neural net work adaptive control and feedback linearization. At the core of this research is the comprehensive simulation code Generic Tiltrotor Simulator (GTRS) of the XV-15 tilt rotor aircraft. For this research the GTRS code has been ported to a Fortran environment for use on PC. The emphasis of the research is on terminal area approach procedures, including conversion from aircraft to helicopter configuration. This report focuses on the longitudinal control which is the more challenging case for augmentation. Therefore, an attitude command attitude hold (ACAH) control augmentation is considered which is typically used for the pitch channel during approach procedures. To evaluate the performance of the neural network adaptive control architecture it was necessary to develop a set of low order pilot models capable of performing such tasks as, follow desired altitude profiles, follow desired speed profiles, operate on both sides of powercurve, convert, including flaps as well as mastangle changes, operate with different stability and control augmentation system (SCAS) modes. The pilot models are divided in two sets, one for the backside of the powercurve and one for the frontside. These two sets are linearly blended with speed. The mastangle is also scheduled with speed. Different aspects of the proposed architecture for the neural network (NNW) augmented model inversion were also demonstrated. The demonstration involved implementation of a NNW architecture using linearized models from GTRS, including rotor states, to represent the XV-15 at various operating points. The dynamics used for the model inversion were based on the XV-15 operating at 30 Kts, with residualized rotor dynamics, and not including cross coupling between translational and rotational states. The neural network demonstrated ACAH control under various circumstances. Future efforts will include the implementation into the Fortran environment of GTRS, including pilot modeling and NNW augmentation for the lateral channels. These efforts should lead to the development of architectures that will provide for fully automated approach, using similar strategies.

Latency causes and reduction in optical metro networks

NASA Astrophysics Data System (ADS)

Bobrovs, Vjaceslavs; Spolitis, Sandis; Ivanovs, Girts

2013-12-01

The dramatic growth of transmitted information in fiber optical networks is leading to a concern about the network latency for high-speed reliable services like financial transactions, telemedicine, virtual and augmented reality, surveillance, and other applications. In order to ensure effective latency engineering, the delay variability needs to be accurately monitored and measured, in order to control it. This paper in brief describes causes of latency in fiber optical metro networks. Several available latency reduction techniques and solutions are also discussed, namely concerning usage of different chromatic dispersion compensation methods, low-latency amplifiers, optical fibers as well as other network elements.

Tuning stochastic transition rates in a bistable genetic network.

NASA Astrophysics Data System (ADS)

Chickarmane, Vijay; Peterson, Carsten

2009-03-01

We investigate the stochastic dynamics of a simple genetic network, a toggle switch, in which the system makes transitions between the two alternative states. Our interest is in exploring whether such stochastic transitions, which occur due to the intrinsic noise such as transcriptional and degradation events, can be slowed down/speeded up, without changing the mean expression levels of the two genes, which comprise the toggle network. Such tuning is achieved by linking a signaling network to the toggle switch. The signaling network comprises of a protein, which can exist either in an active (phosphorylated) or inactive (dephosphorylated) form, and where its state is determined by one of the genetic network components. The active form of the protein in turn feeds back on the dynamics of the genetic network. We find that the rate of stochastic transitions from one state to the other, is determined essentially by the speed of phosphorylation, and hence the rate can be modulated by varying the phosphatase levels. We hypothesize that such a network architecture can be implemented as a general mechanism for controlling transition rates and discuss applications in population studies of two differentiated cell lineages, ex: the myeloid/erythroid lineage in hematopoiesis.

Bootstrap percolation on spatial networks

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao; Hu, Yanqing

2015-10-01

Bootstrap percolation is a general representation of some networked activation process, which has found applications in explaining many important social phenomena, such as the propagation of information. Inspired by some recent findings on spatial structure of online social networks, here we study bootstrap percolation on undirected spatial networks, with the probability density function of long-range links’ lengths being a power law with tunable exponent. Setting the size of the giant active component as the order parameter, we find a parameter-dependent critical value for the power-law exponent, above which there is a double phase transition, mixed of a second-order phase transition and a hybrid phase transition with two varying critical points, otherwise there is only a second-order phase transition. We further find a parameter-independent critical value around -1, about which the two critical points for the double phase transition are almost constant. To our surprise, this critical value -1 is just equal or very close to the values of many real online social networks, including LiveJournal, HP Labs email network, Belgian mobile phone network, etc. This work helps us in better understanding the self-organization of spatial structure of online social networks, in terms of the effective function for information spreading.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

NASA Astrophysics Data System (ADS)

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-08-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

PubMed

Schleussner, Carl-Friedrich; Donges, Jonathan F; Engemann, Denis A; Levermann, Anders

2016-08-11

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

NASA Astrophysics Data System (ADS)

Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

2016-10-01

Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

"We definitely need an audience": experiences of Twitter, Twitter networks and tweet content in adults with severe communication disabilities who use augmentative and alternative communication (AAC).

PubMed

Hemsley, Bronwyn; Dann, Stephen; Palmer, Stuart; Allan, Meredith; Balandin, Susan

2015-01-01

The aim of this study was to investigate the Twitter experiences of adults with severe communication disabilities who use augmentative and alternative communication (AAC) to inform Twitter training and further research on the use of Twitter in populations with communication disabilities. This mixed methods research included five adults with severe communication disabilities who use AAC. It combined (a) quantitative analysis of Twitter networks and (b) manual coding of tweets with (c) narrative interviews with participants on their Twitter experiences and results. The five participants who used AAC and Twitter were diverse in their patterns and experiences of using Twitter. Twitter networks reflected interaction with a close-knit network of people rather than with the broader publics on Twitter. Conversational, Broadcast and Pass Along tweets featured most prominently, with limited use of News or Social Presence tweets. Tweets appeared mostly within each participant's micro- or meso-structural layers of Twitter. People who use AAC report positive experiences in using Twitter. Obtaining help in Twitter, and engaging in hashtag communities facilitated higher frequency of tweets and establishment of Twitter networks. Results reflected an inter-connection of participant Twitter networks that might form part of a larger as yet unexplored emergent community of people who use AAC in Twitter.

Protective and Risky Social Network Factors for Drinking During the Transition From High School to College.

PubMed

Meisel, Matthew K; Barnett, Nancy P

2017-11-01

The transition from high school to college is a unique developmental period to examine the relationship between social networks and alcohol use, because during this transition, students enter new environments and alcohol use becomes more pervasive. The aim of this study was to examine the extent to which personal social networks change during this transition and to examine how changes in the composition of networks are related to alcohol use. Participants (N = 374, 57.8% female) reported on their alcohol use and provided information about individuals in their social network before and immediately after their first year of college. These network members were matched across the two observations and were classified as either carryover (i.e., named at both assessments), dropped (i.e., named at only the first assessment), or added (i.e., named at only the second assessment). We found robust turnover, such that only 22% of network members were retained from the first observation to the second. Furthermore, heavy drinking in high school was associated with retaining more friends during the transition to college, but once in college, adding more heavy drinkers as friends was associated with the greatest alcohol risk. These findings show how changes in the composition of the social network influence an individual's alcohol use during the transition to college. Results from this study could be used to improve interventions that address the composition of the social network as a whole, as well as the characteristics of each individual in their social network.

Physarum polycephalum Percolation as a Paradigm for Topological Phase Transitions in Transportation Networks

NASA Astrophysics Data System (ADS)

Fessel, Adrian; Oettmeier, Christina; Bernitt, Erik; Gauthier, Nils C.; Döbereiner, Hans-Günther

2012-08-01

We study the formation of transportation networks of the true slime mold Physarum polycephalum after fragmentation by shear. Small fragments, called microplasmodia, fuse to form macroplasmodia in a percolation transition. At this topological phase transition, one single giant component forms, connecting most of the previously isolated microplasmodia. Employing the configuration model of graph theory for small link degree, we have found analytically an exact solution for the phase transition. It is generally applicable to percolation as seen, e.g., in vascular networks.

Effect of correlations on controllability transition in network control

PubMed Central

Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo

2016-01-01

The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks. PMID:27063294

Long-term complications following bladder augmentations in patients with spina bifida: bladder calculi, perforation of the augmented bladder and upper tract deterioration.

PubMed

Husmann, Douglas A

2016-02-01

We desire to review our experience with bladder augmentation in spina bifida patients followed in a transitional and adult urologic practice. This paper will specifically focus on three major complications: bladder calculi, the most frequent complication found following bladder augmentation, perforation of the augmentation, its most lethal complication and finally we will address loss of renal function as a direct result of our surgical reconstructive procedures. We reviewed a prospective data base maintained on patients with spina bifida followed in our transitional and adult urology clinic from 1986 to date. Specific attention was given to patients who had developed bladder calculi, sustained a spontaneous perforation of the augmented bladder or had developed new onset of renal scarring or renal insufficiency (≥ stage 3 renal failure) during prolonged follow-up. The development of renal stones (P<0.05) and symptomatic urinary tract infections (P<0.0001) were found to be significantly reduced by the use of high volume (≥240 mL) daily bladder wash outs. Individuals who still developed bladder calculi recalcitrant to high volume wash outs were not benefited by the correction of underlying metabolic abnormalities or mucolytic agents. Spontaneous bladder perforations in the adult patient population with spina bifida were found to be directly correlated to substance abuse and noncompliance with intermittent catheterization, P<0.005. Deterioration of the upper tracts as defined by the new onset of renal scars occurred in 40% (32/80) of the patients managed by a ileocystoplasty and simultaneous bladder neck outlet procedure during a median follow-up interval 14 years (range, 8-45 years). Development of ≥ stage 3 chronic renal failure occurred within 38% (12/32) of the patients with scarring i.e., 15% (12/80) of the total patient population. Prior to the development of the renal scarring, 69% (22/32) of the patients had been noncompliant with intermittent catheterization. The onset of upper tract deterioration (i.e., new scar formation, hydronephrosis, calculus development, decrease in renal function) was silent, that is, clinically asymptomatic in one third (10/32 pts). This paper documents the need for high volume bladder irrigations to both prevent the most common complication following bladder augmentation, which is the development of bladder calculi and to reduce the incidence of symptomatic urinary tract infections. It provides a unique perspective regarding the impact of substance abuse and patient non-compliance with medical directives as being both the most common cause for both spontaneous bladder rupture following augmentation cystoplasty and for deterioration of the upper tracts. These findings should cause the surgeon to reflect on his/her assessment of a patient prior to performing a bladder augmentation procedure and stress the need for close follow-up.

Transitional flow in thin tubes for space station freedom radiator

NASA Technical Reports Server (NTRS)

Loney, Patrick; Ibrahim, Mounir

1995-01-01

A two dimensional finite volume method is used to predict the film coefficients in the transitional flow region (laminar or turbulent) for the radiator panel tubes. The code used to perform this analysis is CAST (Computer Aided Simulation of Turbulent Flows). The information gathered from this code is then used to augment a Sinda85 model that predicts overall performance of the radiator. A final comparison is drawn between the results generated with a Sinda85 model using the Sinda85 provided transition region heat transfer correlations and the Sinda85 model using the CAST generated data.

A variational approach to parameter estimation in ordinary differential equations.

PubMed

Kaschek, Daniel; Timmer, Jens

2012-08-14

Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.

Augmenting the access grid using augmented reality

NASA Astrophysics Data System (ADS)

Li, Ying

2012-01-01

The Access Grid (AG) targets an advanced collaboration environment, with which multi-party group of people from remote sites can collaborate over high-performance networks. However, current AG still employs VIC (Video Conferencing Tool) to offer only pure video for remote communication, while most AG users expect to collaboratively refer and manipulate the 3D geometric models of grid services' results in live videos of AG session. Augmented Reality (AR) technique can overcome the deficiencies with its characteristics of combining virtual and real, real-time interaction and 3D registration, so it is necessary for AG to utilize AR to better assist the advanced collaboration environment. This paper introduces an effort to augment the AG by adding support for AR capability, which is encapsulated in the node service infrastructure, named as Augmented Reality Service (ARS). The ARS can merge the 3D geometric models of grid services' results and real video scene of AG into one AR environment, and provide the opportunity for distributed AG users to interactively and collaboratively participate in the AR environment with better experience.

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

PubMed Central

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-01-01

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

Mechanical critical phenomena and the elastic response of fiber networks

NASA Astrophysics Data System (ADS)

Mackintosh, Fred

The mechanics of cells and tissues are largely governed by scaffolds of filamentous proteins that make up the cytoskeleton, as well as extracellular matrices. Evidence is emerging that such networks can exhibit rich mechanical phase behavior. A classic example of a mechanical phase transition was identified by Maxwell for macroscopic engineering structures: networks of struts or springs exhibit a continuous, second-order phase transition at the isostatic point, where the number of constraints imposed by connectivity just equals the number of mechanical degrees of freedom. We present recent theoretical predictions and experimental evidence for mechanical phase transitions in in both synthetic and biopolymer networks. We show, in particular, excellent quantitative agreement between the mechanics of collagen matrices and the predictions of a strain-controlled phase transition in sub-isostatic networks.

Discriminative Cooperative Networks for Detecting Phase Transitions

NASA Astrophysics Data System (ADS)

Liu, Ye-Hua; van Nieuwenburg, Evert P. L.

2018-04-01

The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model—the snake—from computer vision to host the two networks. The snake, with a DCN "brain," moves and learns actively in the parameter space, and locates phase boundaries automatically.

Studies of Transitional Flow, Unsteady Separation Phenomena and Particle Induced Augmentation Heating on Ablated Nose Tips.

DTIC Science & Technology

1975-10-01

63 29 Variation of Profile Shape with Time for Axisyinmetric Camphor Models 63 30 The Development of Ablated Nose Shapes Over Which Flow...ablation tests using camphor models and inferred from downrange observation of full scale flight missions. Regions of gross instability on nose...been verified in wind tunnel tests of camphor models where shapes similar to those shown on Figure 29 can be developed under transitional conditions

Density functional theory calculations of 95Mo NMR parameters in solid-state compounds.

PubMed

Cuny, Jérôme; Furet, Eric; Gautier, Régis; Le Pollès, Laurent; Pickard, Chris J; d'Espinose de Lacaillerie, Jean-Baptiste

2009-12-21

The application of periodic density functional theory-based methods to the calculation of (95)Mo electric field gradient (EFG) and chemical shift (CS) tensors in solid-state molybdenum compounds is presented. Calculations of EFG tensors are performed using the projector augmented-wave (PAW) method. Comparison of the results with those obtained using the augmented plane wave + local orbitals (APW+lo) method and with available experimental values shows the reliability of the approach for (95)Mo EFG tensor calculation. CS tensors are calculated using the recently developed gauge-including projector augmented-wave (GIPAW) method. This work is the first application of the GIPAW method to a 4d transition-metal nucleus. The effects of ultra-soft pseudo-potential parameters, exchange-correlation functionals and structural parameters are precisely examined. Comparison with experimental results allows the validation of this computational formalism.

Methods of Helium Injection and Removal for Heat Transfer Augmentation

NASA Technical Reports Server (NTRS)

Haight, Harlan; Kegley, Jeff; Bourdreaux, Meghan

2008-01-01

While augmentation of heat transfer from a test article by helium gas at low pressures is well known, the method is rarely employed during space simulation testing because the test objectives usually involve simulation of an orbital thermal environment. Test objectives of cryogenic optical testing at Marshall Space Flight Center's X-ray Cryogenic Facility (XRCF) have typically not been constrained by orbital environment parameters. As a result, several methods of helium injection have been utilized at the XRCF since 1999 to decrease thermal transition times. A brief synopsis of these injection (and removal) methods including will be presented.

Methods of Helium Injection and Removal for Heat Transfer Augmentation

NASA Technical Reports Server (NTRS)

Kegley, Jeffrey

2008-01-01

While augmentation of heat transfer from a test article by helium gas at low pressures is well known, the method is rarely employed during space simulation testing because the test objectives are to simulate an orbital thermal environment. Test objectives of cryogenic optical testing at Marshall Space Flight Center's X-ray Calibration Facility (XRCF) have typically not been constrained by orbital environment parameters. As a result, several methods of helium injection have been utilized at the XRCF since 1999 to decrease thermal transition times. A brief synopsis of these injection (and removal) methods including will be presented.

A call for innovative social media research in the field of augmentative and alternative communication.

PubMed

Hemsley, Bronwyn; Balandin, Susan; Palmer, Stuart; Dann, Stephen

2017-03-01

Augmentative and alternative communication (AAC) social media research is relatively new, and is built on a foundation of research on use of the Internet and social media by people with communication disabilities. Although the field is expanding to include a range of people who use AAC, there are limitations and gaps in research that will need to be addressed in order to keep pace with the rapid evolution of social media connectivity in assistive communication technologies. In this paper, we consider the aims, scope, and methodologies of AAC social media research, with a focus on social network sites. Lack of detailed attention to specific social network sites and little use of social media data limits the extent to which findings can be confirmed. Increased use of social media data across a range of platforms, including Instagram and YouTube, would provide important insights into the lives of people who use AAC and the ways in which they and their supporters use social media. New directions for AAC social media research are presented in line with those discussed at the social media research symposium at the International Society for Augmentative and Alternative Communication in Toronto, Canada, on August 12, 2016.

Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

PubMed

Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

2015-10-01

This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Paradoxical augmented relapse in alcohol-dependent rats during deep-brain stimulation in the nucleus accumbens

PubMed Central

Hadar, R; Vengeliene, V; Barroeta Hlusicke, E; Canals, S; Noori, H R; Wieske, F; Rummel, J; Harnack, D; Heinz, A; Spanagel, R; Winter, C

2016-01-01

Case reports indicate that deep-brain stimulation in the nucleus accumbens may be beneficial to alcohol-dependent patients. The lack of clinical trials and our limited knowledge of deep-brain stimulation call for translational experiments to validate these reports. To mimic the human situation, we used a chronic-continuous brain-stimulation paradigm targeting the nucleus accumbens and other brain sites in alcohol-dependent rats. To determine the network effects of deep-brain stimulation in alcohol-dependent rats, we combined electrical stimulation of the nucleus accumbens with functional magnetic resonance imaging (fMRI), and studied neurotransmitter levels in nucleus accumbens-stimulated versus sham-stimulated rats. Surprisingly, we report here that electrical stimulation of the nucleus accumbens led to augmented relapse behavior in alcohol-dependent rats. Our associated fMRI data revealed some activated areas, including the medial prefrontal cortex and caudate putamen. However, when we applied stimulation to these areas, relapse behavior was not affected, confirming that the nucleus accumbens is critical for generating this paradoxical effect. Neurochemical analysis of the major activated brain sites of the network revealed that the effect of stimulation may depend on accumbal dopamine levels. This was supported by the finding that brain-stimulation-treated rats exhibited augmented alcohol-induced dopamine release compared with sham-stimulated animals. Our data suggest that deep-brain stimulation in the nucleus accumbens enhances alcohol-liking probably via augmented dopamine release and can thereby promote relapse. PMID:27327255

The use of network analysis to study complex animal communication systems: a study on nightingale song.

PubMed

Weiss, Michael; Hultsch, Henrike; Adam, Iris; Scharff, Constance; Kipper, Silke

2014-06-22

The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales (Luscinia megarhynchos) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measures, we calculated shortest path length and transitivity and identified the 'small-world' character of nightingale song networks. Besides comparing network measures with conventional measures of song complexity, we also found a correlation between network measures and age of birds. Furthermore, we determined the numbers of in-coming and out-going edges of each song type, characterizing transition patterns. These transition patterns were shared across males for certain song types. Playbacks with different transition patterns provided first evidence that these patterns are responded to differently and thus play a role in singing interactions. We discuss potential functions of the network properties of song sequences in the framework of vocal leadership. Network approaches provide biologically meaningful parameters to describe the song structure of species with extremely large repertoires and complex rules of song retrieval.

The use of network analysis to study complex animal communication systems: a study on nightingale song

PubMed Central

Weiss, Michael; Hultsch, Henrike; Adam, Iris; Scharff, Constance; Kipper, Silke

2014-01-01

The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales (Luscinia megarhynchos) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measures, we calculated shortest path length and transitivity and identified the ‘small-world’ character of nightingale song networks. Besides comparing network measures with conventional measures of song complexity, we also found a correlation between network measures and age of birds. Furthermore, we determined the numbers of in-coming and out-going edges of each song type, characterizing transition patterns. These transition patterns were shared across males for certain song types. Playbacks with different transition patterns provided first evidence that these patterns are responded to differently and thus play a role in singing interactions. We discuss potential functions of the network properties of song sequences in the framework of vocal leadership. Network approaches provide biologically meaningful parameters to describe the song structure of species with extremely large repertoires and complex rules of song retrieval. PMID:24807258

Evaluating the effect of street network connectivity on first/last mile transit performance.

DOT National Transportation Integrated Search

2011-11-01

"This study defines a novel connectivity indicator (CI) to predict transit performance by : identifying the role that street network connectivity plays in influencing the service quality of : demand responsive feeder transit services. This new CI def...

Investigation of bus transit schedule behavior modeling using advanced techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalaputapu, R.; Demetsky, M.J.

This research focused on investigating the application of artificial neural networks (ANN) and the Box-Jenkins technique for developing and testing schedule behavior models using data obtained for a test route from Tidewater Regional Transit`s AVL system. The three ANN architectures investigated were: Feedforward Network, Elman Network and Jordan Network. In addition, five different model structures were investigated. The time-series methodology was adopted for developing the schedule behavior models. Finally, the role of a schedule behavior model within the framework of an intelligent transit management system is defined and the potential utility of the schedule behavior model is discussed using anmore » example application.« less

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

PubMed Central

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-01-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking. PMID:27510641

Defense Advanced Research Projects Agency: Key Factors Drive Transition of Technologies, but Better Training and Data Dissemination Can Increase Success

DTIC Science & Technology

2015-11-01

more detail. Table 1: Overview of DARPA Programs Selected for GAO Case Study Analyses Program name Program description Advanced Wireless Networks ...Selected DARPA Programs Program name According to DARPA portfolio-level database According to GAO analysis Advanced Wireless Networks for the Soldier...with potential transition partners Achievement of clearly defined technical goals Successful transition Advanced Wireless Networks for Soldier

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

PubMed

Ching, W-K; Chen, X; Tsing, N-K

2009-11-01

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

NASA Astrophysics Data System (ADS)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

The Cognitive Social Network in Dreams: Transitivity, Assortativity, and Giant Component Proportion Are Monotonic.

PubMed

Han, Hye Joo; Schweickert, Richard; Xi, Zhuangzhuang; Viau-Quesnel, Charles

2016-04-01

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social network. But the dream social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations. Copyright © 2015 Cognitive Science Society, Inc.

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

NASA Astrophysics Data System (ADS)

Kasap, Nihat; Agarwal, Anurag

2012-08-01

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

An Assessment of Fatigue Damage and Crack Growth Prediction Techniques (L’Evaluation de l’Endommagement en Fatigue et les Techniques de Prediction de la Propagation des Fissures)

DTIC Science & Technology

1994-03-01

reality the structure of even one individual aircraft consists of many bat- ches and the tens of thousand of cars of one type manufactured in even...generated neural network power spectral densities of surface pressures are used to augment existing data and then load an elastic finite clement...investigated for possible use in augmenting this information which is required for fatigue life calculations. Since empennage environments on fighter

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological function.

Stability analysis for virus spreading in complex networks with quarantine and non-homogeneous transition rates

NASA Astrophysics Data System (ADS)

Alarcon-Ramos, L. A.; Schaum, A.; Rodríguez Lucatero, C.; Bernal Jaquez, R.

2014-03-01

Virus propagations in complex networks have been studied in the framework of discrete time Markov process dynamical systems. These studies have been carried out under the assumption of homogeneous transition rates, yielding conditions for virus extinction in terms of the transition probabilities and the largest eigenvalue of the connectivity matrix. Nevertheless the assumption of homogeneous rates is rather restrictive. In the present study we consider non-homogeneous transition rates, assigned according to a uniform distribution, with susceptible, infected and quarantine states, thus generalizing the previous studies. A remarkable result of this analysis is that the extinction depends on the weakest element in the network. Simulation results are presented for large free-scale networks, that corroborate our theoretical findings.

Glass transition temperature and topological constraints of sodium borophosphate glass-forming liquids.

PubMed

Jiang, Qi; Zeng, Huidan; Liu, Zhao; Ren, Jing; Chen, Guorong; Wang, Zhaofeng; Sun, Luyi; Zhao, Donghui

2013-09-28

Sodium borophosphate glasses exhibit intriguing mixed network former effect, with the nonlinear compositional dependence of their glass transition temperature as one of the most typical examples. In this paper, we establish the widely applicable topological constraint model of sodium borophosphate mixed network former glasses to explain the relationship between the internal structure and nonlinear changes of glass transition temperature. The application of glass topology network was discussed in detail in terms of the unified methodology for the quantitative distribution of each coordinated boron and phosphorus units and glass transition temperature dependence of atomic constraints. An accurate prediction of composition scaling of the glass transition temperature was obtained based on topological constraint model.

Augmented Reality 2.0

NASA Astrophysics Data System (ADS)

Schmalstieg, Dieter; Langlotz, Tobias; Billinghurst, Mark

Augmented Reality (AR) was first demonstrated in the 1960s, but only recently have technologies emerged that can be used to easily deploy AR applications to many users. Camera-equipped cell phones with significant processing power and graphics abilities provide an inexpensive and versatile platform for AR applications, while the social networking technology of Web 2.0 provides a large-scale infrastructure for collaboratively producing and distributing geo-referenced AR content. This combination of widely used mobile hardware and Web 2.0 software allows the development of a new type of AR platform that can be used on a global scale. In this paper we describe the Augmented Reality 2.0 concept and present existing work on mobile AR and web technologies that could be used to create AR 2.0 applications.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

Extraordinary variability and sharp transitions in a maximally frustrated dynamic network

NASA Astrophysics Data System (ADS)

Liu, Wenjia; Schmittmann, Beate; Zia, R. K. P.

2013-03-01

Most previous studies of complex networks have focused on single, static networks. However, in the real world, networks are dynamic and interconnected. Inspired by the presence of extroverts and introverts in the general population, we investigate a highly simplified model of a social network, involving two types of nodes: one preferring the highest degree possible, and one preferring no connections whatsoever. There are only two control parameters in the model: the number of ``introvert'' and ``extrovert'' nodes, NI and NE. Our key findings are as follows: As a function of NI and NE, the system exhibits a highly unusual transition, displaying extraordinary fluctuations (as in 2nd order transitions) and discontinuous jumps (characteristic of 1st order transitions). Most remarkably, the system can be described by an Ising-like Hamiltonian with long-range multi-spin interactions and some of its properties can be obtained analytically. This is in stark contrast with other dynamic network models which rely almost exclusively on simulations. NSF-DMR-1005417/1244666 and and ICTAS Virginia Tech

Percolation of networks with directed dependency links

NASA Astrophysics Data System (ADS)

Niu, Dunbiao; Yuan, Xin; Du, Minhui; Stanley, H. Eugene; Hu, Yanqing

2016-04-01

The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdős-Rényi networks.

Traffic modeling of transit oriented development : evaluation of transit friendly strategies and innovative intersection designs in West Valley City, UT.

DOT National Transportation Integrated Search

2014-07-01

Street networks designed to support Transit Oriented Development (TOD) increase accessibility for non-motorized traffic. However, the implications of TOD supportive networks for still dominant vehicular : traffic are rarely addressed. Due to this lac...

Cell transmission model of dynamic assignment for urban rail transit networks.

PubMed

Xu, Guangming; Zhao, Shuo; Shi, Feng; Zhang, Feilian

2017-01-01

For urban rail transit network, the space-time flow distribution can play an important role in evaluating and optimizing the space-time resource allocation. For obtaining the space-time flow distribution without the restriction of schedules, a dynamic assignment problem is proposed based on the concept of continuous transmission. To solve the dynamic assignment problem, the cell transmission model is built for urban rail transit networks. The priority principle, queuing process, capacity constraints and congestion effects are considered in the cell transmission mechanism. Then an efficient method is designed to solve the shortest path for an urban rail network, which decreases the computing cost for solving the cell transmission model. The instantaneous dynamic user optimal state can be reached with the method of successive average. Many evaluation indexes of passenger flow can be generated, to provide effective support for the optimization of train schedules and the capacity evaluation for urban rail transit network. Finally, the model and its potential application are demonstrated via two numerical experiments using a small-scale network and the Beijing Metro network.

The Climb to the Top: Is the Network the Route for Women?

ERIC Educational Resources Information Center

Warihay, Philomena D.

1980-01-01

Although aspiring women managers can receive some developmental support from women in management positions, this support frequently needs to be augmented by that from human resources personnel and successful men. (Author/JM)

Towards multi-platform software architecture for Collaborative Teleoperation

NASA Astrophysics Data System (ADS)

Domingues, Christophe; Otmane, Samir; Davesne, Frederic; Mallem, Malik

2009-03-01

Augmented Reality (AR) can provide to a Human Operator (HO) a real help in achieving complex tasks, such as remote control of robots and cooperative teleassistance. Using appropriate augmentations, the HO can interact faster, safer and easier with the remote real world. In this paper, we present an extension of an existing distributed software and network architecture for collaborative teleoperation based on networked human-scaled mixed reality and mobile platform. The first teleoperation system was composed by a VR application and a Web application. However the 2 systems cannot be used together and it is impossible to control a distant robot simultaneously. Our goal is to update the teleoperation system to permit a heterogeneous collaborative teleoperation between the 2 platforms. An important feature of this interface is based on the use of different Virtual Reality platforms and different Mobile platforms to control one or many robots.

A growth path for deep space communications

NASA Technical Reports Server (NTRS)

Layland, J. W.; Smith, J. G.

1987-01-01

Increased Deep Space Network (DPN) receiving capability far beyond that now available for Voyager is achievable through a mix of increased antenna aperture and increased frequency of operation. In this note a sequence of options are considered: adding midsized antennas for arraying with the existing network at X-band; converting to Ka-band and adding array elements; augmenting the DSN with an orbiting Ka-band station; and augmenting the DSN with an optical receiving capability, either on the ground or in space. Costs of these options are compared as means of achieving significantly increased receiving capability. The envelope of lowest costs projects a possible path for moving from X-band to Ka-band and thence to optical frequencies, and potentially for moving from ground-based to space-based apertures. The move to Ka-band is clearly of value now, with development of optical communications technology a good investment for the future.

Evolving neural networks through augmenting topologies.

PubMed

Stanley, Kenneth O; Miikkulainen, Risto

2002-01-01

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Towards multi-platform software architecture for Collaborative Teleoperation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domingues, Christophe; Otmane, Samir; Davesne, Frederic

2009-03-05

Augmented Reality (AR) can provide to a Human Operator (HO) a real help in achieving complex tasks, such as remote control of robots and cooperative teleassistance. Using appropriate augmentations, the HO can interact faster, safer and easier with the remote real world. In this paper, we present an extension of an existing distributed software and network architecture for collaborative teleoperation based on networked human-scaled mixed reality and mobile platform. The first teleoperation system was composed by a VR application and a Web application. However the 2 systems cannot be used together and it is impossible to control a distant robotmore » simultaneously. Our goal is to update the teleoperation system to permit a heterogeneous collaborative teleoperation between the 2 platforms. An important feature of this interface is based on the use of different Virtual Reality platforms and different Mobile platforms to control one or many robots.« less

Augmented Cognition Transition

DTIC Science & Technology

2009-05-01

that our efforts had operational relevance to the Future Force Warrior (FFW). We also thank Mr. Dennis Magnifico and Mr. Steve Specht for their help... yellow ), or “Exceeds Capacity” (red). In addition, the history of the moment-to-moment assessment of a soldier’s cognitive state is shown via a line

State feedback control design for Boolean networks.

PubMed

Liu, Rongjie; Qian, Chunjiang; Liu, Shuqian; Jin, Yu-Fang

2016-08-26

Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states. We found six simplest forms of controllable 2-node Boolean networks and explored the consistency of transition matrices while extending these six forms to controllable networks with more nodes. Importantly, we proposed the first state feedback control strategy to drive the network based on the status of all nodes in the network. Finally, we applied our reachability condition to the major switch of P53 pathway to predict the progression of the pathway and validate the prediction with published experimental results. This control strategy allowed us to apply realtime control to drive Boolean networks, which could not be achieved by the current control strategy for Boolean networks. Our results enabled a more comprehensive understanding of the evolution of Boolean networks and might be extended to output feedback control design.

Automated transit networks (ATN) : a review of the state of the industry and prospects for the future.

DOT National Transportation Integrated Search

2014-09-01

The concept of Automated Transit Networks (ATN) - in which fully automated vehicles on exclusive, grade-separated guideways : provide on-demand, primarily non-stop, origin-to-destination service over an area network has been around since the 1950...

Long-term complications following bladder augmentations in patients with spina bifida: bladder calculi, perforation of the augmented bladder and upper tract deterioration

PubMed Central

2016-01-01

Background We desire to review our experience with bladder augmentation in spina bifida patients followed in a transitional and adult urologic practice. This paper will specifically focus on three major complications: bladder calculi, the most frequent complication found following bladder augmentation, perforation of the augmentation, its most lethal complication and finally we will address loss of renal function as a direct result of our surgical reconstructive procedures. Methods We reviewed a prospective data base maintained on patients with spina bifida followed in our transitional and adult urology clinic from 1986 to date. Specific attention was given to patients who had developed bladder calculi, sustained a spontaneous perforation of the augmented bladder or had developed new onset of renal scarring or renal insufficiency (≥ stage 3 renal failure) during prolonged follow-up. Results The development of renal stones (P<0.05) and symptomatic urinary tract infections (P<0.0001) were found to be significantly reduced by the use of high volume (≥240 mL) daily bladder wash outs. Individuals who still developed bladder calculi recalcitrant to high volume wash outs were not benefited by the correction of underlying metabolic abnormalities or mucolytic agents. Spontaneous bladder perforations in the adult patient population with spina bifida were found to be directly correlated to substance abuse and noncompliance with intermittent catheterization, P<0.005. Deterioration of the upper tracts as defined by the new onset of renal scars occurred in 40% (32/80) of the patients managed by a ileocystoplasty and simultaneous bladder neck outlet procedure during a median follow-up interval 14 years (range, 8–45 years). Development of ≥ stage 3 chronic renal failure occurred within 38% (12/32) of the patients with scarring i.e., 15% (12/80) of the total patient population. Prior to the development of the renal scarring, 69% (22/32) of the patients had been noncompliant with intermittent catheterization. The onset of upper tract deterioration (i.e., new scar formation, hydronephrosis, calculus development, decrease in renal function) was silent, that is, clinically asymptomatic in one third (10/32 pts). Conclusions This paper documents the need for high volume bladder irrigations to both prevent the most common complication following bladder augmentation, which is the development of bladder calculi and to reduce the incidence of symptomatic urinary tract infections. It provides a unique perspective regarding the impact of substance abuse and patient non-compliance with medical directives as being both the most common cause for both spontaneous bladder rupture following augmentation cystoplasty and for deterioration of the upper tracts. These findings should cause the surgeon to reflect on his/her assessment of a patient prior to performing a bladder augmentation procedure and stress the need for close follow-up. PMID:26904407

Percolation of a general network of networks.

PubMed

Gao, Jianxi; Buldyrev, Sergey V; Stanley, H Eugene; Xu, Xiaoming; Havlin, Shlomo

2013-12-01

Percolation theory is an approach to study the vulnerability of a system. We develop an analytical framework and analyze the percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the size of the failure, i.e., the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to an abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic Erdős-Rényi (ER) networks, where each network depends on the same number m of other networks, i.e., for a random regular network (RR) formed of interdependent ER networks. The dependency between nodes of different networks is taken as one-to-one correspondence, i.e., a node in one network can depend only on one node in the other network (no-feedback condition). In contrast to a treelike NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m and the topology of each network but not on n. We also analyzed the extremely vulnerable feedback condition of coupling, where the coupling between nodes of different networks is not one-to-one correspondence. In the case of NetONet formed of ER networks, percolation only exhibits two phases, a second order phase transition and collapse, and no first order percolation transition regime is found in the case of the no-feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when the coupling strength q (fraction of interdependency links) is large and a second order phase transition when q is small. Our insight on the resilience of coupled networks might help in designing robust interdependent systems.

The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

NASA Astrophysics Data System (ADS)

Wang, Guochang; Cheng, Guojian; Carr, Timothy R.

2013-04-01

The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.

Performance Technology Program (PTP-S 2). Volume 9: Evaluation of reentry vehicle nosetip transition and heat transfer in the AEDC hyperballistics track G

NASA Astrophysics Data System (ADS)

Wassel, A. T.; Shih, W. C. L.; Curtis, R. J.

1981-01-01

Boundary layer transition and surface heating distributions on graphite fine weave carbon-carbon, and metallic nosetip materials were derived from surface temperature responses measured in nitrogen environments during both free-flight and track-guided testing in the AEDC Hyperballistics Range/Track G. Innovative test procedures were developed, and heat transfer results were validated against established theory through experiments using a super-smooth tungsten model. Quantitative definitions of mean transition front locations were established by deriving heat flux distributions from measured temperatures, and comparisons made with existing nosetip transition correlations. Qualitative transition locations were inferred directly from temperature distributions to investigate preferred orientations on fine weave nosetips. Levels of roughness augmented heat transfer were generally shown to be below values predicted by state of the art methods.

Transition from amplitude to oscillation death in a network of oscillators

NASA Astrophysics Data System (ADS)

Nandan, Mauparna; Hens, C. R.; Pal, Pinaki; Dana, Syamal K.

2014-12-01

We report a transition from a homogeneous steady state (HSS) to inhomogeneous steady states (IHSSs) in a network of globally coupled identical oscillators. We perturb a synchronized population of oscillators in the network with a few local negative or repulsive mean field links. The whole population splits into two clusters for a certain number of repulsive mean field links and a range of coupling strength. For further increase of the strength of interaction, these clusters collapse into a HSS followed by a transition to IHSSs where all the oscillators populate either of the two stable steady states. We analytically determine the origin of HSS and its transition to IHSS in relation to the number of repulsive mean-field links and the strength of interaction using a reductionism approach to the model network. We verify the results with numerical examples of the paradigmatic Landau-Stuart limit cycle system and the chaotic Rössler oscillator as dynamical nodes. During the transition from HSS to IHSSs, the network follows the Turing type symmetry breaking pitchfork or transcritical bifurcation depending upon the system dynamics.

Transition from amplitude to oscillation death in a network of oscillators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nandan, Mauparna; Department of Mathematics, National Institute of Technology, Durgapur 713209; Hens, C. R.

2014-12-01

We report a transition from a homogeneous steady state (HSS) to inhomogeneous steady states (IHSSs) in a network of globally coupled identical oscillators. We perturb a synchronized population of oscillators in the network with a few local negative or repulsive mean field links. The whole population splits into two clusters for a certain number of repulsive mean field links and a range of coupling strength. For further increase of the strength of interaction, these clusters collapse into a HSS followed by a transition to IHSSs where all the oscillators populate either of the two stable steady states. We analytically determinemore » the origin of HSS and its transition to IHSS in relation to the number of repulsive mean-field links and the strength of interaction using a reductionism approach to the model network. We verify the results with numerical examples of the paradigmatic Landau-Stuart limit cycle system and the chaotic Rössler oscillator as dynamical nodes. During the transition from HSS to IHSSs, the network follows the Turing type symmetry breaking pitchfork or transcritical bifurcation depending upon the system dynamics.« less

Proof of concept : GTFS data as a basis for optimization of Oregon's regional and statewide transit networks.

DOT National Transportation Integrated Search

2014-05-01

Assessing the current "state of health" of individual transit networks is a fundamental part of studies aimed at planning changes and/or upgrades to the transportation network serving a region. To be able to effect changes that benefit both the indiv...

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

PubMed Central

Kim, Minkyung; Kim, Seunghwan; Mashour, George A.; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations. PMID:28713258

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks.

PubMed

Kim, Minkyung; Kim, Seunghwan; Mashour, George A; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are "progressive and earlier" or "abrupt but delayed" account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations.

Implementing an Internet-based communication network for use during skilled nursing facility to emergency department care transitions: challenges and opportunities for improvement.

PubMed

Hustey, Fredric M; Palmer, Robert M

2012-03-01

To explore the feasibility of implementing an Internet-based communication network for communication of health care information during skilled nursing facility (SNF)-to-ED care transitions, and to identify potential barriers to system implementation. Qualitative. The largest SNF affiliated with the ED of an urban tertiary care center. Consecutive sample of all patients transferred from SNF to ED over 8 months between June 2007 and January 2008; ED and SNF care providers. The development and implementation of an Internet-based communication network for use during SNF-to-ED care transitions. This network was developed by adapting a preexisting Internet-based system that is widely used to facilitate placement of hospitalized patients into SNFs. Internet-based SNF and ED surveys were used to help identify barriers to implementation. There were 276/276 care transitions reviewed. The Internet-based communication network was used in 76 (28%) care transitions, with usage peaking at 40% near the end of the study. Barriers to success that were identified included lack of an electronic medical record (EMR) at the SNF; pervasive negative attitudes between ED and SNF personnel; time necessary for network use during care transitions; frustration by emergency physicians at low system usage rates by SNF personnel; and additional login requirements by ED personnel. Although implementing an Internet-based network for nursing home to ED communication may be feasible, significant barriers were identified in this study that are likely generalizable to other health care settings. Understanding such barriers is an essential first step toward building successful electronic communication networks in the future. Copyright © 2012 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.

Validation and augmentation of Inrix arterial travel time data using independent sources.

DOT National Transportation Integrated Search

2015-02-01

Travel time data is a key input to Intelligent Transportation Systems (ITS) applications. Advancement in vehicle : tracking and identification technologies and proliferation of location-aware and connected devices has made network-wide travel time da...

Mergers + acquisitions.

PubMed

Hoppszallern, Suzanna

2002-05-01

The hospital sector in 2001 led the health care field in mergers and acquisitions. Most deals involved a network augmenting its presence within a specific region or in a market adjacent to its primary service area. Analysts expect M&A activity to increase in 2002.

NREL Leads Effort to Get Traffic Moving in the Right Direction | News |

Science.gov Websites

fuels. This emerging approach to sustainable transportation augments ongoing work by NREL that is aimed at taking a systems approach to the broader network of travelers, services, and environments in order

A Rather Intelligent Language Teacher.

ERIC Educational Resources Information Center

Cerri, Stefano; Breuker, Joost

1981-01-01

Characteristics of DART (Didactic Augmented Recursive Transition), an ATN-based system for writing intelligent computer assisted instruction (ICAI) programs that is available on the PLATO system are described. DART allows writing programs in an ATN dialect, compiling them in machine code for the PLATO system, and executing them as if the original…

Integrated multimodal human-computer interface and augmented reality for interactive display applications

NASA Astrophysics Data System (ADS)

Vassiliou, Marius S.; Sundareswaran, Venkataraman; Chen, S.; Behringer, Reinhold; Tam, Clement K.; Chan, M.; Bangayan, Phil T.; McGee, Joshua H.

2000-08-01

We describe new systems for improved integrated multimodal human-computer interaction and augmented reality for a diverse array of applications, including future advanced cockpits, tactical operations centers, and others. We have developed an integrated display system featuring: speech recognition of multiple concurrent users equipped with both standard air- coupled microphones and novel throat-coupled sensors (developed at Army Research Labs for increased noise immunity); lip reading for improving speech recognition accuracy in noisy environments, three-dimensional spatialized audio for improved display of warnings, alerts, and other information; wireless, coordinated handheld-PC control of a large display; real-time display of data and inferences from wireless integrated networked sensors with on-board signal processing and discrimination; gesture control with disambiguated point-and-speak capability; head- and eye- tracking coupled with speech recognition for 'look-and-speak' interaction; and integrated tetherless augmented reality on a wearable computer. The various interaction modalities (speech recognition, 3D audio, eyetracking, etc.) are implemented a 'modality servers' in an Internet-based client-server architecture. Each modality server encapsulates and exposes commercial and research software packages, presenting a socket network interface that is abstracted to a high-level interface, minimizing both vendor dependencies and required changes on the client side as the server's technology improves.

Neuronal network model of interictal and recurrent ictal activity

NASA Astrophysics Data System (ADS)

Lopes, M. A.; Lee, K.-E.; Goltsev, A. V.

2017-12-01

We propose a neuronal network model which undergoes a saddle node on an invariant circle bifurcation as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.

Percolation Features on Climate Network under Attacks of El Niño Events

NASA Astrophysics Data System (ADS)

Lu, Z.

2015-12-01

Percolation theory under different attacks is one of the main research areas in complex networks but never be applied to investigate climate network. In this study, for the first time we construct a climate network of surface air temperature field to analyze its percolation features. Here, we regard El Niño event as a kind of naturally attacks generated from Pacific Ocean to attack its upper climate network. We find that El Niño event leads an abrupt percolation phase transition to the climate network which makes it splitting and unstable suddenly. Comparing the results of the climate network under three different forms of attacks, including most connected attack (MA), localized attack (LA) and random attack (RA) respectively, it is found that both MA and LA lead first-order transition and RA leads second-order transition to the climate network. Furthermore, we find that most real attacks consist of all these three forms of attacks. With El Niño event emerging, the ratios of LA and MA increase and dominate the style of attack while RA decreasing. It means the percolation phase transition due to El Niño events is close to first-order transition mostly affected by LA and MA. Our research may help us further understand two questions from perspective of percolation on network: (1) Why not all warming in Pacific Ocean but El Niño events could affect the climate. (2) Why the climate affected by El Niño events changes abruptly.

Conducting-insulating transition in adiabatic memristive networks

NASA Astrophysics Data System (ADS)

Sheldon, Forrest C.; Di Ventra, Massimiliano

2017-01-01

The development of neuromorphic systems based on memristive elements—resistors with memory—requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

The Kinetics and Thermodynamics of CO2 Capture by Aqueous Ammonia Derived Using Meta-GGA Density Functional Theory and Wavefunction-Based Model Chemistry Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beste, Ariana; Attalla, Moetaz; Jackson, Phil

2012-01-01

A meta GGA-DFT study of CO{sub 2} activation in aqueous ammonia solutions, with an emphasis on the reaction barrier and molecularity, has been undertaken using the M06-2X functional with an augmented triple-zeta split-valence basis set (6-311++G(d,p)). Up to five base molecules were treated explicitly in order to establish the effects of solvent catalysis in the chemical capture process. Aqueous free energies of solvation were determined for optimized reactant and transition structures using SM8/M06-2X/6-311++G(d,p). The concept of the solvent pre-complex as presented by Dixon and coworkers (Nguyen, M. T.; Matus, M. H.; Jackson, V. E.; Ngan, V. T.; Rustad, J. R.;more » Dixon, D. A. J. Phys. Chem. A 2008, 112, 10386-10398) was exploited to account for the energetics of disruption of the hydrogen-bonding solvent nano-network prior to the CO{sub 2} activation step. Selected gas- and aqueous-phase thermodynamic quantities have also been derived.« less

AdaRTE: adaptable dialogue architecture and runtime engine. A new architecture for health-care dialogue systems.

PubMed

Rojas-Barahona, L M; Giorgino, T

2007-01-01

Spoken dialogue systems have been increasingly employed to provide ubiquitous automated access via telephone to information and services for the non-Internet-connected public. In the health care context, dialogue systems have been successfully applied. Nevertheless, speech-based technology is not easy to implement because it requires a considerable development investment. The advent of VoiceXML for voice applications contributed to reduce the proliferation of incompatible dialogue interpreters, but introduced new complexity. As a response to these issues, we designed an architecture for dialogue representation and interpretation, AdaRTE, which allows developers to layout dialogue interactions through a high level formalism that offers both declarative and procedural features. AdaRTE aim is to provide a ground for deploying complex and adaptable dialogues whilst allows the experimentation and incremental adoption of innovative speech technologies. It provides the dynamic behavior of Augmented Transition Networks and enables the generation of different backends formats such as VoiceXML. It is especially targeted to the health care context, where a framework for easy dialogue deployment could reduce the barrier for a more widespread adoption of dialogue systems.

Face to face versus Facebook: does exposure to social networking web sites augment or attenuate physiological arousal among the socially anxious?

PubMed

Rauch, Shannon M; Strobel, Cara; Bella, Megan; Odachowski, Zachary; Bloom, Christopher

2014-03-01

The present study tested two competing hypotheses about the effect of Facebook exposure on the physiological arousal level of participants who then encountered the stimulus person in a face-to-face situation. Facebook exposure may attenuate later arousal by providing increased comfort and confidence, but it is also possible that Facebook exposure will augment arousal, particularly among the socially anxious. Participants completed a measure of social anxiety and were exposed to a stimulus person via Facebook, face to face, or both. Galvanic skin response was recorded during the exposures to the stimulus person. Results were consistent with the augmentation hypothesis: a prior exposure on Facebook will lead to increased arousal during a face-to-face encounter, particularly for those high in social anxiety.

Precise calculation of a bond percolation transition and survival rates of nodes in a complex network.

PubMed

Kawamoto, Hirokazu; Takayasu, Hideki; Jensen, Henrik Jeldtoft; Takayasu, Misako

2015-01-01

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.

Social Network Implications of Normative School Transitions in Non-Urban School Districts

ERIC Educational Resources Information Center

Temkin, Deborah A.; Gest, Scott D.; Osgood, D. Wayne; Feinberg, Mark; Moody, James

2018-01-01

This article expands research on normative school transitions (NSTs) from elementary to middle school or middle to high school by examining the extent to which they disrupt structures of friendship networks. Social network analysis is used to quantify aspects of connectedness likely relevant to student experiences of social support. Data were…

Dynamics, morphogenesis and convergence of evolutionary quantum Prisoner's Dilemma games on networks

PubMed Central

Yong, Xi

2016-01-01

The authors proposed a quantum Prisoner's Dilemma (PD) game as a natural extension of the classic PD game to resolve the dilemma. Here, we establish a new Nash equilibrium principle of the game, propose the notion of convergence and discover the convergence and phase-transition phenomena of the evolutionary games on networks. We investigate the many-body extension of the game or evolutionary games in networks. For homogeneous networks, we show that entanglement guarantees a quick convergence of super cooperation, that there is a phase transition from the convergence of defection to the convergence of super cooperation, and that the threshold for the phase transitions is principally determined by the Nash equilibrium principle of the game, with an accompanying perturbation by the variations of structures of networks. For heterogeneous networks, we show that the equilibrium frequencies of super-cooperators are divergent, that entanglement guarantees emergence of super-cooperation and that there is a phase transition of the emergence with the threshold determined by the Nash equilibrium principle, accompanied by a perturbation by the variations of structures of networks. Our results explore systematically, for the first time, the dynamics, morphogenesis and convergence of evolutionary games in interacting and competing systems. PMID:27118882

Critical forces for actin filament buckling and force transmission influence transport in actomyosin networks

NASA Astrophysics Data System (ADS)

Stam, Samantha; Gardel, Margaret

Viscoelastic networks of biopolymers coordinate the motion of intracellular objects during transport. These networks have nonlinear mechanical properties due to events such as filament buckling or breaking of cross-links. The influence of such nonlinear properties on the time and length scales of transport is not understood. Here, we use in vitro networks of actin and the motor protein myosin II to clarify how intracellular forces regulate active diffusion. We observe two transitions in the mean-squared displacement of cross-linked actin with increasing motor concentration. The first is a sharp transition from initially subdiffusive to diffusive-like motion that requires filament buckling but does not cause net contraction of the network. Further increase of the motor density produces a second transition to network rupture and ballistic actin transport. This corresponds with an increase in the correlation of motion and thus may be caused when forces propagate far enough for global motion. We conclude that filament buckling and overall network contraction require different amounts of force and produce distinct transport properties. These nonlinear transitions may act as mechanical switches that can be turned on to produce observed motion within cells.

All-optical OXC transition strategy from WDM optical network to elastic optical network.

PubMed

Chen, Xin; Li, Juhao; Guo, Bingli; Zhu, Paikun; Tang, Ruizhi; Chen, Zhangyuan; He, Yongqi

2016-02-22

Elastic optical network (EON) has been proposed recently as a spectrum-efficient optical layer to adapt to rapidly-increasing traffic demands instead of current deployed wavelength-division-multiplexing (WDM) optical network. In contrast with conventional WDM optical cross-connect (OXCs) based on wavelength selective switches (WSSs), the EON OXCs are based on spectrum selective switches (SSSs) which are much more expensive than WSSs, especially for large-scale switching architectures. So the transition cost from WDM OXCs to EON OXCs is a major obstacle to realizing EON. In this paper, we propose and experimentally demonstrate a transition OXC (TOXC) structure based on 2-stage cascading switching architectures, which make full use of available WSSs in current deployed WDM OXCs to reduce number and port count of required SSSs. Moreover, we propose a contention-aware spectrum allocation (CASA) scheme for EON built with the proposed TOXCs. We show by simulation that the TOXCs reduce the network capital expenditure transiting from WDM optical network to EON about 50%, with a minor traffic blocking performance degradation and about 10% accommodated traffic number detriment compared with all-SSS EON OXC architectures.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic.

PubMed

Gómez, José M; Verdú, Miguel

2017-03-06

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.

Percolation in real interdependent networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2015-07-01

The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Whereas theoretical methods of direct applicability to real isolated networks exist, the frameworks developed so far in percolation theory for interdependent network layers are of little help in practical contexts, as they are suited only for special models in the limit of infinite size. Here, we introduce a set of heuristic equations that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network. We demonstrate that percolation transitions in interdependent networks can be understood by decomposing these systems into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a smooth percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in small networks. We provide examples of real systems that have developed interdependent networks sharing cores of `high quality’ edges to prevent catastrophic failures.

Exact coupling threshold for structural transition reveals diversified behaviors in interconnected networks.

PubMed

Darabi Sahneh, Faryad; Scoglio, Caterina; Van Mieghem, Piet

2015-10-01

An interconnected network features a structural transition between two regimes [F. Radicchi and A. Arenas, Nat. Phys. 9, 717 (2013)]: one where the network components are structurally distinguishable and one where the interconnected network functions as a whole. Our exact solution for the coupling threshold uncovers network topologies with unexpected behaviors. Specifically, we show conditions that superdiffusion, introduced by Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013)], can occur despite the network components functioning distinctly. Moreover, we find that components of certain interconnected network topologies are indistinguishable despite very weak coupling between them.

Roll-Axis Hydrofluidic Stability Augmentation System Development

DTIC Science & Technology

1975-09-01

lifi .1035 SW 30 left for znro time delay - r Ight for other. 17 Preceding page Hank Recordings of the simulated aircraft performance to...DESIGN The analytical effort defined the gains and shaping networks required for the roll-axis damper system for the OH-58A helicopter, and the...Shaping Networks Usually a combination of resistors and capacitors (bellows) is designed to provide the following functions: a) b) 3.1.4 1 Lag

From scale-free to Erdos-Rényi networks.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2006-05-01

We analyze a model that interpolates between scale-free and Erdos-Rényi networks. The model introduced generates a one-parameter family of networks and allows one to analyze the role of structural heterogeneity. Analytical calculations are compared with extensive numerical simulations in order to describe the transition between these two important classes of networks. Finally, an application of the proposed model to the study of the percolation transition is presented.

System-wide analysis of the transcriptional network of human myelomonocytic leukemia cells predicts attractor structure and phorbol-ester-induced differentiation and dedifferentiation transitions

NASA Astrophysics Data System (ADS)

Sakata, Katsumi; Ohyanagi, Hajime; Sato, Shinji; Nobori, Hiroya; Hayashi, Akiko; Ishii, Hideshi; Daub, Carsten O.; Kawai, Jun; Suzuki, Harukazu; Saito, Toshiyuki

2015-02-01

We present a system-wide transcriptional network structure that controls cell types in the context of expression pattern transitions that correspond to cell type transitions. Co-expression based analyses uncovered a system-wide, ladder-like transcription factor cluster structure composed of nearly 1,600 transcription factors in a human transcriptional network. Computer simulations based on a transcriptional regulatory model deduced from the system-wide, ladder-like transcription factor cluster structure reproduced expression pattern transitions when human THP-1 myelomonocytic leukaemia cells cease proliferation and differentiate under phorbol myristate acetate stimulation. The behaviour of MYC, a reprogramming Yamanaka factor that was suggested to be essential for induced pluripotent stem cells during dedifferentiation, could be interpreted based on the transcriptional regulation predicted by the system-wide, ladder-like transcription factor cluster structure. This study introduces a novel system-wide structure to transcriptional networks that provides new insights into network topology.

Study of aerodynamic technology for VSTOL fighter/attack aircraft, phase 1

NASA Technical Reports Server (NTRS)

Driggers, H. H.

1978-01-01

A conceptual design study was performed of a vertical attitude takeoff and landing (VATOL) fighter/attack aircraft. The configuration has a close-coupled canard-delta wing, side two-dimensional ramp inlets, and two augmented turbofan engines with thrust vectoring capability. Performance and sensitivities to objective requirements were calculated. Aerodynamic characteristics were estimated based on contractor and NASA wind tunnel data. Computer simulations of VATOL transitions were performed. Successful transitions can be made, even with series post-stall instabilities, if reaction controls are properly phased. Principal aerodynamic uncertainties identified were post-stall aerodynamics, transonic aerodynamics with thrust vectoring and inlet performance in VATOL transition. A wind tunnel research program was recommended to resolve the aerodynamic uncertainties.

Navigation Architecture for a Space Mobile Network

NASA Technical Reports Server (NTRS)

Valdez, Jennifer E.; Ashman, Benjamin; Gramling, Cheryl; Heckler, Gregory W.; Carpenter, Russell

2016-01-01

The Tracking and Data Relay Satellite System (TDRSS) Augmentation Service for Satellites (TASS) is a proposed beacon service to provide a global, space based GPS augmentation service based on the NASA Global Differential GPS (GDGPS) System. The TASS signal will be tied to the GPS time system and usable as an additional ranging and Doppler radiometric source. Additionally, it will provide data vital to autonomous navigation in the near Earth regime, including space weather information, TDRS ephemerides, Earth Orientation Parameters (EOP), and forward commanding capability. TASS benefits include enhancing situational awareness, enabling increased autonomy, and providing near real-time command access for user platforms. As NASA Headquarters' Space Communication and Navigation Office (SCaN) begins to move away from a centralized network architecture and towards a Space Mobile Network (SMN) that allows for user initiated services, autonomous navigation will be a key part of such a system. This paper explores how a TASS beacon service enables the Space Mobile Networking paradigm, what a typical user platform would require, and provides an in-depth analysis of several navigation scenarios and operations concepts. This paper provides an overview of the TASS beacon and its role within the SMN and user community. Supporting navigation analysis is presented for two user mission scenarios: an Earth observing spacecraft in low earth orbit (LEO), and a highly elliptical spacecraft in a lunar resonance orbit. These diverse flight scenarios indicate the breadth of applicability of the TASS beacon for upcoming users within the current network architecture and in the SMN.

Deep Convolutional Neural Networks for Endotracheal Tube Position and X-ray Image Classification: Challenges and Opportunities.

PubMed

Lakhani, Paras

2017-08-01

The goal of this study is to evaluate the efficacy of deep convolutional neural networks (DCNNs) in differentiating subtle, intermediate, and more obvious image differences in radiography. Three different datasets were created, which included presence/absence of the endotracheal (ET) tube (n = 300), low/normal position of the ET tube (n = 300), and chest/abdominal radiographs (n = 120). The datasets were split into training, validation, and test. Both untrained and pre-trained deep neural networks were employed, including AlexNet and GoogLeNet classifiers, using the Caffe framework. Data augmentation was performed for the presence/absence and low/normal ET tube datasets. Receiver operating characteristic (ROC), area under the curves (AUC), and 95% confidence intervals were calculated. Statistical differences of the AUCs were determined using a non-parametric approach. The pre-trained AlexNet and GoogLeNet classifiers had perfect accuracy (AUC 1.00) in differentiating chest vs. abdominal radiographs, using only 45 training cases. For more difficult datasets, including the presence/absence and low/normal position endotracheal tubes, more training cases, pre-trained networks, and data-augmentation approaches were helpful to increase accuracy. The best-performing network for classifying presence vs. absence of an ET tube was still very accurate with an AUC of 0.99. However, for the most difficult dataset, such as low vs. normal position of the endotracheal tube, DCNNs did not perform as well, but achieved a reasonable AUC of 0.81.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

Secondary fuel delivery system

DOEpatents

Parker, David M.; Cai, Weidong; Garan, Daniel W.; Harris, Arthur J.

2010-02-23

A secondary fuel delivery system for delivering a secondary stream of fuel and/or diluent to a secondary combustion zone located in the transition piece of a combustion engine, downstream of the engine primary combustion region is disclosed. The system includes a manifold formed integral to, and surrounding a portion of, the transition piece, a manifold inlet port, and a collection of injection nozzles. A flowsleeve augments fuel/diluent flow velocity and improves the system cooling effectiveness. Passive cooling elements, including effusion cooling holes located within the transition boundary and thermal-stress-dissipating gaps that resist thermal stress accumulation, provide supplemental heat dissipation in key areas. The system delivers a secondary fuel/diluent mixture to a secondary combustion zone located along the length of the transition piece, while reducing the impact of elevated vibration levels found within the transition piece and avoiding the heat dissipation difficulties often associated with traditional vibration reduction methods.

Using Twitter to access the human right of communication for people who use Augmentative and Alternative Communication (AAC).

PubMed

Hemsley, Bronwyn; Palmer, Stuart; Dann, Stephen; Balandin, Susan

2018-02-01

Articles 19, 26 and 27 of the Universal Declaration of Human Rights and Articles 4, 9 and 21 of the Convention on the Rights of Persons with Disabilities promote the human rights of communication, education, use of technology and access to information. Social media is an important form of online communication, and Twitter increases users' visibility, influence and reach online. The aim of this sociotechnical research was to determine the impact of teaching three people who use Augmentative and Alternative Communication (AAC) to use Twitter. Three participants were trained in ways of using Twitter strategically. Data collected from participants' Twitter profiles were examined to determine the impact of training on Twitter follower count, frequency of tweeting, tweet content and the development of social networks. Data were also examined using (1) KH Coder software analysis and visualisation of co-occurring networks in the text data, based on word frequencies; and (2) Gephi software analysis to show the Twitter network for each participant. Two participants showed an improvement in Twitter skills and strategies. Twitter can be used to improve social connectedness of people who use AAC, and should not be overlooked in relation to communication rights.

A general stochastic model for studying time evolution of transition networks

NASA Astrophysics Data System (ADS)

Zhan, Choujun; Tse, Chi K.; Small, Michael

2016-12-01

We consider a class of complex networks whose nodes assume one of several possible states at any time and may change their states from time to time. Such networks represent practical networks of rumor spreading, disease spreading, language evolution, and so on. Here, we derive a model describing the dynamics of this kind of network and a simulation algorithm for studying the network evolutionary behavior. This model, derived at a microscopic level, can reveal the transition dynamics of every node. A numerical simulation is taken as an ;experiment; or ;realization; of the model. We use this model to study the disease propagation dynamics in four different prototypical networks, namely, the regular nearest-neighbor (RN) network, the classical Erdös-Renyí (ER) random graph, the Watts-Strogátz small-world (SW) network, and the Barabási-Albert (BA) scalefree network. We find that the disease propagation dynamics in these four networks generally have different properties but they do share some common features. Furthermore, we utilize the transition network model to predict user growth in the Facebook network. Simulation shows that our model agrees with the historical data. The study can provide a useful tool for a more thorough understanding of the dynamics networks.

Cascading failures in interdependent networks with finite functional components

NASA Astrophysics Data System (ADS)

Di Muro, M. A.; Buldyrev, S. V.; Stanley, H. E.; Braunstein, L. A.

2016-10-01

We present a cascading failure model of two interdependent networks in which functional nodes belong to components of size greater than or equal to s . We find theoretically and via simulation that in complex networks with random dependency links the transition is first order for s ≥3 and continuous for s =2 . We also study interdependent lattices with a distance constraint r in the dependency links and find that increasing r moves the system from a regime without a phase transition to one with a second-order transition. As r continues to increase, the system collapses in a first-order transition. Each regime is associated with a different structure of domain formation of functional nodes.

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Multiple tipping points and optimal repairing in interacting networks

PubMed Central

Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

2016-01-01

Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

In-transit temperature extremes could have negative effects on ladybird (Coleomegilla maculata) hatch rate

USDA-ARS?s Scientific Manuscript database

The shipment of mass-produced natural enemies for augmentative release is a standard procedure used by the biological control industry. Yet there has been insufficient research on the effects of temperature change, experienced during shipment, on the quality of predators as they arrive at release si...

Internet-Based Methods to Construct a Stakeholder Network for the Sustainability of Narragansett Bay, Rhode Island

EPA Science Inventory

Background\\Questions\\Methods Conservation coalitions, where numerous organizations collaborate for the augmented environmental protection of a critical habitat, have been shown to reduce redundancy and increase effectiveness. In order to initiate an effective conservation coalit...

Augmenting an observation network to facilitate flow and transport model discrimination.

USDA-ARS?s Scientific Manuscript database

Improving understanding of subsurface conditions includes performance comparison for competing models, independently developed or obtained via model abstraction. The model comparison and discrimination can be improved if additional observations will be included. The objective of this work was to i...

Teaching with Transmedia

ERIC Educational Resources Information Center

Pence, Harry E.

2012-01-01

The media environment is currently being dramatically changed by social networking, mobile computing, augmented reality, and transmedia. Of these four, transmedia is probably the least familiar to most educators. Transmedia enhances a central story idea with a variety of media components that provide additional information, give increased…

Detection of Intermediate-Period Transiting Planets with a Network of Small Telescopes: transitsearch.org

NASA Astrophysics Data System (ADS)

Seagroves, Scott; Harker, Justin; Laughlin, Gregory; Lacy, Justin; Castellano, Tim

2003-12-01

We describe a project (transitsearch.org) currently attempting to discover transiting intermediate-period planets orbiting bright parent stars, and we simulate that project's performance. The discovery of such a transit would be an important astronomical advance, bridging the critical gap in understanding between HD 209458b and Jupiter. However, the task is made difficult by intrinsically low transit probabilities and small transit duty cycles. This project's efficient and economical strategy is to photometrically monitor stars that are known (from radial velocity surveys) to bear planets, using a network of widely spaced observers with small telescopes. These observers, each individually capable of precision (1%) differential photometry, monitor candidates during the time windows in which the radial velocity solution predicts a transit if the orbital inclination is close to 90°. We use Monte Carlo techniques to simulate the performance of this network, performing simulations with different configurations of observers in order to optimize coordination of an actual campaign. Our results indicate that transitsearch.org can reliably rule out or detect planetary transits within the current catalog of known planet-bearing stars. A distributed network of skilled amateur astronomers and small college observatories is a cost-effective method for discovering the small number of transiting planets with periods in the range 10 days

Precise Calculation of a Bond Percolation Transition and Survival Rates of Nodes in a Complex Network

PubMed Central

Kawamoto, Hirokazu; Takayasu, Hideki; Jensen, Henrik Jeldtoft; Takayasu, Misako

2015-01-01

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition. PMID:25885791

Social influence in small-world networks

NASA Astrophysics Data System (ADS)

Sun, Kai; Mao, Xiao-Ming; Ouyang, Qi

2002-12-01

We report on our numerical studies of the Axelrod model for social influence in small-world networks. Our simulation results show that the topology of the network has a crucial effect on the evolution of cultures. As the randomness of the network increases, the system undergoes a transition from a highly fragmented phase to a uniform phase. We also find that the power-law distribution at the transition point, reported by Castellano et al, is not a critical phenomenon; it exists not only at the onset of transition but also for almost any control parameters. All these power-law distributions are stable against perturbations. A mean-field theory is developed to explain these phenomena.

Cranial implant design using augmented reality immersive system.

PubMed

Ai, Zhuming; Evenhouse, Ray; Leigh, Jason; Charbel, Fady; Rasmussen, Mary

2007-01-01

Software tools that utilize haptics for sculpting precise fitting cranial implants are utilized in an augmented reality immersive system to create a virtual working environment for the modelers. The virtual environment is designed to mimic the traditional working environment as closely as possible, providing more functionality for the users. The implant design process uses patient CT data of a defective area. This volumetric data is displayed in an implant modeling tele-immersive augmented reality system where the modeler can build a patient specific implant that precisely fits the defect. To mimic the traditional sculpting workspace, the implant modeling augmented reality system includes stereo vision, viewer centered perspective, sense of touch, and collaboration. To achieve optimized performance, this system includes a dual-processor PC, fast volume rendering with three-dimensional texture mapping, the fast haptic rendering algorithm, and a multi-threading architecture. The system replaces the expensive and time consuming traditional sculpting steps such as physical sculpting, mold making, and defect stereolithography. This augmented reality system is part of a comprehensive tele-immersive system that includes a conference-room-sized system for tele-immersive small group consultation and an inexpensive, easily deployable networked desktop virtual reality system for surgical consultation, evaluation and collaboration. This system has been used to design patient-specific cranial implants with precise fit.

Mentoring in the Context of Latino Youth's Broader Village during Their Transition from High School

ERIC Educational Resources Information Center

Sanchez, Bernadette; Esparza, Patricia; Berardi, Luciano; Pryce, Julia

2011-01-01

The aims of this study were to examine the mentoring and social network experiences of Latino youth during the high school transition. A mixed-methods approach was used to examine participants' natural mentoring relationships before and after the transition along with the broader social networks of youth. A total of 32 Latino participants…

Falling Behind: Lingering Costs of the High School Transition for Youth Friendships and Grades

ERIC Educational Resources Information Center

Felmlee, Diane; McMillan, Cassie; Inara Rodis, Paulina; Osgood, D. Wayne

2018-01-01

This study investigates the influence of structural transitions to high school on adolescents' friendship networks and academic grades from 6th through 12th grade, in a direct comparison of students who do and do not transition. We utilize data from 14,462 youth in 51 networks from 26 districts (Promoting School-Community Partnerships to Enhance…

Crestal Sinus Augmentation with Recombinant Human Bone Morphogenetic Protein 2: Clinical and Radiographic Outcomes of 2-Year Pilot Trial.

PubMed

Kuchler, Ulrike; Rudelstorfer, Claudia M; Barth, Barbara; Tepper, Gabor; Lidinsky, Dominika; Heimel, Patrick; Watzek, Georg; Gruber, Reinhard

Recombinant human bone morphogenetic protein 2 (rhBMP-2) together with an absorbable collagen carrier (ACS) was approved for augmentation of the maxillary sinus prior to implant placement. The original registration trial was based on a lateral window approach. Clinical outcomes of crestal sinus augmentation with rhBMP-2 have not been reported so far. An uncontrolled pilot trial in which seven patients with a residual maxillary height below 5 mm were enrolled to receive crestal sinus augmentation with rhBMP-2/ACS was conducted. Elevation of the sinus mucosa was performed by gel pressure. Primary endpoints were the gain in augmentation height and volume measured by computed tomography after 6 months. Evaluation of bone quality at the time of implant placement was based on histology. Secondary endpoints were the clinical and radiologic evaluation of the implants and patient satisfaction by visual analog scale (VAS) at the 2-year follow-up. Median gain in augmentation height was 7.2 mm (range 0.0 to 17.5 mm). Five patients gained at least 5 mm of bone height. Two patients with a perforation of the sinus mucosa failed to respond to rhBMP-2/ACS and underwent lateral window augmentation. The median gain in augmentation volume of the five patients was 781.3 mm³ (range 426.9 to 1,242.8 mm³). Biopsy specimens showed a cancellous network consisting of primary plexiform bone with little secondary lamellar bone. After 2 years, implants were in function with no signs of inflammation or peri-implant bone loss. Patients were satisfied with the esthetic outcomes and chewing function. This pilot clinical trial supports the original concept that rhBMP-2/ACS supports bone formation, also in crestal sinus augmentation, and emphasizes the relevance of the integrity of the sinus mucosa to predict the bone gain.

Analyzing GAIAN Database (GaianDB) on a Tactical Network

DTIC Science & Technology

2015-11-30

we connected 3 Raspberry Pi’s running GaianDB and our augmented version of splatform to a network of 3 CSRs. The Raspberry Pi is a low power, low...based on Debian from a connected secure digital high capacity (SDHC) card or a universal serial bus (USB) device. The Raspberry Pi comes equipped with...requirements, capabilities, and cost make the Raspberry Pi a useful device for sensor experimentation. From there, we performed 3 types of benchmarks

Percolation of spatially constraint networks

NASA Astrophysics Data System (ADS)

Li, Daqing; Li, Guanliang; Kosmidis, Kosmas; Stanley, H. E.; Bunde, Armin; Havlin, Shlomo

2011-03-01

We study how spatial constraints are reflected in the percolation properties of networks embedded in one-dimensional chains and two-dimensional lattices. We assume long-range connections between sites on the lattice where two sites at distance r are chosen to be linked with probability p(r)~r-δ. Similar distributions have been found in spatially embedded real networks such as social and airline networks. We find that for networks embedded in two dimensions, with 2<δ<4, the percolation properties show new intermediate behavior different from mean field, with critical exponents that depend on δ. For δ<2, the percolation transition belongs to the universality class of percolation in Erdös-Rényi networks (mean field), while for δ>4 it belongs to the universality class of percolation in regular lattices. For networks embedded in one dimension, we find that, for δ<1, the percolation transition is mean field. For 1<δ<2, the critical exponents depend on δ, while for δ>2 there is no percolation transition as in regular linear chains.

Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Meng, Qiang

2014-05-01

This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride (P&R) system and congestion pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users' route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users' generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit (bus) flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

PubMed

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

PubMed Central

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200

Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, E.; Morgan, M. J.; Biedron, S. G.

2009-01-01

This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by themore » PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.« less

Phase transitions in a multistate majority-vote model on complex networks

NASA Astrophysics Data System (ADS)

Chen, Hanshuang; Li, Guofeng

2018-06-01

We generalize the original majority-vote (MV) model from two states to arbitrary p states and study the order-disorder phase transitions in such a p -state MV model on complex networks. By extensive Monte Carlo simulations and a mean-field theory, we show that for p ≥3 the order of phase transition is essentially different from a continuous second-order phase transition in the original two-state MV model. Instead, for p ≥3 the model displays a discontinuous first-order phase transition, which is manifested by the appearance of the hysteresis phenomenon near the phase transition. Within the hysteresis loop, the ordered phase and disordered phase are coexisting, and rare flips between the two phases can be observed due to the finite-size fluctuation. Moreover, we investigate the type of phase transition under a slightly modified dynamics [Melo et al., J. Stat. Mech. (2010) P11032, 10.1088/1742-5468/2010/11/P11032]. We find that the order of phase transition in the three-state MV model depends on the degree heterogeneity of networks. For p ≥4 , both dynamics produce the first-order phase transitions.

Real-Time Minimization of Tracking Error for Aircraft Systems

NASA Technical Reports Server (NTRS)

Garud, Sumedha; Kaneshige, John T.; Krishnakumar, Kalmanje S.; Kulkarni, Nilesh V.; Burken, John

2013-01-01

This technology presents a novel, stable, discrete-time adaptive law for flight control in a Direct adaptive control (DAC) framework. Where errors are not present, the original control design has been tuned for optimal performance. Adaptive control works towards achieving nominal performance whenever the design has modeling uncertainties/errors or when the vehicle suffers substantial flight configuration change. The baseline controller uses dynamic inversion with proportional-integral augmentation. On-line adaptation of this control law is achieved by providing a parameterized augmentation signal to a dynamic inversion block. The parameters of this augmentation signal are updated to achieve the nominal desired error dynamics. If the system senses that at least one aircraft component is experiencing an excursion and the return of this component value toward its reference value is not proceeding according to the expected controller characteristics, then the neural network (NN) modeling of aircraft operation may be changed.

Augmented cholesterol absorption and sarcolemmal sterol enrichment slow small intestinal transit in mice, contributing to cholesterol cholelithogenesis

PubMed Central

Xie, Meimin; Kotecha, Vijay R; Andrade, Jon David P; Fox, James G; Carey, Martin C

2012-01-01

Cholesterol gallstones are associated with slow intestinal transit in humans as well as in animal models, but the molecular mechanism is unknown. We investigated in C57L/J mice whether the components of a lithogenic diet (LD; 1.0% cholesterol, 0.5% cholic acid and 17% triglycerides), as well as distal intestinal infection with Helicobacter hepaticus, influence small intestinal transit time. By quantifying the distribution of 3H-sitostanol along the length of the small intestine following intraduodenal instillation, we observed that, in both sexes, the geometric centre (dimensionless) was retarded significantly (P < 0.05) by LD but not slowed further by helicobacter infection (males, 9.4 ± 0.5 (uninfected), 9.6 ± 0.5 (infected) on LD compared with 12.5 ± 0.4 and 11.4 ± 0.5 on chow). The effect of the LD was reproduced only by the binary combination of cholesterol and cholic acid. We inferred that the LD-induced cholesterol enrichment of the sarcolemmae of intestinal smooth muscle cells produced hypomotility from signal-transduction decoupling of cholecystokinin (CCK), a physiological agonist for small intestinal propulsion in mice. Treatment with ezetimibe in an amount sufficient to block intestinal cholesterol absorption caused small intestinal transit time to return to normal. In most cholesterol gallstone-prone humans, lithogenic bile carries large quantities of hepatic cholesterol into the upper small intestine continuously, thereby reproducing this dietary effect in mice. Intestinal hypomotility promotes cholelithogenesis by augmenting formation of deoxycholate, a pro-lithogenic secondary bile salt, and increasing the fraction of intestinal cholesterol absorbed. PMID:22331417

The dynamic and geometric phase transition in the cellular network of pancreatic islet

NASA Astrophysics Data System (ADS)

Wang, Xujing

2013-03-01

The pancreatic islet is a micro-organ that contains several thousands of endocrine cells, majority of which being the insulin releasing β - cells . - cellsareexcitablecells , andarecoupledtoeachother through gap junctional channels. Here, using percolation theory, we investigate the role of network structure in determining the dynamics of the β-cell network. We show that the β-cell synchronization depends on network connectivity. More specifically, as the site occupancy is reducing, initially the β-cell synchronization is barely affected, until it reaches around a critical value, where the synchronization exhibit a sudden rapid decline, followed by an slow exponential tail. This critical value coincides with the critical site open probability for percolation transition. The dependence over bond strength is similar, exhibiting critical-behavior like dependence around a certain value of bond strength. These results suggest that the β-cell network undergoes a dynamic phase transition when the network is percolated. We further apply the findings to study diabetes. During the development of diabetes, the β - cellnetworkconnectivitydecreases . Siteoccupancyreducesfromthe reducing β-cell mass, and the bond strength is increasingly impaired from β-cell stress and chronic hyperglycemia. We demonstrate that the network dynamics around the percolation transition explain the disease dynamics around onset, including a long time mystery in diabetes, the honeymoon phenomenon.

Informal Networks in Youth Transitions in West Germany: Biographical Resource or Reproduction of Social Inequality?

ERIC Educational Resources Information Center

Walther, Andreas; Stauber, Barbara; Pohl, Axel

2005-01-01

This article deals with informal networks and their role in young people's strategies of coping with the uncertainties of transitions to work. The underlying hypothesis is that informal networks have a high potential in this regard that, however, is strongly differentiated according to class and education. Drawing on West German data from the…

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

Network inoculation: Heteroclinics and phase transitions in an epidemic model

NASA Astrophysics Data System (ADS)

Yang, Hui; Rogers, Tim; Gross, Thilo

2016-08-01

In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.

Immersive Technologies and Language Learning

ERIC Educational Resources Information Center

Blyth, Carl

2018-01-01

This article briefly traces the historical conceptualization of linguistic and cultural immersion through technological applications, from the early days of locally networked computers to the cutting-edge technologies known as virtual reality and augmented reality. Next, the article explores the challenges of immersive technologies for the field…

Flight evaluation of configuration management system concepts during transition to the landing approach for a powered-lift STOL aircraft

NASA Technical Reports Server (NTRS)

Franklin, J. A.; Innis, R. C.

1980-01-01

Flight experiments were conducted to evaluate two control concepts for configuration management during the transition to landing approach for a powered-lift STOL aircraft. NASA Ames' augmentor wing research aircraft was used in the program. Transitions from nominal level-flight configurations at terminal area pattern speeds were conducted along straight and curved descending flightpaths. Stabilization and command augmentation for attitude and airspeed control were used in conjunction with a three-cue flight director that presented commands for pitch, roll, and throttle controls. A prototype microwave system provided landing guidance. Results of these flight experiments indicate that these configuration management concepts permit the successful performance of transitions and approaches along curved paths by powered-lift STOL aircraft. Flight director guidance was essential to accomplish the task.

A deep semantic mobile application for thyroid cytopathology

NASA Astrophysics Data System (ADS)

Kim, Edward; Corte-Real, Miguel; Baloch, Zubair

2016-03-01

Cytopathology is the study of disease at the cellular level and often used as a screening tool for cancer. Thyroid cytopathology is a branch of pathology that studies the diagnosis of thyroid lesions and diseases. A pathologist views cell images that may have high visual variance due to different anatomical structures and pathological characteristics. To assist the physician with identifying and searching through images, we propose a deep semantic mobile application. Our work augments recent advances in the digitization of pathology and machine learning techniques, where there are transformative opportunities for computers to assist pathologists. Our system uses a custom thyroid ontology that can be augmented with multimedia metadata extracted from images using deep machine learning techniques. We describe the utilization of a particular methodology, deep convolutional neural networks, to the application of cytopathology classification. Our method is able to leverage networks that have been trained on millions of generic images, to medical scenarios where only hundreds or thousands of images exist. We demonstrate the benefits of our framework through both quantitative and qualitative results.

Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

NASA Astrophysics Data System (ADS)

Gong, Yubing; Xie, Huijuan

2017-09-01

Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

NASA Astrophysics Data System (ADS)

Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

2017-08-01

Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

Statistical Physics of Cascading Failures in Complex Networks

NASA Astrophysics Data System (ADS)

Panduranga, Nagendra Kumar

Systems such as the power grid, world wide web (WWW), and internet are categorized as complex systems because of the presence of a large number of interacting elements. For example, the WWW is estimated to have a billion webpages and understanding the dynamics of such a large number of individual agents (whose individual interactions might not be fully known) is a challenging task. Complex network representations of these systems have proved to be of great utility. Statistical physics is the study of emergence of macroscopic properties of systems from the characteristics of the interactions between individual molecules. Hence, statistical physics of complex networks has been an effective approach to study these systems. In this dissertation, I have used statistical physics to study two distinct phenomena in complex systems: i) Cascading failures and ii) Shortest paths in complex networks. Understanding cascading failures is considered to be one of the "holy grails" in the study of complex systems such as the power grid, transportation networks, and economic systems. Studying failures of these systems as percolation on complex networks has proved to be insightful. Previously, cascading failures have been studied extensively using two different models: k-core percolation and interdependent networks. The first part of this work combines the two models into a general model, solves it analytically, and validates the theoretical predictions through extensive computer simulations. The phase diagram of the percolation transition has been systematically studied as one varies the average local k-core threshold and the coupling between networks. The phase diagram of the combined processes is very rich and includes novel features that do not appear in the models which study each of the processes separately. For example, the phase diagram consists of first- and second-order transition regions separated by two tricritical lines that merge together and enclose a two-stage transition region. In the two-stage transition, the size of the giant component undergoes a first-order jump at a certain occupation probability followed by a continuous second-order transition at a smaller occupation probability. Furthermore, at certain fixed interdependencies, the percolation transition cycles from first-order to second-order to two-stage to first-order as the k-core threshold is increased. We setup the analytical equations describing the phase boundaries of the two-stage transition region and we derive the critical exponents for each type of transition. Understanding the shortest paths between individual elements in systems like communication networks and social media networks is important in the study of information cascades in these systems. Often, large heterogeneity can be present in the connections between nodes in these networks. Certain sets of nodes can be more highly connected among themselves than with the nodes from other sets. These sets of nodes are often referred to as 'communities'. The second part of this work studies the effect of the presence of communities on the distribution of shortest paths in a network using a modular Erdős-Renyi network model. In this model, the number of communities and the degree of modularity of the network can be tuned using the parameters of the model. We find that the model reaches a percolation threshold while tuning the degree of modularity of the network and the distribution of the shortest paths in the network can be used as an indicator of how the communities are connected.

"The Only Future Certainty Is that I'll Still Be Speaking to Her": Social Capital/Network for the Transition of Disadvantaged Young People

ERIC Educational Resources Information Center

Inui, Akio; Nishimura, Takayuki

2007-01-01

This paper examines the significance of social network and social capital in youth transition from school to work, with a focus on both instrumental and expressive aspects. In recent years the transition of Japanese young people has changed drastically, similar to young people in other industrialised countries. The individualisation of transition…

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Ice-coupled wave propagation across an abrupt change in ice rigidity, density, or thickness

NASA Astrophysics Data System (ADS)

Barrett, Murray D.; Squire, Vernon A.

1996-09-01

The model of Fox and Squire [1990, 1991, 1994], which discusses the oblique propagation of surface gravity waves from the open sea into an ice sheet of constant thickness and properties, is augmented to include propagation across an abrupt transition of properties within a continuous ice sheet or across two dissimilar ice sheets that abut one another but are free to move independently. Rigidity, thickness, and/or density may change across the transition, allowing, for example, the modeling of ice-coupled waves into, across, and out of refrozen leads and polynyas, across cracks, and through coherent pressure ridges. Reflection and transmission behavior is reported for various changes in properties under both types of transition conditions.

Reconstruction of stochastic temporal networks through diffusive arrival times

NASA Astrophysics Data System (ADS)

Li, Xun; Li, Xiang

2017-06-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications.

Reconstruction of stochastic temporal networks through diffusive arrival times

PubMed Central

Li, Xun; Li, Xiang

2017-01-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications. PMID:28604687

Rochester New York Integrated Transit Demonstration : Volume 3. Appendices.

DOT National Transportation Integrated Search

1979-03-01

The Rochester Integrated Transit Demonstration (RITD) was designed to assess the roles of demand-responsive transit services in a regionwide transit system that includes an extensive fixed-route bus network. The demonstration extended transit service...

Rochester New York Integrated Transit Demonstration : Volume 2. Evaluation Report.

DOT National Transportation Integrated Search

1979-03-01

The Rochester Integrated Transit Demonstration (RITD) was designed to assess the roles of demand-responsive transit services in a regionwide transit system that includes an extensive fixed-route bus network. The demonstration extended transit service...

Rochester New York Integrated Transit Demonstration : Volume 1. Executive Summary.

DOT National Transportation Integrated Search

1979-03-01

The Rochester Integrated Transit Demonstration (RITD) was designed to assess the roles of demand-responsive transit services in a regionwide transit system that includes an extensive fixed-route bus network. The demonstration extended transit service...

Network rewiring dynamics with convergence towards a star network

PubMed Central

Dick, G.; Parry, M.

2016-01-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature 393, 440–442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach. PMID:27843396

Network rewiring dynamics with convergence towards a star network.

PubMed

Whigham, P A; Dick, G; Parry, M

2016-10-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.

Mental Illness as a Barrier to Marriage among Unmarried Mothers

ERIC Educational Resources Information Center

Teitler, Julien O.; Reichman, Nancy E.

2008-01-01

This study explores how mental illness shapes transitions to marriage among unwed mothers using augmented data from the Fragile Families and Child Wellbeing study (N = 2,351). We estimate proportional hazard models to assess the effects of mental illness on the likelihood of marriage over a 5-year period following a nonmarital birth. Diagnosed…

Employer Understanding of Work-Integrated Learning and the Challenges of Engaging in Work Placement Opportunities

ERIC Educational Resources Information Center

Jackson, Denise; Rowbottom, David; Ferns, Sonia; McLaren, Diane

2017-01-01

This study examines employer understanding of Work-Integrated Learning (WIL), reasons for participation and the challenges and barriers posed during the WIL process. This is important given the drive to grow WIL, augmented by the National Strategy for WIL, and the significant benefits it holds in preparing students for their transition to…

Bionic Manufacturing: Towards Cyborg Cells and Sentient Microbots.

PubMed

Srivastava, Sarvesh Kumar; Yadav, Vikramaditya G

2018-05-01

Bio-inspired engineering applies biological design principles towards developing engineering solutions but is not practical as a manufacturing paradigm. We advocate 'bionic manufacturing', a synergistic fusion of biotic and abiotic components, to transition away from bio-inspiration toward bio-augmentation to address current limitations in bio-inspired manufacturing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adsorbate Diffusion on Transition Metal Nanoparticles

DTIC Science & Technology

2015-01-01

different sizes and shapes using density functional theory calculations. We show that nanoparticles bind adsorbates more strongly than the...structure theoretical methods, a quantitative study with accurate density functional theory (DFT) calculations is still missing. Here, we perform a...functional theory . The projector augmented wave (PAW) potentials29,30 were used for electron- ion interactions and the generalized gradient approximation

Navigating Learning Journeys of Online Teachers: Threshold Concepts and Self-Efficacy

ERIC Educational Resources Information Center

Northcote, Maria; Gosselin, Kevin P.; Reynaud, Daniel; Kilgour, Peter; Anderson, Malcolm

2015-01-01

Higher education institutions are developing more and more online courses to supplement and augment the courses they offer in on-campus modes. In fact, some universities now offer the majority of their courses through online contexts. However, for academic staff who design and teach these courses, the transition from teaching on-campus courses to…

TV audio and video on the same channel

NASA Technical Reports Server (NTRS)

Hopkins, J. B.

1979-01-01

Transmitting technique adds audio to video signal during vertical blanking interval. SIVI (signal in the vertical interval) is used by TV networks and stations to transmit cuing and automatic-switching tone signals to augment automatic and manual operations. It can also be used to transmit one-way instructional information, such as bulletin alerts, program changes, and commercial-cutaway aural cues from the networks to affiliates. Additonally, it can be used as extra sound channel for second-language transmission to biligual stations.

Augmentation-related brain plasticity

PubMed Central

Di Pino, Giovanni; Maravita, Angelo; Zollo, Loredana; Guglielmelli, Eugenio; Di Lazzaro, Vincenzo

2014-01-01

Today, the anthropomorphism of the tools and the development of neural interfaces require reconsidering the concept of human-tools interaction in the framework of human augmentation. This review analyses the plastic process that the brain undergoes when it comes into contact with augmenting artificial sensors and effectors and, on the other hand, the changes that the use of external augmenting devices produces in the brain. Hitherto, few studies investigated the neural correlates of augmentation, but clues on it can be borrowed from logically-related paradigms: sensorimotor training, cognitive enhancement, cross-modal plasticity, sensorimotor functional substitution, use and embodiment of tools. Augmentation modifies function and structure of a number of areas, i.e., primary sensory cortices shape their receptive fields to become sensitive to novel inputs. Motor areas adapt the neuroprosthesis representation firing-rate to refine kinematics. As for normal motor outputs, the learning process recruits motor and premotor cortices and the acquisition of proficiency decreases attentional recruitment, focuses the activity on sensorimotor areas and increases the basal ganglia drive on the cortex. Augmentation deeply relies on the frontoparietal network. In particular, premotor cortex is involved in learning the control of an external effector and owns the tool motor representation, while the intraparietal sulcus extracts its visual features. In these areas, multisensory integration neurons enlarge their receptive fields to embody supernumerary limbs. For operating an anthropomorphic neuroprosthesis, the mirror system is required to understand the meaning of the action, the cerebellum for the formation of its internal model and the insula for its interoception. In conclusion, anthropomorphic sensorized devices can provide the critical sensory afferences to evolve the exploitation of tools through their embodiment, reshaping the body representation and the sense of the self. PMID:24966816

Teaching Psychological and Social Gerontology to Millennial Undergraduates

ERIC Educational Resources Information Center

Siegal, Brittany; Kagan, Sarah H.

2012-01-01

Matters of development and generation may create barriers in teaching millennial undergraduates psychological and social gerontology. We introduce strategy to mitigate these barriers by teaching psychological and social gerontology as undergraduate honors courses, augmented with the use of social networking tools. We detail honors programming,…

A Vocabulary-Added Reading Intervention for English Learners At-Risk of Reading Difficulties

ERIC Educational Resources Information Center

Filippini, Alexis L.; Gerber, Michael M.; Leafstedt, Jill M.

2012-01-01

This study examined the added value of a vocabulary plus phonological awareness (vocab+) intervention against a phonological awareness (PA only) intervention only. The vocabulary intervention built networks among words through attention to morphological and semantic relationships. This supplementary classroom instruction augmented existing…

Social climber attachment in forming networks produces a phase transition in a measure of connectivity

NASA Astrophysics Data System (ADS)

Taylor, Dane; Larremore, Daniel B.

2012-09-01

The formation and fragmentation of networks are typically studied using percolation theory, but most previous research has been restricted to studying a phase transition in cluster size, examining the emergence of a giant component. This approach does not study the effects of evolving network structure on dynamics that occur at the nodes, such as the synchronization of oscillators and the spread of information, epidemics, and neuronal excitations. We introduce and analyze an alternative link-formation rule, called social climber (SC) attachment, that may be combined with arbitrary percolation models to produce a phase transition using the largest eigenvalue of the network adjacency matrix as the order parameter. This eigenvalue is significant in the analyses of many network-coupled dynamical systems in which it measures the quality of global coupling and is hence a natural measure of connectivity. We highlight the important self-organized properties of SC attachment and discuss implications for controlling dynamics on networks.

Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses.

PubMed

Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile

2015-02-01

Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Axillary silicone lymphadenopathy secondary to augmentation mammaplasty

PubMed Central

Dragoumis, Dimitrios M.; Assimaki, Anthoula S.; Vrizas, Triantafyllos I.; Tsiftsoglou, Aris P.

2010-01-01

We report a case involving a 45-year-old woman, who presented with an axillary mass 10 years after bilateral cosmetic augmentation mammaplasty. A lump was detected in the left axilla, and subsequent mammography and magnetic resonance imaging demonstrated intracapsular rupture of the left breast prosthesis. An excisional biopsy of the left axillary lesion and replacement of the ruptured implant was performed. Histological analysis showed that the axillary lump was lymph nodes containing large amounts of silicone. Silicone lymphadenopathy is an obscure complication of procedures involving the use of silicone. It is thought to occur following the transit of silicone droplets from breast implants to lymph nodes by macrophages and should always be considered as a differential diagnosis in patients in whom silicone prostheses are present. PMID:21217983

Linear Augmentation for Stabilizing Stationary Solutions: Potential Pitfalls and Their Application

PubMed Central

Karnatak, Rajat

2015-01-01

Linear augmentation has recently been shown to be effective in targeting desired stationary solutions, suppressing bistablity, in regulating the dynamics of drive response systems and in controlling the dynamics of hidden attractors. The simplicity of the procedure is the main highlight of this scheme but questions related to its general applicability still need to be addressed. Focusing on the issue of targeting stationary solutions, this work demonstrates instances where the scheme fails to stabilize the required solutions and leads to other complicated dynamical scenarios. Examples from conservative as well as dissipative systems are presented in this regard and important applications in dissipative predator—prey systems are discussed, which include preventative measures to avoid potentially catastrophic dynamical transitions in these systems. PMID:26544879

Exacerbated vulnerability of coupled socio-economic risk in complex networks

NASA Astrophysics Data System (ADS)

Zhang, Xin; Feng, Ling; Berman, Yonatan; Hu, Ning; Stanley, H. Eugene

2016-10-01

The study of risk contagion in economic networks has most often focused on the financial liquidities of institutions and assets. In practice the agents in a network affect each other through social contagion, i.e., through herd behavior and the tendency to follow leaders. We study the coupled risk between social and economic contagion and find it significantly more severe than when economic risk is considered alone. Using the empirical network from the China venture capital market we find that the system exhibits an extreme risk of abrupt phase transition and large-scale damage, which is in clear contrast to the smooth phase transition traditionally observed in economic contagion alone. We also find that network structure impacts market resilience and that the randomization of the social network of the market participants can reduce system fragility when there is herd behavior. Our work indicates that under coupled contagion mechanisms network resilience can exhibit a fundamentally different behavior, i.e., an abrupt transition. It also reveals the extreme risk when a system has coupled socio-economic risks, and this could be of interest to both policy makers and market practitioners.

Phase transitions in cooperative coinfections: Simulation results for networks and lattices

NASA Astrophysics Data System (ADS)

Grassberger, Peter; Chen, Li; Ghanbarnejad, Fakhteh; Cai, Weiran

2016-04-01

We study the spreading of two mutually cooperative diseases on different network topologies, and with two microscopic realizations, both of which are stochastic versions of a susceptible-infected-removed type model studied by us recently in mean field approximation. There it had been found that cooperativity can lead to first order transitions from spreading to extinction. However, due to the rapid mixing implied by the mean field assumption, first order transitions required nonzero initial densities of sick individuals. For the stochastic model studied here the results depend strongly on the underlying network. First order transitions are found when there are few short but many long loops: (i) No first order transitions exist on trees and on 2-d lattices with local contacts. (ii) They do exist on Erdős-Rényi (ER) networks, on d -dimensional lattices with d ≥4 , and on 2-d lattices with sufficiently long-ranged contacts. (iii) On 3-d lattices with local contacts the results depend on the microscopic details of the implementation. (iv) While single infected seeds can always lead to infinite epidemics on regular lattices, on ER networks one sometimes needs finite initial densities of infected nodes. (v) In all cases the first order transitions are actually "hybrid"; i.e., they display also power law scaling usually associated with second order transitions. On regular lattices, our model can also be interpreted as the growth of an interface due to cooperative attachment of two species of particles. Critically pinned interfaces in this model seem to be in different universality classes than standard critically pinned interfaces in models with forbidden overhangs. Finally, the detailed results mentioned above hold only when both diseases propagate along the same network of links. If they use different links, results can be rather different in detail, but are similar overall.

Intelligent Resource Management for Local Area Networks: Approach and Evolution

NASA Technical Reports Server (NTRS)

Meike, Roger

1988-01-01

The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.

Phase Transition in Opinion Diffusion in Social Networks

DTIC Science & Technology

2012-05-01

the opinions of social agents diffuse in a network under a so-called hard-interaction model, in which the agents inter- act more strongly with...gent behavior. Index Terms— opinion diffusion , opinion dynamics, social net- works, phase transition, herding. 1. INTRODUCTION The study of the

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

NASA Astrophysics Data System (ADS)

Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

1994-03-01

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

ACTS TDMA network control. [Advanced Communication Technology Satellite

NASA Technical Reports Server (NTRS)

Inukai, T.; Campanella, S. J.

1984-01-01

This paper presents basic network control concepts for the Advanced Communications Technology Satellite (ACTS) System. Two experimental systems, called the low-burst-rate and high-burst-rate systems, along with ACTS ground system features, are described. The network control issues addressed include frame structures, acquisition and synchronization procedures, coordinated station burst-time plan and satellite-time plan changes, on-board clock control based on ground drift measurements, rain fade control by means of adaptive forward-error-correction (FEC) coding and transmit power augmentation, and reassignment of channel capacities on demand. The NASA ground system, which includes a primary station, diversity station, and master control station, is also described.

Two coupled effects of sub micron silica particles on the mechanical relaxation behavior of ethylene-propylene-diene rubber chains.

PubMed

Gu, Zhen; Zhang, Xian; Ding, Xin; Bao, Chao; Fang, Fei; Li, Shiyuan; Zhou, Haifeng; Xue, Meng; Wang, Huan; Tian, Xingyou

2014-08-28

This article studied the influence of silica (SiO2) particles on the crosslinked network and the molecular mobility of ethylene-propylene-diene (EPDM) rubber chains by dynamic mechanical analysis (DMA). When SiO2 fraction is lower than 8 phr, the chain segments that participate in the glass-rubber transition (α transition) decrease with increasing the SiO2 content, while the whole crosslinked network is almost unaffected by the presence of SiO2. When the SiO2 fraction increases to about 20 phr, there appears a new tan δ peak (α' transition) above the α transition. This could be because the crosslinking reaction took place only on a small scale and the formed network became gradually incomplete when the content of the particles exceeded some critical value, and the α' transition is attributed primarily to the motion of non-elastic network chains loosely attached to the three-dimensional network. However, at SiO2 loadings higher than 40 phr, the crosslinking density was kept basically constant. The α' transition is hindered by a restriction of the chain mobility due to SiO2. The different changes of α' transition depended on the two coupled effects of SiO2, including restricting the chain mobility and decreasing the crosslinking density. Correspondingly, with increasing the mobility of EPDM chains and SiO2-induced strengthening, the mechanical properties of EPDM composite are dramatically improved. With the addition of 20 phr of SiO2 in the EPDM, a 113% increase in the elongation at break, a 510% increase in the fracture energy, and a 283% increase in the tensile strength are achieved.

Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network

PubMed Central

Yin, Haodong; Han, Baoming; Li, Dewei; Wu, Jianjun; Sun, Huijun

2016-01-01

A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization model is mathematically constructed based on 0–1 integer programming to describe passengers’ responses to alternative origin station closure scenarios and destination station closure scenarios; this model also considers the availability of multi-mode transportation and the uncertain duration of the station closure; (2) An integrated solution algorithm based on the passenger simulation is developed to solve the proposed model and to estimate the effects of a station closure on passenger demand in a rail transit network. Furthermore, 13 groups of numerical experiments based on the Beijing rail transit network are performed as case studies with 2,074,267 records of smart card data. The comparisons of the model outputs and the manual survey show that the accuracy of our proposed behavior optimization model is approximately 80%. The results also show that our model can be used to capture the passenger behavior and to quantitatively estimate the effects of alternative closure scenarios on passenger flow demand for the rail transit network. Moreover, the closure duration and its overestimation greatly influence the individual behavioral choices of the affected passengers and the passenger demand. Furthermore, if the rail transit operator can more accurately estimate the closure duration (namely, as g approaches 1), the impact of the closure can be somewhat mitigated. PMID:27935963

Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network.

PubMed

Yin, Haodong; Han, Baoming; Li, Dewei; Wu, Jianjun; Sun, Huijun

2016-01-01

A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization model is mathematically constructed based on 0-1 integer programming to describe passengers' responses to alternative origin station closure scenarios and destination station closure scenarios; this model also considers the availability of multi-mode transportation and the uncertain duration of the station closure; (2) An integrated solution algorithm based on the passenger simulation is developed to solve the proposed model and to estimate the effects of a station closure on passenger demand in a rail transit network. Furthermore, 13 groups of numerical experiments based on the Beijing rail transit network are performed as case studies with 2,074,267 records of smart card data. The comparisons of the model outputs and the manual survey show that the accuracy of our proposed behavior optimization model is approximately 80%. The results also show that our model can be used to capture the passenger behavior and to quantitatively estimate the effects of alternative closure scenarios on passenger flow demand for the rail transit network. Moreover, the closure duration and its overestimation greatly influence the individual behavioral choices of the affected passengers and the passenger demand. Furthermore, if the rail transit operator can more accurately estimate the closure duration (namely, as g approaches 1), the impact of the closure can be somewhat mitigated.

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

Probing many-body localization with neural networks

NASA Astrophysics Data System (ADS)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

A Networks Approach to Modeling Enzymatic Reactions.

PubMed

Imhof, P

2016-01-01

Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

Neural network tracking and extension of positive tracking periods

NASA Technical Reports Server (NTRS)

Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

2004-01-01

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.

Neural network tracking and extension of positive tracking periods

NASA Astrophysics Data System (ADS)

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.

Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks.

PubMed

Liu, Wei; Huang, Jie

2018-03-01

This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations.

PubMed

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-03-03

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2-3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations

PubMed Central

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-01-01

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2–3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling. PMID:28273814

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

PubMed Central

González, Fernando Cornelio Jimènez; Villegas, Osslan Osiris Vergara; Ramírez, Dulce Esperanza Torres; Sánchez, Vianey Guadalupe Cruz; Domínguez, Humberto Ochoa

2014-01-01

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia. PMID:25230306

Smart multi-level tool for remote patient monitoring based on a wireless sensor network and mobile augmented reality.

PubMed

González, Fernando Cornelio Jiménez; Villegas, Osslan Osiris Vergara; Ramírez, Dulce Esperanza Torres; Sánchez, Vianey Guadalupe Cruz; Domínguez, Humberto Ochoa

2014-09-16

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

Ab initio study on structural stability of uranium carbide

NASA Astrophysics Data System (ADS)

Sahoo, B. D.; Joshi, K. D.; Gupta, Satish C.

2013-06-01

First principles calculations have been performed using plane wave pseudopotential and full potential linearized augmented plane wave (FP-LAPW) methods to analyze structural, elastic and dynamic stability of UC under hydrostatic compression. Our calculations within pseudopotential method suggest that the rocksalt (B1) structure will transform to body centered orthorhombic (bco) structure at ˜21.5 GPa. The FP-LAPW calculations put this transition at 23 GPa. The transition pressures determined from our calculations though agree reasonably with the experimental value of 27 GPa, the high pressure bco structure suggested by theory differs slightly from the experimentally reported pseudo bco phase. The elastic stability analysis of B1 phase suggests that the B1 to bco transition is driven by the failure of C44 modulus. This finding is further substantiated by the lattice dynamic calculations which demonstrate that the B1 phase becomes dynamically unstable around the transition pressure and the instability is of long wavelength nature.

Modeling Developmental Transitions in Adaptive Resonance Theory

ERIC Educational Resources Information Center

Raijmakers, Maartje E. J.; Molenaar, Peter C. M.

2004-01-01

Neural networks are applied to a theoretical subject in developmental psychology: modeling developmental transitions. Two issues that are involved will be discussed: discontinuities and acquiring qualitatively new knowledge. We will argue that by the appearance of a bifurcation, a neural network can show discontinuities and may acquire…

Preparing for the Integration of Emerging Technologies.

ERIC Educational Resources Information Center

Dyrli, Odvard Egil; Kinnaman, Daniel E.

1994-01-01

Discussion of the process of integrating new technologies into schools considers the evolution of technology, including personal computers, CD-ROMs, hypermedia, and networking/communications; the transition from Industrial-Age to Information-Age schools; and the logical steps of transition. Sidebars discuss a networked multimedia pilot project and…

Growth dominates choice in network percolation

NASA Astrophysics Data System (ADS)

Vijayaraghavan, Vikram S.; Noël, Pierre-André; Waagen, Alex; D'Souza, Raissa M.

2013-09-01

The onset of large-scale connectivity in a network (i.e., percolation) often has a major impact on the function of the system. Traditionally, graph percolation is analyzed by adding edges to a fixed set of initially isolated nodes. Several years ago, it was shown that adding nodes as well as edges to the graph can yield an infinite order transition, which is much smoother than the traditional second-order transition. More recently, it was shown that adding edges via a competitive process to a fixed set of initially isolated nodes can lead to a delayed, extremely abrupt percolation transition with a significant jump in large but finite systems. Here we analyze a process that combines both node arrival and edge competition. If started from a small collection of seed nodes, we show that the impact of node arrival dominates: although we can significantly delay percolation, the transition is of infinite order. Thus, node arrival can mitigate the trade-off between delay and abruptness that is characteristic of explosive percolation transitions. This realization may inspire new design rules where network growth can temper the effects of delay, creating opportunities for network intervention and control.

Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing the mechanism of drop formation at the interface of horizontal oil-water stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective.

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

NASA Technical Reports Server (NTRS)

Price, Kent M.; Holdridge, Mark; Odubiyi, Jide; Jaworski, Allan; Morgan, Herbert K.

1991-01-01

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

PubMed

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

A Martian Telecommunications Network: UHF Relay Support of the Mars Exploration Rovers by the Mars Global Surveyor, Mars Odyssey, and Mars Express Orbiters

NASA Technical Reports Server (NTRS)

Edwards, Charles D., Jr.; Barbieri, A.; Brower, E.; Estabrook, P.; Gibbs, R.; Horttor, R.; Ludwinski, J.; Mase, R.; McCarthy, C.; Schmidt, R.;

Coordinated and uncoordinated optimization of networks

NASA Astrophysics Data System (ADS)

Brede, Markus

2010-06-01

In this paper, we consider spatial networks that realize a balance between an infrastructure cost (the cost of wire needed to connect the network in space) and communication efficiency, measured by average shortest path length. A global optimization procedure yields network topologies in which this balance is optimized. These are compared with network topologies generated by a competitive process in which each node strives to optimize its own cost-communication balance. Three phases are observed in globally optimal configurations for different cost-communication trade offs: (i) regular small worlds, (ii) starlike networks, and (iii) trees with a center of interconnected hubs. In the latter regime, i.e., for very expensive wire, power laws in the link length distributions P(w)∝w-α are found, which can be explained by a hierarchical organization of the networks. In contrast, in the local optimization process the presence of sharp transitions between different network regimes depends on the dimension of the underlying space. Whereas for d=∞ sharp transitions between fully connected networks, regular small worlds, and highly cliquish periphery-core networks are found, for d=1 sharp transitions are absent and the power law behavior in the link length distribution persists over a much wider range of link cost parameters. The measured power law exponents are in agreement with the hypothesis that the locally optimized networks consist of multiple overlapping suboptimal hierarchical trees.

Optimizing Pedestrian-Friendly Walking Path for the First and Last Mile Transit Journey by Using the Analytical Network Process (anp) Decision Model and GIS Network Analysis

NASA Astrophysics Data System (ADS)

Naharudin, N.; Ahamad, M. S. S.; Sadullah, A. F. M.

2017-10-01

Every transit trip begins and ends with pedestrian travel. People need to walk to access the transit services. However, their choice to walk depends on many factors including the connectivity, level of comfort and safety. These factors can influence the pleasantness of riding the transit itself, especially during the first/last mile (FLM) journey. This had triggered few studies attempting to measure the pedestrian-friendliness a walking environment can offer. There were studies that implement the pedestrian experience on walking to assess the pedestrian-friendliness of a walking environment. There were also studies that use spatial analysis to measure it based on the path connectivity and accessibility to public facilities and amenities. Though both are good, but the perception-based studies and spatial analysis can be combined to derive more holistic results. This paper proposes a framework for selecting a pedestrian-friendly path for the FLM transit journey by using the two techniques (perception-based and spatial analysis). First, the degree of importance for the factors influencing a good walking environment will be aggregated by using Analytical Network Process (ANP) decision rules based on people's preferences on those factors. The weight will then be used as attributes in the GIS network analysis. Next, the network analysis will be performed to find a pedestrian-friendly walking route based on the priorities aggregated by ANP. It will choose routes passing through the preferred attributes accordingly. The final output is a map showing pedestrian-friendly walking path for the FLM transit journey.

Toward Optimal Transport Networks

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.

2008-01-01

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

Global epidemic invasion thresholds in directed cattle subpopulation networks having source, sink, and transit nodes

USDA-ARS?s Scientific Manuscript database

Through the characterization of a metapopulation cattle disease model on a directed network having source, transit, and sink nodes, we derive two global epidemic invasion thresholds. The first threshold defines the conditions necessary for an epidemic to successfully spread at the global scale. The ...

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

PubMed Central

Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C.

2014-01-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology. PMID:24921649

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

PubMed

Chen, Guocai; Cairelli, Michael J; Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C

2014-06-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Generalized model for k -core percolation and interdependent networks

NASA Astrophysics Data System (ADS)

Panduranga, Nagendra K.; Gao, Jianxi; Yuan, Xin; Stanley, H. Eugene; Havlin, Shlomo

2017-09-01

Cascading failures in complex systems have been studied extensively using two different models: k -core percolation and interdependent networks. We combine the two models into a general model, solve it analytically, and validate our theoretical results through extensive simulations. We also study the complete phase diagram of the percolation transition as we tune the average local k -core threshold and the coupling between networks. We find that the phase diagram of the combined processes is very rich and includes novel features that do not appear in the models studying each of the processes separately. For example, the phase diagram consists of first- and second-order transition regions separated by two tricritical lines that merge and enclose a two-stage transition region. In the two-stage transition, the size of the giant component undergoes a first-order jump at a certain occupation probability followed by a continuous second-order transition at a lower occupation probability. Furthermore, at certain fixed interdependencies, the percolation transition changes from first-order → second-order → two-stage → first-order as the k -core threshold is increased. The analytic equations describing the phase boundaries of the two-stage transition region are set up, and the critical exponents for each type of transition are derived analytically.

A user-centred methodology for designing an online social network to motivate health behaviour change.

PubMed

Kamal, Noreen; Fels, Sidney

2013-01-01

Positive health behaviour is critical to preventing illness and managing chronic conditions. A user-centred methodology was employed to design an online social network to motivate health behaviour change. The methodology was augmented by utilizing the Appeal, Belonging, Commitment (ABC) Framework, which is based on theoretical models for health behaviour change and use of online social networks. The user-centred methodology included four phases: 1) initial user inquiry on health behaviour and use of online social networks; 2) interview feedback on paper prototypes; 2) laboratory study on medium fidelity prototype; and 4) a field study on the high fidelity prototype. The points of inquiry through these phases were based on the ABC Framework. This yielded an online social network system that linked to external third party databases to deploy to users via an interactive website.

Cyber situational awareness and differential hardening

NASA Astrophysics Data System (ADS)

Dwivedi, Anurag; Tebben, Dan

2012-06-01

The advent of cyber threats has created a need for a new network planning, design, architecture, operations, control, situational awareness, management, and maintenance paradigms. Primary considerations include the ability to assess cyber attack resiliency of the network, and rapidly detect, isolate, and operate during deliberate simultaneous attacks against the network nodes and links. Legacy network planning relied on automatic protection of a network in the event of a single fault or a very few simultaneous faults in mesh networks, but in the future it must be augmented to include improved network resiliency and vulnerability awareness to cyber attacks. Ability to design a resilient network requires the development of methods to define, and quantify the network resiliency to attacks, and to be able to develop new optimization strategies for maintaining operations in the midst of these newly emerging cyber threats. Ways to quantify resiliency, and its use in visualizing cyber vulnerability awareness and in identifying node or link criticality, are presented in the current work, as well as a methodology of differential network hardening based on the criticality profile of cyber network components.

Resilience of networks formed of interdependent modular networks

NASA Astrophysics Data System (ADS)

Shekhtman, Louis M.; Shai, Saray; Havlin, Shlomo

2015-12-01

Many infrastructure networks have a modular structure and are also interdependent with other infrastructures. While significant research has explored the resilience of interdependent networks, there has been no analysis of the effects of modularity. Here we develop a theoretical framework for attacks on interdependent modular networks and support our results through simulations. We focus, for simplicity, on the case where each network has the same number of communities and the dependency links are restricted to be between pairs of communities of different networks. This is particularly realistic for modeling infrastructure across cities. Each city has its own infrastructures and different infrastructures are dependent only within the city. However, each infrastructure is connected within and between cities. For example, a power grid will connect many cities as will a communication network, yet a power station and communication tower that are interdependent will likely be in the same city. It has previously been shown that single networks are very susceptible to the failure of the interconnected nodes (between communities) (Shai et al 2014 arXiv:1404.4748) and that attacks on these nodes are even more crippling than attacks based on betweenness (da Cunha et al 2015 arXiv:1502.00353). In our example of cities these nodes have long range links which are more likely to fail. For both treelike and looplike interdependent modular networks we find distinct regimes depending on the number of modules, m. (i) In the case where there are fewer modules with strong intraconnections, the system first separates into modules in an abrupt first-order transition and then each module undergoes a second percolation transition. (ii) When there are more modules with many interconnections between them, the system undergoes a single transition. Overall, we find that modular structure can significantly influence the type of transitions observed in interdependent networks and should be considered in attempts to make interdependent networks more resilient.

A steep-slope transistor based on abrupt electronic phase transition

NASA Astrophysics Data System (ADS)

Shukla, Nikhil; Thathachary, Arun V.; Agrawal, Ashish; Paik, Hanjong; Aziz, Ahmedullah; Schlom, Darrell G.; Gupta, Sumeet Kumar; Engel-Herbert, Roman; Datta, Suman

2015-08-01

Collective interactions in functional materials can enable novel macroscopic properties like insulator-to-metal transitions. While implementing such materials into field-effect-transistor technology can potentially augment current state-of-the-art devices by providing unique routes to overcome their conventional limits, attempts to harness the insulator-to-metal transition for high-performance transistors have experienced little success. Here, we demonstrate a pathway for harnessing the abrupt resistivity transformation across the insulator-to-metal transition in vanadium dioxide (VO2), to design a hybrid-phase-transition field-effect transistor that exhibits gate controlled steep (`sub-kT/q') and reversible switching at room temperature. The transistor design, wherein VO2 is implemented in series with the field-effect transistor's source rather than into the channel, exploits negative differential resistance induced across the VO2 to create an internal amplifier that facilitates enhanced performance over a conventional field-effect transistor. Our approach enables low-voltage complementary n-type and p-type transistor operation as demonstrated here, and is applicable to other insulator-to-metal transition materials, offering tantalizing possibilities for energy-efficient logic and memory applications.

A steep-slope transistor based on abrupt electronic phase transition.

PubMed

Shukla, Nikhil; Thathachary, Arun V; Agrawal, Ashish; Paik, Hanjong; Aziz, Ahmedullah; Schlom, Darrell G; Gupta, Sumeet Kumar; Engel-Herbert, Roman; Datta, Suman

2015-08-07

Collective interactions in functional materials can enable novel macroscopic properties like insulator-to-metal transitions. While implementing such materials into field-effect-transistor technology can potentially augment current state-of-the-art devices by providing unique routes to overcome their conventional limits, attempts to harness the insulator-to-metal transition for high-performance transistors have experienced little success. Here, we demonstrate a pathway for harnessing the abrupt resistivity transformation across the insulator-to-metal transition in vanadium dioxide (VO2), to design a hybrid-phase-transition field-effect transistor that exhibits gate controlled steep ('sub-kT/q') and reversible switching at room temperature. The transistor design, wherein VO2 is implemented in series with the field-effect transistor's source rather than into the channel, exploits negative differential resistance induced across the VO2 to create an internal amplifier that facilitates enhanced performance over a conventional field-effect transistor. Our approach enables low-voltage complementary n-type and p-type transistor operation as demonstrated here, and is applicable to other insulator-to-metal transition materials, offering tantalizing possibilities for energy-efficient logic and memory applications.

Investigating the relationship between subjective drug craving and temporal dynamics of the default mode network, executive control network, and salience network in methamphetamine dependents using rsfMRI

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Somayyeh; Hossein-Zadeh, Gholam-Ali; Shahbabaie, Alireza; Ekhtiari, Hamed

2016-03-01

Resting state functional connectivity (rsFC) studies using fMRI provides a great deal of knowledge on the spatiotemporal organization of the brain. The relationships between and within a number of resting state functional networks, namely the default mode network (DMN), salience network (SN) and executive control network (ECN) have been intensely studied in basic and clinical cognitive neuroscience [1]. However, the presumption of spatial and temporal stationarity has mostly restricted the assessment of rsFC [1]. In this study, sliding window correlation analysis and k-means clustering were exploited to examine the temporal dynamics of rsFC of these three networks in 24 abstinent methamphetamine dependents. Afterwards, using canonical correlation analysis (CCA) the possible relationship between the level of self-reported craving and the temporal dynamics was examined. Results indicate that the rsFC transits between 6 discrete "FC states" in the meth dependents. CCA results show that higher levels of craving are associated with higher probability of transiting from state 4 to 6 (positive FC of DMN-ECN getting weak and negative FC of DMN-SN appearing) and staying in state 4 (positive FC of DMN-ECN), lower probability of staying in state 2 (negative FC of DMN-ECN), transiting from state 4 to 2 (change of positive FC of DMN-ECN to negative FC), and transiting from state 3 to 5 (appearance of negative FC of DMN-SN and positive FC of DMN-ECN with the presence of negative FC of SN-ECN). Quantitative measures of temporal dynamics in large-scale brain networks could bring new added values to increase potentials for applications of rsfMRI in addiction medicine.

Chaotic gas turbine subject to augmented Lorenz equations.

PubMed

Cho, Kenichiro; Miyano, Takaya; Toriyama, Toshiyuki

2012-09-01

Inspired by the chaotic waterwheel invented by Malkus and Howard about 40 years ago, we have developed a gas turbine that randomly switches the sense of rotation between clockwise and counterclockwise. The nondimensionalized expressions for the equations of motion of our turbine are represented as a starlike network of many Lorenz subsystems sharing the angular velocity of the turbine rotor as the central node, referred to as augmented Lorenz equations. We show qualitative similarities between the statistical properties of the angular velocity of the turbine rotor and the velocity field of large-scale wind in turbulent Rayleigh-Bénard convection reported by Sreenivasan et al. [Phys. Rev. E 65, 056306 (2002)]. Our equations of motion achieve the random reversal of the turbine rotor through the stochastic resonance of the angular velocity in a double-well potential and the force applied by rapidly oscillating fields. These results suggest that the augmented Lorenz model is applicable as a dynamical model for the random reversal of turbulent large-scale wind through cessation.

Setting Access Permission through Transitive Relationship in Web-based Social Networks

NASA Astrophysics Data System (ADS)

Hong, Dan; Shen, Vincent Y.

The rising popularity of various social networking websites has created a huge problem on Internet privacy. Although it is easy to post photos, comments, opinions on some events, etc. on the Web, some of these data (such as a person’s location at a particular time, criticisms of a politician, etc.) are private and should not be accessed by unauthorized users. Although social networks facilitate sharing, the fear of sending sensitive data to a third party without knowledge or permission of the data owners discourages people from taking full advantage of some social networking applications. We exploit the existing relationships on social networks and build a ‘‘trust network’’ with transitive relationship to allow controlled data sharing so that the privacy and preferences of data owners are respected. The trust network linking private data owners, private data requesters, and intermediary users is a directed weighted graph. The permission value for each private data requester can be automatically assigned in this network based on the transitive relationship. Experiments were conducted to confirm the feasibility of constructing the trust network from existing social networks, and to assess the validity of permission value assignments in the query process. Since the data owners only need to define the access rights of their closest contacts once, this privacy scheme can make private data sharing easily manageable by social network participants.

Collective dynamics in heterogeneous networks of neuronal cellular automata

NASA Astrophysics Data System (ADS)

Manchanda, Kaustubh; Bose, Amitabha; Ramaswamy, Ramakrishna

2017-12-01

We examine the collective dynamics of heterogeneous random networks of model neuronal cellular automata. Each automaton has b active states, a single silent state and r - b - 1 refractory states, and can show 'spiking' or 'bursting' behavior, depending on the values of b. We show that phase transitions that occur in the dynamical activity can be related to phase transitions in the structure of Erdõs-Rényi graphs as a function of edge probability. Different forms of heterogeneity allow distinct structural phase transitions to become relevant. We also show that the dynamics on the network can be described by a semi-annealed process and, as a result, can be related to the Boolean Lyapunov exponent.

Communication: Analysing kinetic transition networks for rare events.

PubMed

Stevenson, Jacob D; Wales, David J

2014-07-28

The graph transformation approach is a recently proposed method for computing mean first passage times, rates, and committor probabilities for kinetic transition networks. Here we compare the performance to existing linear algebra methods, focusing on large, sparse networks. We show that graph transformation provides a much more robust framework, succeeding when numerical precision issues cause the other methods to fail completely. These are precisely the situations that correspond to rare event dynamics for which the graph transformation was introduced.

Multistable binary decision making on networks

NASA Astrophysics Data System (ADS)

Lucas, Andrew; Lee, Ching Hua

2013-03-01

We propose a simple model for a binary decision making process on a graph, motivated by modeling social decision making with cooperative individuals. The model is similar to a random field Ising model or fiber bundle model, but with key differences in behavior on heterogeneous networks. For many types of disorder and interactions between the nodes, we predict with mean field theory discontinuous phase transitions that are largely independent of network structure. We show how these phase transitions can also be understood by studying microscopic avalanches and describe how network structure enhances fluctuations in the distribution of avalanches. We suggest theoretically the existence of a “glassy” spectrum of equilibria associated with a typical phase, even on infinite graphs, so long as the first moment of the degree distribution is finite. This behavior implies that the model is robust against noise below a certain scale and also that phase transitions can switch from discontinuous to continuous on networks with too few edges. Numerical simulations suggest that our theory is accurate.

Mobile device geo-localization and object visualization in sensor networks

NASA Astrophysics Data System (ADS)

Lemaire, Simon; Bodensteiner, Christoph; Arens, Michael

2014-10-01

In this paper we present a method to visualize geo-referenced objects on modern smartphones using a multi- functional application design. The application applies different localization and visualization methods including the smartphone camera image. The presented application copes well with different scenarios. A generic application work flow and augmented reality visualization techniques are described. The feasibility of the approach is experimentally validated using an online desktop selection application in a network with a modern of-the-shelf smartphone. Applications are widespread and include for instance crisis and disaster management or military applications.

Increasing the Literacy Skills of Students Who Require AAC through Modified Direct Instruction and Specific Instructional Feedback

ERIC Educational Resources Information Center

Westover, Jennifer M.

2010-01-01

Literacy skills are fundamental for all learners. For students who require augmentative and alternative communication (AAC), strong literacy skills provide a gateway to generative communication, genuine social networking, improved access to academic opportunities, access to information technology and future employment opportunities. However, many…

Solute transport with multisegment, equilibrium-controlled, classical reactions: Problem solvability and feed forward method's applicability for complex segments of at most binary participants

USGS Publications Warehouse

Rubin, Jacob

1992-01-01

The feed forward (FF) method derives efficient operational equations for simulating transport of reacting solutes. It has been shown to be applicable in the presence of networks with any number of homogeneous and/or heterogeneous, classical reaction segments that consist of three, at most binary participants. Using a sequential (network type after network type) exploration approach and, independently, theoretical explanations, it is demonstrated for networks with classical reaction segments containing more than three, at most binary participants that if any one of such networks leads to a solvable transport problem then the FF method is applicable. Ways of helping to avoid networks that produce problem insolvability are developed and demonstrated. A previously suggested algebraic, matrix rank procedure has been adapted and augmented to serve as the main, easy-to-apply solvability test for already postulated networks. Four network conditions that often generate insolvability have been identified and studied. Their early detection during network formulation may help to avoid postulation of insolvable networks.

Surface hopping, transition state theory and decoherence. I. Scattering theory and time-reversibility

NASA Astrophysics Data System (ADS)

Jain, Amber; Herman, Michael F.; Ouyang, Wenjun; Subotnik, Joseph E.

2015-10-01

We provide an in-depth investigation of transmission coefficients as computed using the augmented-fewest switches surface hopping algorithm in the low energy regime. Empirically, microscopic reversibility is shown to hold approximately. Furthermore, we show that, in some circumstances, including decoherence on top of surface hopping calculations can help recover (as opposed to destroy) oscillations in the transmission coefficient as a function of energy; these oscillations can be studied analytically with semiclassical scattering theory. Finally, in the spirit of transition state theory, we also show that transmission coefficients can be calculated rather accurately starting from the curve crossing point and running trajectories forwards and backwards.

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

PubMed

Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.

PubMed

Wang, Shui-Hua; Lv, Yi-Ding; Sui, Yuxiu; Liu, Shuai; Wang, Su-Jing; Zhang, Yu-Dong

2017-11-17

Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.

Deep neural network features for horses identity recognition using multiview horses' face pattern

NASA Astrophysics Data System (ADS)

Jarraya, Islem; Ouarda, Wael; Alimi, Adel M.

2017-03-01

To control the state of horses in the born, breeders needs a monitoring system with a surveillance camera that can identify and distinguish between horses. We proposed in [5] a method of horse's identification at a distance using the frontal facial biometric modality. Due to the change of views, the face recognition becomes more difficult. In this paper, the number of images used in our THoDBRL'2015 database (Tunisian Horses DataBase of Regim Lab) is augmented by adding other images of other views. Thus, we used front, right and left profile face's view. Moreover, we suggested an approach for multiview face recognition. First, we proposed to use the Gabor filter for face characterization. Next, due to the augmentation of the number of images, and the large number of Gabor features, we proposed to test the Deep Neural Network with the auto-encoder to obtain the more pertinent features and to reduce the size of features vector. Finally, we performed the proposed approach on our THoDBRL'2015 database and we used the linear SVM for classification.

Comparative study of flare control laws. [optimal control of b-737 aircraft approach and landing

NASA Technical Reports Server (NTRS)

Nadkarni, A. A.; Breedlove, W. J., Jr.

1979-01-01

A digital 3-D automatic control law was developed to achieve an optimal transition of a B-737 aircraft between various initial glid slope conditions and the desired final touchdown condition. A discrete, time-invariant, optimal, closed-loop control law presented for a linear regulator problem, was extended to include a system being acted upon by a constant disturbance. Two forms of control laws were derived to solve this problem. One method utilized the feedback of integral states defined appropriately and augmented with the original system equations. The second method formulated the problem as a control variable constraint, and the control variables were augmented with the original system. The control variable constraint control law yielded a better performance compared to feedback control law for the integral states chosen.

Examining the Impact of Pre-Induction Social Networking on the Student Transition into Higher Education

ERIC Educational Resources Information Center

Ribchester, Chris; Ross, Kim; Rees, Emma L. E.

2014-01-01

This research paper considers how bespoke online social networks have been used to support students' transition into higher education during the weeks immediately prior to formal "on-site" induction. An analysis of online activities showed some differences in the pattern of engagement between two contrasting departments (Geography and…

Mutually cooperative epidemics on power-law networks

NASA Astrophysics Data System (ADS)

Cui, Peng-Bi; Colaiori, Francesca; Castellano, Claudio

2017-08-01

The spread of an infectious disease can, in some cases, promote the propagation of other pathogens favoring violent outbreaks, which cause a discontinuous transition to an endemic state. The topology of the contact network plays a crucial role in these cooperative dynamics. We consider a susceptible-infected-removed-type model with two mutually cooperative pathogens: An individual already infected with one disease has an increased probability of getting infected by the other. We present a heterogeneous mean-field theoretical approach to the coinfection dynamics on generic uncorrelated power-law degree-distributed networks and validate its results by means of numerical simulations. We show that, when the second moment of the degree distribution is finite, the epidemic transition is continuous for low cooperativity, while it is discontinuous when cooperativity is sufficiently high. For scale-free networks, i.e., topologies with diverging second moment, the transition is instead always continuous. In this way we clarify the effect of heterogeneity and system size on the nature of the transition, and we validate the physical interpretation about the origin of the discontinuity.

Phase transitions in Pareto optimal complex networks

NASA Astrophysics Data System (ADS)

Seoane, Luís F.; Solé, Ricard

2015-09-01

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

Appplication of statistical mechanical methods to the modeling of social networks

NASA Astrophysics Data System (ADS)

Strathman, Anthony Robert

With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Glass Transition Temperature Measurement for Undercured Cyanate Ester Networks: Challenges, Tips, and Tricks (Briefing Charts)

DTIC Science & Technology

2014-01-29

DISTRIBUTION A: Approved for public release; distribution is unlimited. Thermosetting Polymers Have a TG Envelope – Not Just a TG 4 • The glass transition...glass transition temperature of a thermosetting polymer can vary over a wide range of temperatures depending on how the polymer is processed • A... thermosetting polymer with only one kind of network formation and negligible side reactions, the conversion may be determined at every point in the scan. • By

Blood vessels segmentation of hatching eggs based on fully convolutional networks

NASA Astrophysics Data System (ADS)

Geng, Lei; Qiu, Ling; Wu, Jun; Xiao, Zhitao

2018-04-01

FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

Fabric defect detection based on faster R-CNN

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Liu, Xianghui; Li, Chunlei; Li, Bicao; Wang, Baorui

2018-04-01

In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.

Community detection in networks with unequal groups.

PubMed

Zhang, Pan; Moore, Cristopher; Newman, M E J

2016-01-01

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

Burst synchronization transitions in a neuronal network of subnetworks

NASA Astrophysics Data System (ADS)

Sun, Xiaojuan; Lei, Jinzhi; Perc, Matjaž; Kurths, Jürgen; Chen, Guanrong

2011-03-01

In this paper, the transitions of burst synchronization are explored in a neuronal network consisting of subnetworks. The studied network is composed of electrically coupled bursting Hindmarsh-Rose neurons. Numerical results show that two types of burst synchronization transitions can be induced not only by the variations of intra- and intercoupling strengths but also by changing the probability of random links between different subnetworks and the number of subnetworks. Furthermore, we find that the underlying mechanisms for these two bursting synchronization transitions are different: one is due to the change of spike numbers per burst, while the other is caused by the change of the bursting type. Considering that changes in the coupling strengths and neuronal connections are closely interlaced with brain plasticity, the presented results could have important implications for the role of the brain plasticity in some functional behavior that are associated with synchronization.

Social network analysis of child and adult interorganizational connections.

PubMed

Davis, Maryann; Koroloff, Nancy; Johnsen, Matthew

2012-01-01

Because most programs serve either children and their families or adults, a critical component of service and treatment continuity in mental health and related services for individuals transitioning into adulthood (ages 14-25) is coordination across programs on either side of the adult age divide. This study was conducted in Clark County, Washington, a community that had received a Partnership for Youth Transition grant from the Federal Center for Mental Health Services. Social Network Analysis methodology was used to describe the strength and direction of each organization's relationship to other organizations in the transition network. Interviews were conducted before grant implementation (n=103) and again four years later (n=99). The findings of the study revealed significant changes in the nature of relationships between organizations over time. While the overall density of the transition service network remained stable, specific ways of connecting did change. Some activities became more decentralized while others became more inclusive as evidenced by the increase in size of the largest K-core. This was particularly true for the activity of "receiving referrals." These changes reflected more direct contact between child and adult serving organizations. The two separate child and adult systems identified at baseline appeared more integrated by the end of the grant period. Having greater connectivity among all organizations regardless of ages served should benefit youth and young adults of transition age. This study provides further evidence that Social Network Analysis is a useful method for measuring change in service system integration over time.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

Comparative analysis of two discretizations of Ricci curvature for complex networks.

PubMed

Samal, Areejit; Sreejith, R P; Gu, Jiao; Liu, Shiping; Saucan, Emil; Jost, Jürgen

2018-06-05

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.

A Model of Mental State Transition Network

NASA Astrophysics Data System (ADS)

Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

NASA Astrophysics Data System (ADS)

Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

2018-06-01

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

Coupled protein-ligand dynamics in truncated hemoglobin N from atomistic simulations and transition networks.

PubMed

Cazade, Pierre-André; Berezovska, Ganna; Meuwly, Markus

2015-05-01

The nature of ligand motion in proteins is difficult to characterize directly using experiment. Specifically, it is unclear to what degree these motions are coupled. All-atom simulations are used to sample ligand motion in truncated Hemoglobin N. A transition network analysis including ligand- and protein-degrees of freedom is used to analyze the microscopic dynamics. Clustering of two different subsets of MD trajectories highlights the importance of a diverse and exhaustive description to define the macrostates for a ligand-migration network. Monte Carlo simulations on the transition matrices from one particular clustering are able to faithfully capture the atomistic simulations. Contrary to clustering by ligand positions only, including a protein degree of freedom yields considerably improved coarse grained dynamics. Analysis with and without imposing detailed balance agree closely which suggests that the underlying atomistic simulations are converged with respect to sampling transitions between neighboring sites. Protein and ligand dynamics are not independent from each other and ligand migration through globular proteins is not passive diffusion. Transition network analysis is a powerful tool to analyze and characterize the microscopic dynamics in complex systems. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.

Group percolation in interdependent networks

NASA Astrophysics Data System (ADS)

Wang, Zexun; Zhou, Dong; Hu, Yanqing

2018-03-01

In many real network systems, nodes usually cooperate with each other and form groups to enhance their robustness to risks. This motivates us to study an alternative type of percolation, group percolation, in interdependent networks under attack. In this model, nodes belonging to the same group survive or fail together. We develop a theoretical framework for this group percolation and find that the formation of groups can improve the resilience of interdependent networks significantly. However, the percolation transition is always of first order, regardless of the distribution of group sizes. As an application, we map the interdependent networks with intersimilarity structures, which have attracted much attention recently, onto the group percolation and confirm the nonexistence of continuous phase transitions.

Indoor Subspacing to Implement Indoorgml for Indoor Navigation

NASA Astrophysics Data System (ADS)

Jung, H.; Lee, J.

2015-10-01

According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

From quiescence to proliferation: Cdk oscillations drive the mammalian cell cycle

PubMed Central

Gérard, Claude; Goldbeter, Albert

2012-01-01

We recently proposed a detailed model describing the dynamics of the network of cyclin-dependent kinases (Cdks) driving the mammalian cell cycle (Gérard and Goldbeter, 2009). The model contains four modules, each centered around one cyclin/Cdk complex. Cyclin D/Cdk4–6 and cyclin E/Cdk2 promote progression in G1 and elicit the G1/S transition, respectively; cyclin A/Cdk2 ensures progression in S and the transition S/G2, while the activity of cyclin B/Cdk1 brings about the G2/M transition. This model shows that in the presence of sufficient amounts of growth factor the Cdk network is capable of temporal self-organization in the form of sustained oscillations, which correspond to the ordered, sequential activation of the various cyclin/Cdk complexes that control the successive phases of the cell cycle. The results suggest that the switch from cellular quiescence to cell proliferation corresponds to the transition from a stable steady state to sustained oscillations in the Cdk network. The transition depends on a finely tuned balance between factors that promote or hinder progression in the cell cycle. We show that the transition from quiescence to proliferation can occur in multiple ways that alter this balance. By resorting to bifurcation diagrams, we analyze the mechanism of oscillations in the Cdk network. Finally, we show that the complexity of the detailed model can be greatly reduced, without losing its key dynamical properties, by considering a skeleton model for the Cdk network. Using such a skeleton model for the mammalian cell cycle we show that positive feedback (PF) loops enhance the amplitude and the robustness of Cdk oscillations with respect to molecular noise. We compare the relative merits of the detailed and skeleton versions of the model for the Cdk network driving the mammalian cell cycle. PMID:23130001

Learning phase transitions by confusion

NASA Astrophysics Data System (ADS)

van Nieuwenburg, Evert P. L.; Liu, Ye-Hua; Huber, Sebastian D.

2017-02-01

Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever-larger data sets, classification problems are now routinely solved using machine-learning techniques. Here, we propose a neural-network approach to finding phase transitions, based on the performance of a neural network after it is trained with data that are deliberately labelled incorrectly. We demonstrate the success of this method on the topological phase transition in the Kitaev chain, the thermal phase transition in the classical Ising model, and the many-body-localization transition in a disordered quantum spin chain. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand. It therefore paves the way to the development of a generic tool for identifying unexplored phase transitions.

Learning phase transitions by confusion

NASA Astrophysics Data System (ADS)

van Nieuwenburg, Evert; Liu, Ye-Hua; Huber, Sebastian

Classifying phases of matter is a central problem in physics. For quantum mechanical systems, this task can be daunting owing to the exponentially large Hilbert space. Thanks to the available computing power and access to ever larger data sets, classification problems are now routinely solved using machine learning techniques. Here, we propose to use a neural network based approach to find transitions depending on the performance of the neural network after training it with deliberately incorrectly labelled data. We demonstrate the success of this method on the topological phase transition in the Kitaev chain, the thermal phase transition in the classical Ising model, and the many-body-localization transition in a disordered quantum spin chain. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand. It therefore paves the way to a generic tool to identify unexplored transitions.

STAR 3 randomized controlled trial to compare sensor-augmented insulin pump therapy with multiple daily injections in the treatment of type 1 diabetes: research design, methods, and baseline characteristics of enrolled subjects.

PubMed

Davis, Stephen N; Horton, Edward S; Battelino, Tadej; Rubin, Richard R; Schulman, Kevin A; Tamborlane, William V

2010-04-01

Sensor-augmented pump therapy (SAPT) integrates real-time continuous glucose monitoring (RT-CGM) with continuous subcutaneous insulin infusion (CSII) and offers an alternative to multiple daily injections (MDI). Previous studies provide evidence that SAPT may improve clinical outcomes among people with type 1 diabetes. Sensor-Augmented Pump Therapy for A1c Reduction (STAR) 3 is a multicenter randomized controlled trial comparing the efficacy of SAPT to that of MDI in subjects with type 1 diabetes. Subjects were randomized to either continue with MDI or transition to SAPT for 1 year. Subjects in the MDI cohort were allowed to transition to SAPT for 6 months after completion of the study. SAPT subjects who completed the study were also allowed to continue for 6 months. The primary end point was the difference between treatment groups in change in hemoglobin A1c (HbA1c) percentage from baseline to 1 year of treatment. Secondary end points included percentage of subjects with HbA1c < or =7% and without severe hypoglycemia, as well as area under the curve of time spent in normal glycemic ranges. Tertiary end points include percentage of subjects with HbA1c < or =7%, key safety end points, user satisfaction, and responses on standardized assessments. A total of 495 subjects were enrolled, and the baseline characteristics similar between the SAPT and MDI groups. Study completion is anticipated in June 2010. Results of this randomized controlled trial should help establish whether an integrated RT-CGM and CSII system benefits patients with type 1 diabetes more than MDI.

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy.

PubMed

Neal, Sharon L

2018-01-01

The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process, the results are consistent with the evolution of mixed phase bicelles (nanodisks) and small amounts of worm-like DMPC/DHPC aggregates, and perhaps DHPC micelles, at low temperature to suspensions of branched and entangled worm-like aggregates above the DMPC gel phase transition and perforated multi-lamellar aggregates at high temperature.

Phase transition of Boolean networks with partially nested canalizing functions

NASA Astrophysics Data System (ADS)

Jansen, Kayse; Matache, Mihaela Teodora

2013-07-01

We generate the critical condition for the phase transition of a Boolean network governed by partially nested canalizing functions for which a fraction of the inputs are canalizing, while the remaining non-canalizing inputs obey a complementary threshold Boolean function. Past studies have considered the stability of fully or partially nested canalizing functions paired with random choices of the complementary function. In some of those studies conflicting results were found with regard to the presence of chaotic behavior. Moreover, those studies focus mostly on ergodic networks in which initial states are assumed equally likely. We relax that assumption and find the critical condition for the sensitivity of the network under a non-ergodic scenario. We use the proposed mathematical model to determine parameter values for which phase transitions from order to chaos occur. We generate Derrida plots to show that the mathematical model matches the actual network dynamics. The phase transition diagrams indicate that both order and chaos can occur, and that certain parameters induce a larger range of values leading to order versus chaos. The edge-of-chaos curves are identified analytically and numerically. It is shown that the depth of canalization does not cause major dynamical changes once certain thresholds are reached; these thresholds are fairly small in comparison to the connectivity of the nodes.

Is the kinetoplast DNA a percolating network of linked rings at its critical point?

NASA Astrophysics Data System (ADS)

Michieletto, Davide; Marenduzzo, Davide; Orlandini, Enzo

2015-05-01

In this work we present a computational study of the kinetoplast genome, modelled as a large number of semiflexible unknotted loops, which are allowed to link with each other. As the DNA density increases, the systems shows a percolation transition between a gas of unlinked rings and a network of linked loops which spans the whole system. Close to the percolation transition, we find that the mean valency of the network, i.e. the average number of loops which are linked to any one loop, is around three, as found experimentally for the kinetoplast DNA (kDNA). Even more importantly, by simulating the digestion of the network by a restriction enzyme, we show that the distribution of oligomers, i.e. structures formed by a few loops which remain linked after digestion, quantitatively matches experimental data obtained from gel electrophoresis, provided that the density is, once again, close to the percolation transition. With respect to previous work, our analysis builds on a reduced number of assumptions, yet can still fully explain the experimental data. Our findings suggest that the kDNA can be viewed as a network of linked loops positioned very close to the percolation transition, and we discuss the possible biological implications of this remarkable fact.

Network Disruption in the Preclinical Stages of Alzheimer's Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment.

PubMed

López-Sanz, David; Garcés, Pilar; Álvarez, Blanca; Delgado-Losada, María Luisa; López-Higes, Ramón; Maestú, Fernando

2017-12-01

Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer's Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Social network based dynamic transit service through the OMITS system.

DOT National Transportation Integrated Search

2014-02-01

The Open Mode Integrated Transportation System (OMITS) forms a sustainable information infrastructure for communication within and between the mobile/Internet network, the roadway : network, and the users social network. It manipulates the speed g...

NSI directed to continue SPAN's functions

NASA Technical Reports Server (NTRS)

Rounds, Fred

1991-01-01

During a series of network management retreats in June and July 1990, representatives from NASA Headquarters Codes O and S agreed on networking roles and responsibilities for their respective organizations. The representatives decided that NASA Science Internet (NSI) will assume management of both the Space Physics Analysis Network (SPAN) and the NASA Science Network (NSN). SPAN is now known as the NSI/DECnet, and NSN is now known as the NSI/IP. Some management functions will be distributed between Ames Research Center (ARC) and Goddard Space Flight Center (GSFC). NSI at ARC has the lead role for requirements generation and networking engineering. Advanced Applications and the Network Information Center is being developed at GSFC. GSFC will lead the NSI User Services, but NSI at Ames will continue to provide the User Services during the transition. The transition will be made as transparent as possible for the users. DECnet service will continue, but is now directly managed by NSI at Ames. NSI will continue to work closely with routing center managers at other NASA centers, and has formed a transition team to address the change in management. An NSI/DECnet working group had also been formed as a separate engineering group within NSI to plan the transition to Phase 5, DECnet's approach to Open System Integration (OSI). Transition is not expected for a year or more due to delays in produce releases. Plans to upgrade speeds in tail circuits and the backbone are underway. The proposed baseline service for new connections is up to 56 Kbps; 9.6 Kbps lines will gradually be upgraded as requirements dictate. NSI is in the process of consolidating protocol traffic, tail circuits, and the backbone. Currently NSI's backbone is fractional T1; NSI will go to full T1 service as soon as it is feasible.

Some properties of asymmetric Hopfield neural networks with finite time of transition between states

NASA Astrophysics Data System (ADS)

Suleimenov, Ibragim; Mun, Grigoriy; Panchenko, Sergey; Pak, Ivan

2016-11-01

There were implemented samples of asymmetric Hopfield neural networks which have finite time of transition from one state to another. It was shown that in such systems, various oscillation modes could occur. It was revealed that the oscillation of the output signal of certain neuron could be treated as extra logical variable, which describes the state of the neuron. Asymmetric Hopfield neural networks are described in terms of ternary logic. Such logic may be employed in image recognition procedure.

Hybrid phase transition into an absorbing state: Percolation and avalanches

NASA Astrophysics Data System (ADS)

Lee, Deokjae; Choi, S.; Stippinger, M.; Kertész, J.; Kahng, B.

2016-04-01

Interdependent networks are more fragile under random attacks than simplex networks, because interlayer dependencies lead to cascading failures and finally to a sudden collapse. This is a hybrid phase transition (HPT), meaning that at the transition point the order parameter has a jump but there are also critical phenomena related to it. Here we study these phenomena on the Erdős-Rényi and the two-dimensional interdependent networks and show that the hybrid percolation transition exhibits two kinds of critical behaviors: divergence of the fluctuations of the order parameter and power-law size distribution of finite avalanches at a transition point. At the transition point global or "infinite" avalanches occur, while the finite ones have a power law size distribution; thus the avalanche statistics also has the nature of a HPT. The exponent βm of the order parameter is 1 /2 under general conditions, while the value of the exponent γm characterizing the fluctuations of the order parameter depends on the system. The critical behavior of the finite avalanches can be described by another set of exponents, βa and γa. These two critical behaviors are coupled by a scaling law: 1 -βm=γa .

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

PubMed

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Technology acceptance perception for promotion of sustainable consumption.

PubMed

Biswas, Aindrila; Roy, Mousumi

2018-03-01

Economic growth in the past decades has resulted in change in consumption pattern and emergence of tech-savvy generation with unprecedented increase in the usage of social network technology. In this paper, the technology acceptance value gap adapted from the technology acceptance model has been applied as a tool supporting social network technology usage and subsequent promotion of sustainable consumption. The data generated through the use of structured questionnaires have been analyzed using structural equation modeling. The validity of the model and path estimates signifies the robustness of Technology Acceptance value gap in adjudicating the efficiency of social network technology usage in augmentation of sustainable consumption and awareness. The results indicate that subjective norm gap, ease-of-operation gap, and quality of green information gap have the most adversarial impact on social network technology usage. Eventually social networking technology usage has been identified as a significant antecedent of sustainable consumption.

Integrated Network Architecture for Sustained Human and Robotic Exploration

NASA Technical Reports Server (NTRS)

Noreen, Gary; Cesarone, Robert; Deutsch, Leslie; Edwards, Charles; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazolla, Sabino;

Providing end-to-end QoS for multimedia applications in 3G wireless networks

NASA Astrophysics Data System (ADS)

Guo, Katherine; Rangarajan, Samapth; Siddiqui, M. A.; Paul, Sanjoy

2003-11-01

As the usage of wireless packet data services increases, wireless carriers today are faced with the challenge of offering multimedia applications with QoS requirements within current 3G data networks. End-to-end QoS requires support at the application, network, link and medium access control (MAC) layers. We discuss existing CDMA2000 network architecture and show its shortcomings that prevent supporting multiple classes of traffic at the Radio Access Network (RAN). We then propose changes in RAN within the standards framework that enable support for multiple traffic classes. In addition, we discuss how Session Initiation Protocol (SIP) can be augmented with QoS signaling for supporting end-to-end QoS. We also review state of the art scheduling algorithms at the base station and provide possible extensions to these algorithms to support different classes of traffic as well as different classes of users.

Complexity analysis on public transport networks of 97 large- and medium-sized cities in China

NASA Astrophysics Data System (ADS)

Tian, Zhanwei; Zhang, Zhuo; Wang, Hongfei; Ma, Li

2018-04-01

The traffic situation in Chinese urban areas is continuing to deteriorate. To make a better planning and designing of the public transport system, it is necessary to make profound research on the structure of urban public transport networks (PTNs). We investigate 97 large- and medium-sized cities’ PTNs in China, construct three types of network models — bus stop network, bus transit network and bus line network, then analyze the structural characteristics of them. It is revealed that bus stop network is small-world and scale-free, bus transit network and bus line network are both small-world. Betweenness centrality of each city’s PTN shows similar distribution pattern, although these networks’ size is various. When classifying cities according to the characteristics of PTNs or economic development level, the results are similar. It means that the development of cities’ economy and transport network has a strong correlation, PTN expands in a certain model with the development of economy.

Hybrid Percolation Transition in Cluster Merging Processes: Continuously Varying Exponents

NASA Astrophysics Data System (ADS)

Cho, Y. S.; Lee, J. S.; Herrmann, H. J.; Kahng, B.

2016-01-01

Consider growing a network, in which every new connection is made between two disconnected nodes. At least one node is chosen randomly from a subset consisting of g fraction of the entire population in the smallest clusters. Here we show that this simple strategy for improving connection exhibits a more unusual phase transition, namely a hybrid percolation transition exhibiting the properties of both first-order and second-order phase transitions. The cluster size distribution of finite clusters at a transition point exhibits power-law behavior with a continuously varying exponent τ in the range 2 <τ (g )≤2.5 . This pattern reveals a necessary condition for a hybrid transition in cluster aggregation processes, which is comparable to the power-law behavior of the avalanche size distribution arising in models with link-deleting processes in interdependent networks.

Quantum entanglement percolation

NASA Astrophysics Data System (ADS)

Siomau, Michael

2016-09-01

Quantum communication demands efficient distribution of quantum entanglement across a network of connected partners. The search for efficient strategies for the entanglement distribution may be based on percolation theory, which describes evolution of network connectivity with respect to some network parameters. In this framework, the probability to establish perfect entanglement between two remote partners decays exponentially with the distance between them before the percolation transition point, which unambiguously defines percolation properties of any classical network or lattice. Here we introduce quantum networks created with local operations and classical communication, which exhibit non-classical percolation transition points leading to striking communication advantages over those offered by the corresponding classical networks. We show, in particular, how to establish perfect entanglement between any two nodes in the simplest possible network—the 1D chain—using imperfectly entangled pairs of qubits.

Stagnation Region Heat Transfer Augmentation at Very High Turbulence Levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ames, Forrest; Kingery, Joseph E.

A database for stagnation region heat transfer has been extended to include heat transfer measurements acquired downstream from a new high intensity turbulence generator. This work was motivated by gas turbine industry heat transfer designers who deal with heat transfer environments with increasing Reynolds numbers and very high turbulence levels. The new mock aero-combustor turbulence generator produces turbulence levels which average 17.4%, which is 37% higher than the older turbulence generator. The increased level of turbulence is caused by the reduced contraction ratio from the liner to the exit. Heat transfer measurements were acquired on two large cylindrical leading edgemore » test surfaces having a four to one range in leading edge diameter (40.64 cm and 10.16 cm). Gandvarapu and Ames [1] previously acquired heat transfer measurements for six turbulence conditions including three grid conditions, two lower turbulence aero-combustor conditions, and a low turbulence condition. The data are documented and tabulated for an eight to one range in Reynolds numbers for each test surface with Reynolds numbers ranging from 62,500 to 500,000 for the large leading edge and 15,625 to 125,000 for the smaller leading edge. The data show augmentation levels of up to 136% in the stagnation region for the large leading edge. This heat transfer rate is an increase over the previous aero-combustor turbulence generator which had augmentation levels up to 110%. Note, the rate of increase in heat transfer augmentation decreases for the large cylindrical leading edge inferring only a limited level of turbulence intensification in the stagnation region. The smaller cylindrical leading edge shows more consistency with earlier stagnation region heat transfer results correlated on the TRL (Turbulence, Reynolds number, Length scale) parameter. The downstream regions of both test surfaces continue to accelerate the flow but at a much lower rate than the leading edge. Bypass transition occurs in these regions providing a useful set of data to ground the prediction of transition onset and length over a wide range of Reynolds numbers and turbulence intensity and scales.« less

Analyzing the effects of transit network change on agency performance and riders in a decentralized, small-to-mid-sized US metropolitan area : a case study of Tallahassee, Florida.

DOT National Transportation Integrated Search

2013-05-01

On July 11, 2011, StarMetro, the local public transit agency in Tallahassee, Florida, restructured its entire bus network from a : downtown-focused radial system to a decentralized, grid-like system that local officials and agency leaders believed wo...

Self-Emergent Peer Support Using Online Social Networking during Cross-Border Transition

ERIC Educational Resources Information Center

Ding, Feng; Stapleton, Paul

2015-01-01

Transitioning from school to university is a major development for learners, often accompanied by difficulties. When overseas students arrive at university for the first time these challenges are multiplied. It is suggested, however, that these difficulties can be mitigated to a certain extent via the use of online social networks. The present…

Internet Protocol Transition Workbook

DTIC Science & Technology

1982-03-01

U N C-* INTERNET PROTOCOL TRANSITION WORKBOOK March 1982 Network Information Canter SRI International Menlo Park, CA 94025 t tv l...Feinler Network Information Center SRI International Menlo Park. California 94025 (415) 859-3695 FEINLEROSRI-NIC (Online mail) [Page ii] I.7 Internet ...31 Postel. J., " Internet Control Message Protocol - DARPA Internet Program Protocol Specification." RFC 792, USC/ Information Sciences Institute

Starting Online: Exploring the Use of a Social Networking Site to Facilitate Transition into Higher Education

ERIC Educational Resources Information Center

Knight, John; Rochon, Rebecca

2012-01-01

It has been widely recognised that transition into higher education (HE) can be challenging for incoming students. Literature identifies three main areas where students may benefit from support: social, practical and academic. This paper discusses a case study that explores the potential of a social networking environment to provide support in…

Enhancing the Transition to University by Facilitating Social and Study Networks: Results of a One-Day Workshop.

ERIC Educational Resources Information Center

Peat, Mary; Dalziel, James; Grant, Anthony M.

2000-01-01

Describes a one-day workshop developed at the University of Sydney (Australia) to facilitate social and study-related peer networks. Qualitative and quantitative analyses found that the workshops enhanced study, self-motivation, and general enjoyment of university life and were helpful in easing the transition of undergraduate students.…

Phase Transitions of an Epidemic Spreading Model in Small-World Networks

NASA Astrophysics Data System (ADS)

Hua, Da-Yin; Gao, Ke

2011-06-01

We propose a modified susceptible-infected-refractory-susceptible (SIRS) model to investigate the global oscillations of the epidemic spreading in Watts—Strogatz (WS) small-world networks. It is found that when an individual immunity does not change or decays slowly in an immune period, the system can exhibit complex transition from an infecting stationary state to a large amplitude sustained oscillation or an absorbing state with no infection. When the immunity decays rapidly in the immune period, the transition to the global oscillation disappears and there is no oscillation. Furthermore, based on the spatio-temporal evolution patterns and the phase diagram, it is disclosed that a long immunity period takes an important role in the emergence of the global oscillation in small-world networks.

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

Global navigation satellite systems performance analysis and augmentation strategies in aviation

NASA Astrophysics Data System (ADS)

Sabatini, Roberto; Moore, Terry; Ramasamy, Subramanian

2017-11-01

In an era of significant air traffic expansion characterized by a rising congestion of the radiofrequency spectrum and a widespread introduction of Unmanned Aircraft Systems (UAS), Global Navigation Satellite Systems (GNSS) are being exposed to a variety of threats including signal interferences, adverse propagation effects and challenging platform-satellite relative dynamics. Thus, there is a need to characterize GNSS signal degradations and assess the effects of interfering sources on the performance of avionics GNSS receivers and augmentation systems used for an increasing number of mission-essential and safety-critical aviation tasks (e.g., experimental flight testing, flight inspection/certification of ground-based radio navigation aids, wide area navigation and precision approach). GNSS signal deteriorations typically occur due to antenna obscuration caused by natural and man-made obstructions present in the environment (e.g., elevated terrain and tall buildings when flying at low altitude) or by the aircraft itself during manoeuvring (e.g., aircraft wings and empennage masking the on-board GNSS antenna), ionospheric scintillation, Doppler shift, multipath, jamming and spurious satellite transmissions. Anyone of these phenomena can result in partial to total loss of tracking and possible tracking errors, depending on the severity of the effect and the receiver characteristics. After designing GNSS performance threats, the various augmentation strategies adopted in the Communication, Navigation, Surveillance/Air Traffic Management and Avionics (CNS + A) context are addressed in detail. GNSS augmentation can take many forms but all strategies share the same fundamental principle of providing supplementary information whose objective is improving the performance and/or trustworthiness of the system. Hence it is of paramount importance to consider the synergies offered by different augmentation strategies including Space Based Augmentation System (SBAS), Ground Based Augmentation System (GBAS), Aircraft Based Augmentation System (ABAS) and Receiver Autonomous Integrity Monitoring (RAIM). Furthermore, by employing multi-GNSS constellations and multi-sensor data fusion techniques, improvements in availability and continuity can be obtained. SBAS is designed to improve GNSS system integrity and accuracy for aircraft navigation and landing, while an alternative approach to GNSS augmentation is to transmit integrity and differential correction messages from ground-based augmentation systems (GBAS). In addition to existing space and ground based augmentation systems, GNSS augmentation may take the form of additional information being provided by other on-board avionics systems, such as in ABAS. As these on-board systems normally operate via separate principles than GNSS, they are not subject to the same sources of error or interference. Using suitable data link and data processing technologies on the ground, a certified ABAS capability could be a core element of a future GNSS Space-Ground-Aircraft Augmentation Network (SGAAN). Although current augmentation systems can provide significant improvement of GNSS navigation performance, a properly designed and flight-certified SGAAN could play a key role in trusted autonomous system and cyber-physical system applications such as UAS Sense-and-Avoid (SAA).

Transition to Chaos in Random Neuronal Networks

NASA Astrophysics Data System (ADS)

Kadmon, Jonathan; Sompolinsky, Haim

2015-10-01

Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos and its critical properties depend on the shape of the single-neuron nonlinear input-output transfer function, near firing threshold. In particular, for nonlinear transfer functions with a sharp rise near threshold, the transition to chaos disappears in the limit of a large network; instead, the system exhibits chaotic fluctuations even for small synaptic gain. Finally, we investigate transition to chaos in network models with spiking dynamics. We show that when synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate-based dynamics. Systems with finite synaptic time constants and firing rates exhibit a smooth transition from a regime dominated by stationary firing rates to a regime of slow rate fluctuations. This smooth crossover obeys scaling properties, similar to crossover phenomena in statistical mechanics. The theoretical results are supported by computer simulations of several neuronal architectures and dynamics. Consequences for cortical circuit dynamics are discussed. These results advance our understanding of the properties of intrinsic dynamics in realistic neuronal networks and their functional consequences.

Tradeoffs between costs and greenhouse gas emissions in the design of urban transit systems

NASA Astrophysics Data System (ADS)

Griswold, Julia B.; Madanat, Samer; Horvath, Arpad

2013-12-01

Recent investments in the transit sector to address greenhouse gas emissions have concentrated on purchasing efficient replacement vehicles and inducing mode shift from the private automobile. There has been little focus on the potential of network and operational improvements, such as changes in headways, route spacing, and stop spacing, to reduce transit emissions. Most models of transit system design consider user and agency cost while ignoring emissions and the potential environmental benefit of operational improvements. We use a model to evaluate the user and agency costs as well as greenhouse gas benefit of design and operational improvements to transit systems. We examine how the operational characteristics of urban transit systems affect both costs and greenhouse gas emissions. The research identifies the Pareto frontier for designing an idealized transit network. Modes considered include bus, bus rapid transit (BRT), light rail transit (LRT), and metro (heavy) rail, with cost and emissions parameters appropriate for the United States. Passenger demand follows a many-to-many travel pattern with uniformly distributed origins and destinations. The approaches described could be used to optimize the network design of existing bus service or help to select a mode and design attributes for a new transit system. The results show that BRT provides the lowest cost but not the lowest emissions for our large city scenarios. Bus and LRT systems have low costs and the lowest emissions for our small city scenarios. Relatively large reductions in emissions from the cost-optimal system can be achieved with only minor increases in user travel time.

A qualitative analysis of email interactions of children who use augmentative and alternative communication.

PubMed

Sundqvist, Anett; Rönnberg, Jerker

2010-12-01

The aim of this study was to introduce email as a form of interaction for a group of six children who used augmentative and alternative communication. In a 12-week exploratory study, aspects of the email messages sent were analyzed. The content of the messages was analyzed by an inductive qualitative method, and seven descriptive categories emerged. The most frequently occurring categories were Social Etiquette, Personal/Family Statistics and Personal Common Ground. The children utilized different email strategies that included use of most of the above-mentioned categories. Through the email writing practice, the children developed new social skills and increased their social participation. Email practice may be a good strategy to increase children's social networks.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-03-01

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

Mobile Voting Tools for Creating Collaboration Environment and a New Educational Design of the University Lecture

ERIC Educational Resources Information Center

Titova, Svetlana

2014-01-01

Mobile devices can enhance learning experience in many ways: provide instant feedback and better diagnosis of learning problems; enhance learner autonomy; create mobile networking collaboration; help design enquiry-based activities based on augmented reality, geo-location awareness and video-capture. One of the main objectives of the international…

Dynamic Data-Driven UAV Network for Plume Characterization

DTIC Science & Technology

2016-05-23

data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the...and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations...data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where

Stability-to-instability transition in the structure of large-scale networks

NASA Astrophysics Data System (ADS)

Hu, Dandan; Ronhovde, Peter; Nussinov, Zohar

2012-12-01

We examine phase transitions between the “easy,” “hard,” and “unsolvable” phases when attempting to identify structure in large complex networks (“community detection”) in the presence of disorder induced by network “noise” (spurious links that obscure structure), heat bath temperature T, and system size N. The partition of a graph into q optimally disjoint subgraphs or “communities” inherently requires Potts-type variables. In earlier work [Philos. Mag.1478-643510.1080/14786435.2011.616547 92, 406 (2012)], when examining power law and other networks (and general associated Potts models), we illustrated that transitions in the computational complexity of the community detection problem typically correspond to spin-glass-type transitions (and transitions to chaotic dynamics in mechanical analogs) at both high and low temperatures and/or noise. The computationally “hard” phase exhibits spin-glass type behavior including memory effects. The region over which the hard phase extends in the noise and temperature phase diagram decreases as N increases while holding the average number of nodes per community fixed. This suggests that in the thermodynamic limit a direct sharp transition may occur between the easy and unsolvable phases. When present, transitions at low temperature or low noise correspond to entropy driven (or “order by disorder”) annealing effects, wherein stability may initially increase as temperature or noise is increased before becoming unsolvable at sufficiently high temperature or noise. Additional transitions between contending viable solutions (such as those at different natural scales) are also possible. Identifying community structure via a dynamical approach where “chaotic-type” transitions were found earlier. The correspondence between the spin-glass-type complexity transitions and transitions into chaos in dynamical analogs might extend to other hard computational problems. In this work, we examine large networks (with a power law distribution in cluster size) that have a large number of communities (q≫1). We infer that large systems at a constant ratio of q to the number of nodes N asymptotically tend towards insolvability in the limit of large N for any positive T. The asymptotic behavior of temperatures below which structure identification might be possible, T×=O[1/lnq], decreases slowly, so for practical system sizes, there remains an accessible, and generally easy, global solvable phase at low temperature. We further employ multivariate Tutte polynomials to show that increasing q emulates increasing T for a general Potts model, leading to a similar stability region at low T. Given the relation between Tutte and Jones polynomials, our results further suggest a link between the above complexity transitions and transitions associated with random knots.

A Framework for Assessing Feasibility of Transit-Oriented Development (TOD) Project Sites : Research Brief

DOT National Transportation Integrated Search

2017-09-01

This research answers the question: How can a transit agency choose among alternative TOD locations within a transit network? The ultimate objective of the research is to develop a decision support framework which can be used by transit agencies when...

Design of personal rapid transit networks for transit-oriented development cities.

DOT National Transportation Integrated Search

2014-04-01

Personal rapid transit (PRT) is an automated transit system in which vehicles are sized to transport a batch of passengers on demand to their destinations, by means of nonstop and non-transfer on its own right-of-way. PRT vehicles run exclusively on ...

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Half-Metallic Ferromagnetism and Stability of Transition Metal Pnictides and Chalcogenides

NASA Astrophysics Data System (ADS)

Liu, Bang-Gui

It is highly desirable to explore robust half-metallic ferromagnetic materials compatible with important semiconductors for spintronic applications. A state-of-the-art full potential augmented plane wave method within the densityfunctional theory is reliable enough for this purpose. In this chapter we review theoretical research on half-metallic ferromagnetism and structural stability of transition metal pnictides and chalcogenides. We show that some zincblende transition metal pnictides are half-metallic and the half-metallic gap can be fairly wide, which is consistent with experiment. Systematic calculations reveal that zincblende phases of CrTe, CrSe, and VTe are excellent half-metallic ferromagnets. These three materials have wide half-metallic gaps, are low in total energy with respect to the corresponding ground-state phases, and, importantly, are structurally stable. Halfmetallic ferromagnetism is also found in wurtzite transition metal pnictides and chalcogenides and in transition-metal doped semiconductors as well as deformed structures. Some of these half-metallic materials could be grown epitaxially in the form of ultrathin .lms or layers suitable for real spintronic applications.

V/STOL propulsion control analysis: Phase 2, task 5-9

NASA Technical Reports Server (NTRS)

1981-01-01

Typical V/STOL propulsion control requirements were derived for transition between vertical and horizontal flight using the General Electric RALS (Remote Augmented Lift System) concept. Steady-state operating requirements were defined for a typical Vertical-to-Horizontal transition and for a typical Horizontal-to-Vertical transition. Control mode requirements were established and multi-variable regulators developed for individual operating conditions. Proportional/Integral gain schedules were developed and were incorporated into a transition controller with capabilities for mode switching and manipulated variable reassignment. A non-linear component-level transient model of the engine was developed and utilized to provide a preliminary check-out of the controller logic. An inlet and nozzle effects model was developed for subsequent incorporation into the engine model and an aircraft model was developed for preliminary flight transition simulations. A condition monitoring development plan was developed and preliminary design requirements established. The Phase 1 long-range technology plan was refined and restructured toward the development of a real-time high fidelity transient model of a supersonic V/STOL propulsion system and controller for use in a piloted simulation program at NASA-Ames.

Mesenchymal-endothelial-transition contributes to cardiac neovascularization

PubMed Central

Ubil, Eric; Duan, Jinzhu; Pillai, Indulekha C.L.; Rosa-Garrido, Manuel; Wu, Yong; Bargiacchi, Francesca; Lu, Yan; Stanbouly, Seta; Huang, Jie; Rojas, Mauricio; Vondriska, Thomas M.; Stefani, Enrico; Deb, Arjun

2014-01-01

Endothelial cells contribute to a subset of cardiac fibroblasts by undergoing endothelial-to-mesenchymal-transition, but whether cardiac fibroblasts can adopt an endothelial cell fate and directly contribute to neovascularization after cardiac injury is not known. Here, using genetic fate map techniques, we demonstrate that cardiac fibroblasts rapidly adopt an endothelial cell like phenotype after acute ischemic cardiac injury. Fibroblast derived endothelial cells exhibit anatomical and functional characteristics of native endothelial cells. We show that the transcription factor p53 regulates such a switch in cardiac fibroblast fate. Loss of p53 in cardiac fibroblasts severely decreases the formation of fibroblast derived endothelial cells, reduces post infarct vascular density and worsens cardiac function. Conversely, stimulation of the p53 pathway in cardiac fibroblasts augments mesenchymal to endothelial transition, enhances vascularity and improves cardiac function. These observations demonstrate that mesenchymal-to-endothelial-transition contributes to neovascularization of the injured heart and represents a potential therapeutic target for enhancing cardiac repair. PMID:25317562

Mitochondrial network complexity emerges from fission/fusion dynamics.

PubMed

Zamponi, Nahuel; Zamponi, Emiliano; Cannas, Sergio A; Billoni, Orlando V; Helguera, Pablo R; Chialvo, Dante R

2018-01-10

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Complex quantum network geometries: Evolution and phase transitions

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Complex quantum network geometries: Evolution and phase transitions.

PubMed

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Dynamical modeling and analysis of large cellular regulatory networks

NASA Astrophysics Data System (ADS)

Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

2013-06-01

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

In-Vivo Animation of Auditory-Language-Induced Gamma-Oscillations in Children with Intractable Focal Epilepsy

PubMed Central

Brown, Erik C.; Rothermel, Robert; Nishida, Masaaki; Juhász, Csaba; Muzik, Otto; Hoechstetter, Karsten; Sood, Sandeep; Chugani, Harry T.; Asano, Eishi

2008-01-01

We determined if high-frequency gamma-oscillations (50- to 150-Hz) were induced by simple auditory communication over the language network areas in children with focal epilepsy. Four children (ages: 7, 9, 10 and 16 years) with intractable left-hemispheric focal epilepsy underwent extraoperative electrocorticography (ECoG) as well as language mapping using neurostimulation and auditory-language-induced gamma-oscillations on ECoG. The audible communication was recorded concurrently and integrated with ECoG recording to allow for accurate time-lock upon ECoG analysis. In three children, who successfully completed the auditory-language task, high-frequency gamma-augmentation sequentially involved: i) the posterior superior temporal gyrus when listening to the question, ii) the posterior lateral temporal region and the posterior frontal region in the time interval between question completion and the patient’s vocalization, and iii) the pre- and post-central gyri immediately preceding and during the patient’s vocalization. The youngest child, with attention deficits, failed to cooperate during the auditory-language task, and high-frequency gamma-augmentation was noted only in the posterior superior temporal gyrus when audible questions were given. The size of language areas suggested by statistically-significant high-frequency gamma-augmentation was larger than that defined by neurostimulation. The present method can provide in-vivo imaging of electrophysiological activities over the language network areas during language processes. Further studies are warranted to determine whether recording of language-induced gamma-oscillations can supplement language mapping using neurostimulation in presurgical evaluation of children with focal epilepsy. PMID:18455440

Finite-time scaling at the Anderson transition for vibrations in solids

NASA Astrophysics Data System (ADS)

Beltukov, Y. M.; Skipetrov, S. E.

2017-11-01

A model in which a three-dimensional elastic medium is represented by a network of identical masses connected by springs of random strengths and allowed to vibrate only along a selected axis of the reference frame exhibits an Anderson localization transition. To study this transition, we assume that the dynamical matrix of the network is given by a product of a sparse random matrix with real, independent, Gaussian-distributed nonzero entries and its transpose. A finite-time scaling analysis of the system's response to an initial excitation allows us to estimate the critical parameters of the localization transition. The critical exponent is found to be ν =1.57 ±0.02 , in agreement with previous studies of the Anderson transition belonging to the three-dimensional orthogonal universality class.

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

PubMed Central

Wang, Junjie; Li, Yishuai; Liu, Jingyu; He, Kun; Wang, Pu

2013-01-01

Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers' travel demands. Although passengers' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment's vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco. PMID:24260355

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

NASA Astrophysics Data System (ADS)

Fang, Jin-Qing; Li, Yong

2010-02-01

A large unified hybrid network model with a variable speed growth (LUHNM-VSG) is proposed as third model of the unified hybrid network theoretical framework (UHNTF). A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index α are introduced in it. The main effects of vg and α on topological transition features of the LUHNM-VSG are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios (dr, fd, gr, vg). Thus the most of network models can be investigated in the unification way via four hybrid ratios (dr, fd, gr, vg). The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

Bayesian network modelling of upper gastrointestinal bleeding

NASA Astrophysics Data System (ADS)

Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

2013-09-01

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

Augmented Cross-Sectional Studies with Abbreviated Follow-up for Estimating HIV Incidence

PubMed Central

Claggett, B.; Lagakos, S.W.; Wang, R.

2011-01-01

Summary Cross-sectional HIV incidence estimation based on a sensitive and less-sensitive test offers great advantages over the traditional cohort study. However, its use has been limited due to concerns about the false negative rate of the less-sensitive test, reflecting the phenomenon that some subjects may remain negative permanently on the less-sensitive test. Wang and Lagakos (2010) propose an augmented cross-sectional design which provides one way to estimate the size of the infected population who remain negative permanently and subsequently incorporate this information in the cross-sectional incidence estimator. In an augmented cross-sectional study, subjects who test negative on the less-sensitive test in the cross-sectional survey are followed forward for transition into the nonrecent state, at which time they would test positive on the less-sensitive test. However, considerable uncertainty exists regarding the appropriate length of follow-up and the size of the infected population who remain nonreactive permanently to the less-sensitive test. In this paper, we assess the impact of varying follow-up time on the resulting incidence estimators from an augmented cross-sectional study, evaluate the robustness of cross-sectional estimators to assumptions about the existence and the size of the subpopulation who will remain negative permanently, and propose a new estimator based on abbreviated follow-up time (AF). Compared to the original estimator from an augmented cross-sectional study, the AF Estimator allows shorter follow-up time and does not require estimation of the mean window period, defined as the average time between detectability of HIV infection with the sensitive and less-sensitive tests. It is shown to perform well in a wide range of settings. We discuss when the AF Estimator would be expected to perform well and offer design considerations for an augmented cross-sectional study with abbreviated follow-up. PMID:21668904

Augmented cross-sectional studies with abbreviated follow-up for estimating HIV incidence.

PubMed

Claggett, B; Lagakos, S W; Wang, R

2012-03-01

Cross-sectional HIV incidence estimation based on a sensitive and less-sensitive test offers great advantages over the traditional cohort study. However, its use has been limited due to concerns about the false negative rate of the less-sensitive test, reflecting the phenomenon that some subjects may remain negative permanently on the less-sensitive test. Wang and Lagakos (2010, Biometrics 66, 864-874) propose an augmented cross-sectional design that provides one way to estimate the size of the infected population who remain negative permanently and subsequently incorporate this information in the cross-sectional incidence estimator. In an augmented cross-sectional study, subjects who test negative on the less-sensitive test in the cross-sectional survey are followed forward for transition into the nonrecent state, at which time they would test positive on the less-sensitive test. However, considerable uncertainty exists regarding the appropriate length of follow-up and the size of the infected population who remain nonreactive permanently to the less-sensitive test. In this article, we assess the impact of varying follow-up time on the resulting incidence estimators from an augmented cross-sectional study, evaluate the robustness of cross-sectional estimators to assumptions about the existence and the size of the subpopulation who will remain negative permanently, and propose a new estimator based on abbreviated follow-up time (AF). Compared to the original estimator from an augmented cross-sectional study, the AF estimator allows shorter follow-up time and does not require estimation of the mean window period, defined as the average time between detectability of HIV infection with the sensitive and less-sensitive tests. It is shown to perform well in a wide range of settings. We discuss when the AF estimator would be expected to perform well and offer design considerations for an augmented cross-sectional study with abbreviated follow-up. © 2011, The International Biometric Society.

Stakeholders, Networks and Links in Early Childhood Policy: Network Analysis and the "Transition to School: Position Statement"

ERIC Educational Resources Information Center

Wallis, Jake; Dockett, Sue

2015-01-01

The importance of a positive start to school has been highlighted in a range of national and international research. This has stimulated considerable ongoing research attention, as well as initiatives across policy and practice, all with the aim of promoting a positive transition to school for all children. Despite the common interests across…

Antiwindup analysis and design approaches for MIMO systems

NASA Technical Reports Server (NTRS)

Marcopoli, Vincent R.; Phillips, Stephen M.

1994-01-01

Performance degradation of multiple-input multiple-output (MIMO) control systems having limited actuators is often handled by augmenting the controller with an antiwindup mechanism, which attempts to maintain system performance when limits are encountered. The goals of this paper are: (1) To develop a method to analyze antiwindup systems to determine precisely what stability and performance degradation is incurred under limited conditions. It is shown that by reformulating limited actuator commands as resulting from multiplicative perturbations to the corresponding controller requests, mu-analysis tools can be utilized to obtain quantitative measures of stability and performance degradation. (2) To propose a linear, time invariant (LTI) criterion on which to base the antiwindup design. These analysis and design methods are illustrated through the evaluation of two competing antiwindup schemes augmenting the controller of a Short Take-Off and Vertical Landing (STOVL) aircraft in transition flight.

Antiwindup analysis and design approaches for MIMO systems

NASA Technical Reports Server (NTRS)

Marcopoli, Vincent R.; Phillips, Stephen M.

1993-01-01

Performance degradation of multiple-input multiple-output (MIMO) control systems having limited actuators is often handled by augmenting the controller with an antiwindup mechanism, which attempts to maintain system performance when limits are encountered. The goals of this paper are: 1) to develop a method to analyze antiwindup systems to determine precisely what stability and performance degradation is incurred under limited conditions. It is shown that by reformulating limited actuator commands as resulting from multiplicative perturbations to the corresponding controller requests, mu-analysis tools can be utilized to obtain quantitative measures of stability and performance degradation. 2) To propose a linear, time invariant (LTI) criterion on which to base the antiwindup design. These analysis and design methods are illustrated through the evaluation of two competing antiwindup schemes augmenting the controller of a Short Take-Off and Vertical Landing (STOVL) aircraft in transition flight.

Morphological transitions of brain sphingomyelin are determined by the hydration protocol: ripples re-arrange in plane, and sponge-like networks disintegrate into small vesicles.

PubMed

Meyer, H W; Bunjes, H; Ulrich, A S

1999-06-01

The phase transition of hydrated brain sphingomyelin occurs at around 35 degrees C, which is close to the physiological temperature. Freeze-fracture electron microscopy is used to characterize different gel state morphologies in terms of solid-ordered and liquid-ordered phase states, according to the occurrence of ripples and other higher-dimensional bilayer deformations. Evidently, the natural mixed-chain sphingomyelin does not assume the flat L beta, phase but instead the rippled P beta, phase, with symmetric and asymmetric ripples as well as macroripples and an egg-carton pattern, depending on the incubation conditions. An unexpected difference was observed between samples that are hydrated above and below the phase transition temperature. When the lipid is hydrated at low temperature, a sponge-like network of bilayers is formed in the gel state, next to some normal lamellae. The network loses its ripples during cold-incubation, which indicates the formation of a liquid-ordered (lo) gel phase. Ripples re-appear upon warming and the sponge-like network disintegrates spontaneously and irreversibly into small vesicles above the phase transition.

Polaronic Transport in Phosphate Glasses Containing Transition Metal Ions

NASA Astrophysics Data System (ADS)

Henderson, Mark

The goal of this dissertation is to characterize the basic transport properties of phosphate glasses containing various amounts of TIs and to identify and explain any electronic phase transitions which may occur. The P2 O5-V2O5-WO3 (PVW) glass system will be analyzed to find the effect of TI concentration on conduction. In addition, the effect of the relative concentrations of network forming ions (SiO2 and P2O5) on transport will be studied in the P2O5-SiO2-Fe2O 3 (PSF) system. Also presented is a numerical study on a tight-binding model adapted for the purposes of modelling Gaussian traps, mimicking TI's, which are arranged in an extended network. The results of this project will contribute to the development of fundamental theories on the electronic transport in glasses containing mixtures of transition oxides as well as those containing multiple network formers without discernible phase separation. The present study on the PVW follows up on previous investigation into the effect on mixed transition ions in oxide glasses. Past research has focused on glasses containing transition metal ions from the 3d row. The inclusion of tungsten, a 5d transition metal, adds a layer of complexity through the mismatch of the energies of the orbitals contributing to localized states. The data have indicated that a transition reminiscent of a metal-insulator transition (MIT) occurs in this system as the concentration of tungsten increases. As opposed to some other MIT-like transitions found in phosphate glass systems, there seems to be no polaron to bipolaron conversion. Instead, the individual localization parameter for tungsten noticeably decreases dramatically at the transition point as well as the adiabaticity. Another distinctive feature of this project is the study of the PSF system, which contains two true network formers, phosphorous pentoxide (P2O 5) and silicon dioxide (SiO2). It is not usually possible to do a reliable investigation of the conduction properties of such glasses because the two network formers will tend to separate into different phases, making it difficult to obtain homogenous samples. The PSF system proved easier to study than other systems. The hopping in this system seems to be dominated by the Greaves mid-range mechanism. In addition, in samples containing the same proportion of iron, conductivities were found to not depend noticeably on composition, supporting the use of models focusing on the transition metal ions in calculating conductivity. Despite ostensibly changing the structural and metrical properties of the network, the ratio of the concentration of the network formers only appears to have an effect on the conductivity through changing the inter-atomic distance of iron. The numerical model adds to the evidence for the dominating contribution on the nearest-neighbor ordering of TI ions on the electrical properties of a glass; especially interesting is the reproducibility of the mixed-transition ion effect (MTE) in a numerical model where ensemble averages are taken over possible arrangements. It was also determined that the disorder arising from the spread between two types of traps can lead to a MIT as function of population. Finally, an outline of the notion of invariance in TI glasses is extended from work done by other authors, creating an opportunity for further research.

Reliable routing in transit networks.

DOT National Transportation Integrated Search

2013-07-02

The objectives of this project are (1) to make use of the newly emerging transit data sources : for evaluating the variations in transit services (especially headway), and (2) to help passengers : find optimal routing strategies to hedge against thes...

Transit Fare Prepayment Distribution Methods in Sacramento, CA

DOT National Transportation Integrated Search

1985-06-01

This demonstration tested the use of new methods to distribute transit fare prepayment (TFP) instruments at the Sacramento Regional Transit district (RT). Five new distribution methods were implemented to supplement a network of public, private, and ...

Potential Greenhouse Gas Emissions Reductions from Optimizing Urban Transit Networks

DOT National Transportation Integrated Search

2016-05-01

Public transit systems with efficient designs and operating plans can reduce greenhouse gas (GHG) emissions relative to low-occupancy transportation modes, but many current transit systems have not been designed to reduce environmental impacts. This ...

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model.

PubMed

Papasavvas, Christoforos A; Wang, Yujiang; Trevelyan, Andrew J; Kaiser, Marcus

2015-09-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics.

Augmented Performance Environment for Enhancing Interagency Coordination in Stability, Security, Transition, and Reconstruction (SSTR) Operations

DTIC Science & Technology

2009-02-01

assessments and meeting rehearsal and individual learning materials • Specify the metrics to be used to capture the quality of interagency...government; • Improve security; and • Promote reconstruction (Barno, 2004; Dziedzic & Siedl, 2005; Center for Army Lessons Learned (CALL), 2007). The...their orientations and creating group-level, hierarchical orientations out of the aggregated individual orientations (Wan, Chiu, Peng, & Tam , 2007

Nanotextured phase coexistence in the correlated insulator V2O3

NASA Astrophysics Data System (ADS)

McLeod, Alexander

The Mott insulator-metal transition remains among the most studied phenomena in correlated electron physics. However, the formation of spontaneous spatial patterns amidst coexisting insulating and metallic phases remains poorly explored on the meso- and nanoscales. Here we present real-space evolution of the insulator-metal transition in a thin film of V2O3, the ``canonical'' Mott insulator, imaged at high spatial resolution by cryogenic near-field infrared microscopy. We resolve spontaneously nanotextured coexistence of metal and correlated Mott insulator phases near the insulator-metal transition (T = 160-180 K) associated with percolation and an underlying structural phase transition. Augmented with macroscopic temperature-resolved X-ray diffraction measurements of the same film, a quantitative analysis of nano-infrared images acquired across the transition suggests decoupling of electronic and structural transformations. Persistent low-temperature metallicity is accompanied by unconventional dimensional scaling among metallic ``puddles,'' implicating relevance of a long-range Coulombic interaction through the film's first-order insulator-metal transition. The speaker and co-authors acknowledge support from DOE-DE-SC0012375, DOE-DE-SC0012592, and AFOSR Grant No. FA9550-12-1-0381. The speaker also acknowledges support from a US Dept. of Energy Office of Science Graduate Fellowship (DOE SCGF).

NMR Shielding in Metals Using the Augmented Plane Wave Method

PubMed Central

2015-01-01

We present calculations of solid state NMR magnetic shielding in metals, which includes both the orbital and the complete spin response of the system in a consistent way. The latter contains an induced spin-polarization of the core states and needs an all-electron self-consistent treatment. In particular, for transition metals, the spin hyperfine field originates not only from the polarization of the valence s-electrons, but the induced magnetic moment of the d-electrons polarizes the core s-states in opposite direction. The method is based on DFT and the augmented plane wave approach as implemented in the WIEN2k code. A comparison between calculated and measured NMR shifts indicates that first-principle calculations can obtain converged results and are more reliable than initially concluded based on previous publications. Nevertheless large k-meshes (up to 2 000 000 k-points in the full Brillouin-zone) and some Fermi-broadening are necessary. Our results show that, in general, both spin and orbital components of the NMR shielding must be evaluated in order to reproduce experimental shifts, because the orbital part cancels the shift of the usually highly ionic reference compound only for simple sp-elements but not for transition metals. This development paves the way for routine NMR calculations of metallic systems. PMID:26322148

Transition Communities and the Glass Ceiling of Environmental Sustainability Policies at Three Universities

ERIC Educational Resources Information Center

Pardellas Santiago, Miguel; Meira Cartea, Pablo; Iglesias da Cunha, Lucía

2017-01-01

Purpose: This paper deals with the experiences of three European universities that have implemented transition initiatives, using the Transition Network's methodology to promote their sustainability plans. The Transition Communities' model for change is presented from a socio-educational perspective as an effective methodology for encouraging…

VizieR Online Data Catalog: Transit times for Kepler-79's known planets (Jontof-Hutter+, 2014)

NASA Astrophysics Data System (ADS)

Jontof-Hutter, D.; Lissauer, J. J.; Rowe, J. F.; Fabrycky, D. C.

2017-06-01

Variations in the brightness of Kepler-79 were monitored with an effective duty cycle exceeding 90% starting at barycentric Julian date (BJD) 2454964.512, with all data returned to Earth at a cadence of 29.426 minutes (long cadence, LC); data were also returned at a cadence of 58.85 s (short cadence, SC) beginning from BJD 2455093.216. Here and throughout we base our timeline for transit data from T=JD-2454900. Our analysis uses SC data where available, augmented by the LC data set primarily during the epoch prior to T<193 days, for which no SC data were returned to Earth. (1 data file).

Dimensional reduction of the Standard Model coupled to a new singlet scalar field

NASA Astrophysics Data System (ADS)

Brauner, Tomáš; Tenkanen, Tuomas V. I.; Tranberg, Anders; Vuorinen, Aleksi; Weir, David J.

2017-03-01

We derive an effective dimensionally reduced theory for the Standard Model augmented by a real singlet scalar. We treat the singlet as a superheavy field and integrate it out, leaving an effective theory involving only the Higgs and SU(2) L × U(1) Y gauge fields, identical to the one studied previously for the Standard Model. This opens up the possibility of efficiently computing the order and strength of the electroweak phase transition, numerically and nonperturbatively, in this extension of the Standard Model. Understanding the phase diagram is crucial for models of electroweak baryogenesis and for studying the production of gravitational waves at thermal phase transitions.

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

NASA Technical Reports Server (NTRS)

Navarro, Robert

2006-01-01

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

Potts Model in One-Dimension on Directed Small-World Networks

NASA Astrophysics Data System (ADS)

Aquino, Édio O.; Lima, F. W. S.; Araújo, Ascânio D.; Costa Filho, Raimundo N.

2018-06-01

The critical properties of the Potts model with q=3 and 8 states in one-dimension on directed small-world networks are investigated. This disordered system is simulated by updating it with the Monte Carlo heat bath algorithm. The Potts model on these directed small-world networks presents in fact a second-order phase transition with a new set of critical exponents for q=3 considering a rewiring probability p=0.1. For q=8 the system exhibits only a first-order phase transition independent of p.

Exploiting Data Missingness in Bayesian Network Modeling

NASA Astrophysics Data System (ADS)

Rodrigues de Morais, Sérgio; Aussem, Alex

This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.

New concepts of histological changes in experimental augmentation cystoplasty: insights into the development of neoplastic transformation at the enterovesical and gastrovesical anastomosis.

PubMed

Gitlin, J S; Wu, X R; Sun, T T; Ritchey, M L; Shapiro, E

1999-09-01

To our knowledge the pathogenesis of malignancy associated with ileal cystoplasty, ureterosigmoidostomy and ileal conduits is currently unknown. To gain further insights into the mechanism of neoplastic transformation we studied histological changes in a canine augmentation cystoplasty model. Enterocystoplasty and gastrocystoplasty were performed using a 5 to 7 cm. patch of ileum in 8 dogs and gastric antrum in 6. Specimens were harvested 4 months postoperatively. Representative 3 microm sections of the enterovesical and gastrovesical junctions were stained with hematoxylin and eosin. Uroplakin expression was assessed using an indirect peroxidase method subjected to double staining with alcian blue and periodic acid-Schiffreagent. The bladder portion of the augmentation cystoplasty had 3 to 4 stratified cell layers covered with a distinctive umbrella cell layer. Strong uroplakin staining was visible in all cell layers except the basal layer. At the enterovesical and gastrovesical junctions 6 to 10 layers of hyperplastic, urothelial appearing cells covered the glandular epithelium of the ileal and gastric segments. These cells expressed uroplakins. At this junction zone there was a marked decrease of underlying enteric glands, which had atrophied in proportion to the degree of urothelial hyperplasia. Double staining of uroplakin stained sections with alcian blue and periodic acid-Schiff reagent revealed mucosubstances in hyperplastic urothelial cells covering the enteral segments, indicating that the cells co-expressed uroplakins and mucins. Histological changes in this experimental canine model of augmentation cystoplasty indicated that the overgrowth of hyperplastic transitional epithelium develops at the enterovesical and gastrovesical junctions. These cells express not only uroplakins, but also mucosubstances. Our results suggest that the migrated hyperplastic urothelial cells have undergone changes characteristic of the enteric and gastric epithelium, which may have important implications in the pathogenesis of malignancy in bladder augmentations.

Transplantation of autologous differentiated urothelium in an experimental model of composite cystoplasty.

PubMed

Turner, Alex; Subramanian, Ramnath; Thomas, David F M; Hinley, Jennifer; Abbas, Syed Khawar; Stahlschmidt, Jens; Southgate, Jennifer

2011-03-01

Enterocystoplasty is associated with serious complications resulting from the chronic interaction between intestinal epithelium and urine. Composite cystoplasty is proposed as a means of overcoming these complications by substituting intestinal epithelium with tissue-engineered autologous urothelium. To develop a robust surgical procedure for composite cystoplasty and to determine if outcome is improved by transplantation of a differentiated urothelium. Bladder augmentation with in vitro-generated autologous tissues was performed in 11 female Large-White hybrid pigs in a well-equipped biomedical centre with operating facilities. Participants were a team comprising scientists, urologists, a veterinary surgeon, and a histopathologist. Urothelium harvested by open biopsy was expanded in culture and used to develop sheets of nondifferentiated or differentiated urothelium. The sheets were transplanted onto a vascularised, de-epithelialised, seromuscular colonic segment at the time of bladder augmentation. After removal of catheters and balloon at two weeks, voiding behaviour was monitored and animals were sacrificed at 3 months for immunohistology. Eleven pigs underwent augmentation, but four were lost to complications. Voiding behaviour was normal in the remainder. At autopsy, reconstructed bladders were healthy, lined by confluent urothelium, and showed no fibrosis, mucus, calculi, or colonic regrowth. Urothelial morphology was transitional with variable columnar attributes consistent between native and augmented segments. Bladders reconstructed with differentiated cell sheets had fewer lymphocytes infiltrating the lamina propria, indicating more effective urinary barrier function. The study endorses the potential for composite cystoplasty by (1) successfully developing reliable techniques for transplanting urothelium onto a prepared, vascularised, smooth muscle segment and (2) creating a functional urothelium-lined augmentation to overcome the complications of conventional enterocystoplasty. Copyright © 2010 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Transplantation of Autologous Differentiated Urothelium in an Experimental Model of Composite Cystoplasty

PubMed Central

Turner, Alex; Subramanian, Ramnath; Thomas, David F.M.; Hinley, Jennifer; Abbas, Syed Khawar; Stahlschmidt, Jens; Southgate, Jennifer

2011-01-01

Background Enterocystoplasty is associated with serious complications resulting from the chronic interaction between intestinal epithelium and urine. Composite cystoplasty is proposed as a means of overcoming these complications by substituting intestinal epithelium with tissue-engineered autologous urothelium. Objective To develop a robust surgical procedure for composite cystoplasty and to determine if outcome is improved by transplantation of a differentiated urothelium. Design, setting, and participants Bladder augmentation with in vitro–generated autologous tissues was performed in 11 female Large-White hybrid pigs in a well-equipped biomedical centre with operating facilities. Participants were a team comprising scientists, urologists, a veterinary surgeon, and a histopathologist. Measurements Urothelium harvested by open biopsy was expanded in culture and used to develop sheets of nondifferentiated or differentiated urothelium. The sheets were transplanted onto a vascularised, de-epithelialised, seromuscular colonic segment at the time of bladder augmentation. After removal of catheters and balloon at two weeks, voiding behaviour was monitored and animals were sacrificed at 3 months for immunohistology. Results and limitations Eleven pigs underwent augmentation, but four were lost to complications. Voiding behaviour was normal in the remainder. At autopsy, reconstructed bladders were healthy, lined by confluent urothelium, and showed no fibrosis, mucus, calculi, or colonic regrowth. Urothelial morphology was transitional with variable columnar attributes consistent between native and augmented segments. Bladders reconstructed with differentiated cell sheets had fewer lymphocytes infiltrating the lamina propria, indicating more effective urinary barrier function. Conclusions The study endorses the potential for composite cystoplasty by (1) successfully developing reliable techniques for transplanting urothelium onto a prepared, vascularised, smooth muscle segment and (2) creating a functional urothelium-lined augmentation to overcome the complications of conventional enterocystoplasty. PMID:21195539

A hybrid formulation for the numerical simulation of condensed phase explosives

NASA Astrophysics Data System (ADS)

Michael, L.; Nikiforakis, N.

2016-07-01

In this article we present a new formulation and an associated numerical algorithm, for the simulation of combustion and transition to detonation of condensed-phase commercial- and military-grade explosives, which are confined by (or in general interacting with one or more) compliant inert materials. Examples include confined rate-stick problems and interaction of shock waves with gas cavities or solid particles in explosives. This formulation is based on an augmented Euler approach to account for the mixture of the explosive and its products, and a multi-phase diffuse interface approach to solve for the immiscible interaction between the mixture and the inert materials, so it is in essence a hybrid (augmented Euler and multi-phase) model. As such, it has many of the desirable features of the two approaches and, critically for our applications of interest, it provides the accurate recovery of temperature fields across all components. Moreover, it conveys a lot more physical information than augmented Euler, without the complexity of full multi-phase Baer-Nunziato-type models or the lack of robustness of augmented Euler models in the presence of more than two components. The model can sustain large density differences across material interfaces without the presence of spurious oscillations in velocity and pressure, and it can accommodate realistic equations of state and arbitrary (pressure- or temperature-based) reaction-rate laws. Under certain conditions, we show that the formulation reduces to well-known augmented Euler or multi-phase models, which have been extensively validated and used in practice. The full hybrid model and its reduced forms are validated against problems with exact (or independently-verified numerical) solutions and evaluated for robustness for rate-stick and shock-induced cavity collapse case-studies.

Effects of frustration on explosive synchronization

NASA Astrophysics Data System (ADS)

Huang, Xia; Gao, Jian; Sun, Yu-Ting; Zheng, Zhi-Gang; Xu, Can

2016-12-01

In this study, we consider the emergence of explosive synchronization in scale-free networks by considering the Kuramoto model of coupled phase oscillators. The natural frequencies of oscillators are assumed to be correlated with their degrees and frustration is included in the system. This assumption can enhance or delay the explosive transition to synchronization. Interestingly, a de-synchronization phenomenon occurs and the type of phase transition is also changed. Furthermore, we provide an analytical treatment based on a star graph, which resembles that obtained in scale-free networks. Finally, a self-consistent approach is implemented to study the de-synchronization regime. Our findings have important implications for controlling synchronization in complex networks because frustration is a controllable parameter in experiments and a discontinuous abrupt phase transition is always dangerous in engineering in the real world.

Synchronization of mobile chaotic oscillator networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fujiwara, Naoya, E-mail: fujiwara@csis.u-tokyo.ac.jp; Kurths, Jürgen; Díaz-Guilera, Albert

We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to themore » transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.« less

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis.

PubMed

Fayyaz S, S Kiavash; Liu, Xiaoyue Cathy; Zhang, Guohui

2017-01-01

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George's transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis

PubMed Central

Fayyaz S., S. Kiavash; Zhang, Guohui

2017-01-01

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. PMID:28981544

Critical space-time networks and geometric phase transitions from frustrated edge antiferromagnetism

NASA Astrophysics Data System (ADS)

Trugenberger, Carlo A.

2015-12-01

Recently I proposed a simple dynamical network model for discrete space-time that self-organizes as a graph with Hausdorff dimension dH=4 . The model has a geometric quantum phase transition with disorder parameter (dH-ds) , where ds is the spectral dimension of the dynamical graph. Self-organization in this network model is based on a competition between a ferromagnetic Ising model for vertices and an antiferromagnetic Ising model for edges. In this paper I solve a toy version of this model defined on a bipartite graph in the mean-field approximation. I show that the geometric phase transition corresponds exactly to the antiferromagnetic transition for edges, the dimensional disorder parameter of the former being mapped to the staggered magnetization order parameter of the latter. The model has a critical point with long-range correlations between edges, where a continuum random geometry can be defined, exactly as in Kazakov's famed 2D random lattice Ising model but now in any number of dimensions.

Coevolving complex networks in the model of social interactions

NASA Astrophysics Data System (ADS)

Raducha, Tomasz; Gubiec, Tomasz

2017-04-01

We analyze Axelrod's model of social interactions on coevolving complex networks. We introduce four extensions with different mechanisms of edge rewiring. The models are intended to catch two kinds of interactions-preferential attachment, which can be observed in scientists or actors collaborations, and local rewiring, which can be observed in friendship formation in everyday relations. Numerical simulations show that proposed dynamics can lead to the power-law distribution of nodes' degree and high value of the clustering coefficient, while still retaining the small-world effect in three models. All models are characterized by two phase transitions of a different nature. In case of local rewiring we obtain order-disorder discontinuous phase transition even in the thermodynamic limit, while in case of long-distance switching discontinuity disappears in the thermodynamic limit, leaving one continuous phase transition. In addition, we discover a new and universal characteristic of the second transition point-an abrupt increase of the clustering coefficient, due to formation of many small complete subgraphs inside the network.

Global Transportation Network : the heart of in-transit visibility

DOT National Transportation Integrated Search

1999-03-01

The Persian Gulf War highlighted problems concerning in-transit visibility (ITV). The lack of in-transit visibility resulted in over 20,000 of 40,000 containers entering the theater of operations being opened, inventoried, resealed, and shipped back ...

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Mateos, José L.

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport.

PubMed

Riascos, A P; Mateos, José L

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

"My World Has Expanded Even Though I'm Stuck at Home": Experiences of Individuals With Amyotrophic Lateral Sclerosis Who Use Augmentative and Alternative Communication and Social Media.

PubMed

Caron, Jessica; Light, Janice

2015-11-01

This study aimed to expand the current understanding of how persons with amyotrophic lateral sclerosis (pALS) use augmentative and alternative communication and social media to address their communication needs. An online focus group was used to investigate the experiences of 9 pALS who use augmentative and alternative communication and social media. Questions posed to the group related to (a) current use of social media, (b) advantages of social media, (c) barriers to independent use, (d) supports to independent use, and (e) recommendations for developers, policy makers, and other pALS. Participants primarily reported that use of social media was a beneficial tool that provided increased communication opportunities, connections to communication partners, and networks of support. Specific results are discussed with reference to the research as well as implications for practice and recommendations for future research. As individuals with ALS experience loss of function, some communication modes may no longer be viable. Providing access to different modes of communication, including social media, can allow independence, participation and better quality of life.

NRSF-dependent epigenetic mechanisms contribute to programming of stress-sensitive neurons by neonatal experience, promoting resilience.

PubMed

Singh-Taylor, A; Molet, J; Jiang, S; Korosi, A; Bolton, J L; Noam, Y; Simeone, K; Cope, J; Chen, Y; Mortazavi, A; Baram, T Z

2018-03-01

Resilience to stress-related emotional disorders is governed in part by early-life experiences. Here we demonstrate experience-dependent re-programming of stress-sensitive hypothalamic neurons, which takes place through modification of neuronal gene expression via epigenetic mechanisms. Specifically, we found that augmented maternal care reduced glutamatergic synapses onto stress-sensitive hypothalamic neurons and repressed expression of the stress-responsive gene, Crh. In hypothalamus in vitro, reduced glutamatergic neurotransmission recapitulated the repressive effects of augmented maternal care on Crh, and this required recruitment of the transcriptional repressor repressor element-1 silencing transcription factor/neuron restrictive silencing factor (NRSF). Increased NRSF binding to chromatin was accompanied by sequential repressive epigenetic changes which outlasted NRSF binding. chromatin immunoprecipitation-seq analyses of NRSF targets identified gene networks that, in addition to Crh, likely contributed to the augmented care-induced phenotype, including diminished depression-like and anxiety-like behaviors. Together, we believe these findings provide the first causal link between enriched neonatal experience, synaptic refinement and induction of epigenetic processes within specific neurons. They uncover a novel mechanistic pathway from neonatal environment to emotional resilience.

Smart Grid Communications System Blueprint

NASA Astrophysics Data System (ADS)

Clark, Adrian; Pavlovski, Chris

2010-10-01

Telecommunications operators are well versed in deploying 2G and 3G wireless networks. These networks presently support the mobile business user and/or retail consumer wishing to place conventional voice calls and data connections. The electrical power industry has recently commenced transformation of its distribution networks by deploying smart monitoring and control devices throughout their networks. This evolution of the network into a `smart grid' has also motivated the need to deploy wireless technologies that bridge the communication gap between the smart devices and information technology systems. The requirements of these networks differ from traditional wireless networks that communications operators have deployed, which have thus far forced energy companies to consider deploying their own wireless networks. We present our experience in deploying wireless networks to support the smart grid and highlight the key properties of these networks. These characteristics include application awareness, support for large numbers of simultaneous cell connections, high service coverage and prioritized routing of data. We also outline our target blueprint architecture that may be useful to the industry in building wireless and fixed networks to support the smart grid. By observing our experiences, telecommunications operators and equipment manufacturers will be able to augment their current networks and products in a way that accommodates the needs of the emerging industry of smart grids and intelligent electrical networks.

Loss surface of XOR artificial neural networks

NASA Astrophysics Data System (ADS)

Mehta, Dhagash; Zhao, Xiaojun; Bernal, Edgar A.; Wales, David J.

2018-05-01

Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with Nh hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with Nh+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression.

Accurate critical pressures for structural phase transitions of group IV, III-V, and II-VI compounds from the SCAN density functional

NASA Astrophysics Data System (ADS)

Shahi, Chandra; Sun, Jianwei; Perdew, John P.

2018-03-01

Most of the group IV, III-V, and II-VI compounds crystallize in semiconductor structures under ambient conditions. Upon application of pressure, they undergo structural phase transitions to more closely packed structures, sometimes metallic phases. We have performed density functional calculations using projector augmented wave (PAW) pseudopotentials to determine the transition pressures for these transitions within the local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), and the strongly constrained and appropriately normed (SCAN) meta-GGA. LDA underestimates the transition pressure for most of the studied materials. PBE under- or overestimates in many cases. SCAN typically corrects the errors of LDA and PBE for the transition pressure. The accuracy of SCAN is comparable to that of computationally expensive methods like the hybrid functional HSE06, the random phase approximation (RPA), and quantum Monte Carlo (QMC), in cases where calculations with these methods have been reported, but at a more modest computational cost. The improvement from LDA to PBE to SCAN is especially clearcut and dramatic for covalent semiconductor-metal transitions, as for Si and Ge, where it reflects the increasing relative stabilization of the covalent semiconducting phases under increasing functional sophistication.

Phase transition in NK-Kauffman networks and its correction for Boolean irreducibility

NASA Astrophysics Data System (ADS)

Zertuche, Federico

2014-05-01

In a series of articles published in 1986, Derrida and his colleagues studied two mean field treatments (the quenched and the annealed) for NK-Kauffman networks. Their main results lead to a phase transition curve Kc 2 pc(1-pc)=1 (0

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

PubMed

Lee, Hyunwoo; Whang, Mincheol

2018-05-01

Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS) technology. Seismocardiography (SCG) has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs) can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females) using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions), three body postures (i.e., sitting, standing, and supine), and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h). The motion data (i.e., six-axis accelerometer and gyroscope) and heart rate (i.e., electrocardiogram (ECG)) were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main contributions are as follows: (1) the CNN model enhanced heart rate estimation with the benefits of automatic feature extraction from the data; (2) the proposed method was compared with the previous SCG method using signal processing; (3) the method was tested in 12 measurement conditions related to daily motion for a more practical application.

Traffic Dimensioning and Performance Modeling of 4G LTE Networks

ERIC Educational Resources Information Center

Ouyang, Ye

2011-01-01

Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

Augmenting Phase Space Quantization to Introduce Additional Physical Effects

NASA Astrophysics Data System (ADS)

Robbins, Matthew P. G.

Quantum mechanics can be done using classical phase space functions and a star product. The state of the system is described by a quasi-probability distribution. A classical system can be quantized in phase space in different ways with different quasi-probability distributions and star products. A transition differential operator relates different phase space quantizations. The objective of this thesis is to introduce additional physical effects into the process of quantization by using the transition operator. As prototypical examples, we first look at the coarse-graining of the Wigner function and the damped simple harmonic oscillator. By generalizing the transition operator and star product to also be functions of the position and momentum, we show that additional physical features beyond damping and coarse-graining can be introduced into a quantum system, including the generalized uncertainty principle of quantum gravity phenomenology, driving forces, and decoherence.

First year nursing students' experiences of social media during the transition to university: a focus group study.

PubMed

Ferguson, Caleb; DiGiacomo, Michelle; Saliba, Bernard; Green, Janet; Moorley, Calvin; Wyllie, Aileen; Jackson, Debra

2016-10-01

Social media platforms are useful for creating communities, which can then be utilised as a mean for supportive, professional and social learning. To explore first year nursing student experiences with social media in supporting student transition and engagement into higher education. Qualitative focus groups. Ten 1st year Bachelor of Nursing students were included in three face-to-face focus groups. Data were analysed using qualitative thematic content analysis. Three key themes emerged that illustrates the experiences of transition and engagement of first year student nurses using social media at university. (1) Facilitating familiarity and collaboration at a safe distance, (2) promoting independent learning by facilitating access to resources, and (3) mitigating hazards of social media. This study has demonstrated the importance of social media in supporting informal peer-peer learning and support, augmenting online and offline relationships, and building professional identity as a nurse.

Enabling Tussle-Agile Inter-networking Architectures by Underlay Virtualisation

NASA Astrophysics Data System (ADS)

Dianati, Mehrdad; Tafazolli, Rahim; Moessner, Klaus

In this paper, we propose an underlay inter-network virtualisation framework in order to enable tussle-agile flexible networking over the existing inter-network infrastructures. The functionalities that inter-networking elements (transit nodes, access networks, etc.) need to support in order to enable virtualisation are discussed. We propose the base architectures of each the abstract elements to support the required inter-network virtualisation functionalities.

An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process

ERIC Educational Resources Information Center

Buzzetto-More, Nicole A.

2014-01-01

Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student…

From Apprentice to Journeyman to Partner: Benjamin Franklin's Workers and the Growth of the Early-American Printing Trade.

ERIC Educational Resources Information Center

Frasca, Ralph

In studying the history of the American press, little attention has been given to printing networks and the apprenticeship system, factors which permitted the press not only to survive but to grow. Essential to press growth was the apprenticeship system, vocational education which replenished and augmented the craft's practitioners. Apprentices…

Compensation of significant parametric uncertainties using sliding mode online learning

NASA Astrophysics Data System (ADS)

Schnetter, Philipp; Kruger, Thomas

An augmented nonlinear inverse dynamics (NID) flight control strategy using sliding mode online learning for a small unmanned aircraft system (UAS) is presented. Because parameter identification for this class of aircraft often is not valid throughout the complete flight envelope, aerodynamic parameters used for model based control strategies may show significant deviations. For the concept of feedback linearization this leads to inversion errors that in combination with the distinctive susceptibility of small UAS towards atmospheric turbulence pose a demanding control task for these systems. In this work an adaptive flight control strategy using feedforward neural networks for counteracting such nonlinear effects is augmented with the concept of sliding mode control (SMC). SMC-learning is derived from variable structure theory. It considers a neural network and its training as a control problem. It is shown that by the dynamic calculation of the learning rates, stability can be guaranteed and thus increase the robustness against external disturbances and system failures. With the resulting higher speed of convergence a wide range of simultaneously occurring disturbances can be compensated. The SMC-based flight controller is tested and compared to the standard gradient descent (GD) backpropagation algorithm under the influence of significant model uncertainties and system failures.

Measurements by a Vector Network Analyzer at 325 to 508 GHz

NASA Technical Reports Server (NTRS)

Fung, King Man; Samoska, Lorene; Chattopadhyay, Goutam; Gaier, Todd; Kangaslahti, Pekka; Pukala, David; Lau, Yuenie; Oleson, Charles; Denning, Anthony

2008-01-01

Recent experiments were performed in which return loss and insertion loss of waveguide test assemblies in the frequency range from 325 to 508 GHz were measured by use of a swept-frequency two-port vector network analyzer (VNA) test set. The experiments were part of a continuing effort to develop means of characterizing passive and active electronic components and systems operating at ever increasing frequencies. The waveguide test assemblies comprised WR-2.2 end sections collinear with WR-3.3 middle sections. The test set, assembled from commercially available components, included a 50-GHz VNA scattering- parameter test set and external signal synthesizers, augmented with recently developed frequency extenders, and further augmented with attenuators and amplifiers as needed to adjust radiofrequency and intermediate-frequency power levels between the aforementioned components. The tests included line-reflect-line calibration procedures, using WR-2.2 waveguide shims as the "line" standards and waveguide flange short circuits as the "reflect" standards. Calibrated dynamic ranges somewhat greater than about 20 dB for return loss and 35 dB for insertion loss were achieved. The measurement data of the test assemblies were found to substantially agree with results of computational simulations.

A comprehensive assessment of ionospheric gradients observed in Ecuador during 2013 and 2014 for ground based augmentation systems

NASA Astrophysics Data System (ADS)

Sánchez-Naranjo, S.; Rincón, W.; Ramos-Pollán, R.; González, F. A.; Soley, S.

2017-04-01

Ground Based Augmentation Systems GBAS provide differential corrections to approaching and landing aircrafts in the vicinities of an airport. The ionosphere can introduce an error not accountable by those differential corrections, and a threat model for the Conterminous United States region CONUS was developed in order to consider the highest gradients measured. This study presents the first extensive analysis of ionospheric gradients for Ecuador, from data fully covering 2013 and 2014 collected by their national Global Navigation Satellite System GNSS monitoring network (REGME). In this work it is applied an automated methodology adapted for low latitudes for processing data from dual frequency receivers networks, by considering data from all available days in the date range of the study regardless the geomagnetic indices values. The events found above the CONUS threat model occurred during days of nominal geomagnetic indices, confirming: (1) the higher bounds required for an ionospheric threat model for Ecuador, and (2) that geomagnetic indices are not enough to indicate relevant ionospheric anomalies in low latitude regions, reinforcing the necessity of a continuous monitoring of ionosphere. As additional contribution, the events database is published online, making it available to other researchers.

Securing While Sampling in Wireless Body Area Networks With Application to Electrocardiography.

PubMed

Dautov, Ruslan; Tsouri, Gill R

2016-01-01

Stringent resource constraints and broadcast transmission in wireless body area network raise serious security concerns when employed in biomedical applications. Protecting data transmission where any minor alteration is potentially harmful is of significant importance in healthcare. Traditional security methods based on public or private key infrastructure require considerable memory and computational resources, and present an implementation obstacle in compact sensor nodes. This paper proposes a lightweight encryption framework augmenting compressed sensing with wireless physical layer security. Augmenting compressed sensing to secure information is based on the use of the measurement matrix as an encryption key, and allows for incorporating security in addition to compression at the time of sampling an analog signal. The proposed approach eliminates the need for a separate encryption algorithm, as well as the predeployment of a key thereby conserving sensor node's limited resources. The proposed framework is evaluated using analysis, simulation, and experimentation applied to a wireless electrocardiogram setup consisting of a sensor node, an access point, and an eavesdropper performing a proximity attack. Results show that legitimate communication is reliable and secure given that the eavesdropper is located at a reasonable distance from the sensor node and the access point.

Understanding transit ridership demand for a multi-destination, multimodal transit network in an American metropolitan area : lessons for increasing choice ridership while maintaining transit dependent ridership : [research brief].

DOT National Transportation Integrated Search

2012-01-01

Recent research indicates that multi-destination transit systems are far more effective in attracting passengers than central business district (CBD)-focused systems. However, the same research suggests that multi-destination systems appeal largely t...

Networking standards

NASA Technical Reports Server (NTRS)

Davies, Mark

1991-01-01

The enterprise network is currently a multivendor environment consisting of many defacto and proprietary standards. During the 1990s, these networks will evolve towards networks which are based on international standards in both Local Area Network (LAN) and Wide Area Network (WAN) space. Also, you can expect to see the higher level functions and applications begin the same transition. Additional information is given in viewgraph form.

An Evaluation and Demonstration of a Network Based Aircraft Telemetry System

NASA Technical Reports Server (NTRS)

Waldersen, Matt; Schnarr, Otto, III

2017-01-01

The primary topics of this presentation describe the testing of network based telemetry and RF modulation techniques. The overall intend is to aid the aerospace industry in transitioning to a network based telemetry system.

Advanced helmet mounted display (AHMD)

NASA Astrophysics Data System (ADS)

Sisodia, Ashok; Bayer, Michael; Townley-Smith, Paul; Nash, Brian; Little, Jay; Cassarly, William; Gupta, Anurag

2007-04-01

Due to significantly increased U.S. military involvement in deterrent, observer, security, peacekeeping and combat roles around the world, the military expects significant future growth in the demand for deployable virtual reality trainers with networked simulation capability of the battle space visualization process. The use of HMD technology in simulated virtual environments has been initiated by the demand for more effective training tools. The AHMD overlays computer-generated data (symbology, synthetic imagery, enhanced imagery) augmented with actual and simulated visible environment. The AHMD can be used to support deployable reconfigurable training solutions as well as traditional simulation requirements, UAV augmented reality, air traffic control and Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications. This paper will describe the design improvements implemented for production of the AHMD System.

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

NASA Astrophysics Data System (ADS)

Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

2018-04-01

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

Discharge Identity of Medullary Inspiratory Neurons is Altered during Repetitive Fictive Cough

PubMed Central

Segers, L. S.; Nuding, S. C.; Vovk, A.; Pitts, T.; Baekey, D. M.; O’Connor, R.; Morris, K. F.; Lindsey, B. G.; Shannon, R.; Bolser, Donald C.

2012-01-01

This study investigated the stability of the discharge identity of inspiratory decrementing (I-Dec) and augmenting (I-Aug) neurons in the caudal (cVRC) and rostral (rVRC) ventral respiratory column during repetitive fictive cough in the cat. Inspiratory neurons in the cVRC (n = 23) and rVRC (n = 17) were recorded with microelectrodes. Fictive cough was elicited by mechanical stimulation of the intrathoracic trachea. Approximately 43% (10 of 23) of I-Dec neurons shifted to an augmenting discharge pattern during the first cough cycle (C1). By the second cough cycle (C2), half of these returned to a decrementing pattern. Approximately 94% (16 of 17) of I-Aug neurons retained an augmenting pattern during C1 of a multi-cough response episode. Phrenic burst amplitude and inspiratory duration increased during C1, but decreased with each subsequent cough in a series of repetitive coughs. As a step in evaluating the model-driven hypothesis that VRC I-Dec neurons contribute to the augmentation of inspiratory drive during cough via inhibition of VRC tonic expiratory neurons that inhibit premotor inspiratory neurons, cross-correlation analysis was used to assess relationships of tonic expiratory cells with simultaneously recorded inspiratory neurons. Our results suggest that reconfiguration of inspiratory-related sub-networks of the respiratory pattern generator occurs on a cycle-by-cycle basis during repetitive coughing. PMID:22754536

Human Factors Report on Information Management Requirements for Next- Generation Manned Bombers

DTIC Science & Technology

1987-12-01

34 James , W. G. (1984). Al applications to military pilot decision aiding -- A perspective • transition. In Third Aerospace Behavioral Engineering Techno.ogy...8217- - . . . Basden , A. (1983). On the application of expert systems. International Journal of Man-Machine Studies, 19, 461-477. Ben-Bassat, M. and Freedy, A...augmentation system design by defining, developing, and applying appropriate design techniques for a variety of airborne platforms. James , W. G

An animated landscape representation of CD4+ T-cell differentiation, variability, and plasticity: Insights into the behavior of populations versus cells

PubMed Central

Rebhahn, Jonathan A; Deng, Nan; Sharma, Gaurav; Livingstone, Alexandra M; Huang, Sui; Mosmann, Tim R

2014-01-01

Recent advances in understanding CD4+ T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program “LAVA” visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states. PMID:24945794

Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

PubMed

Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

2016-11-28

The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model

PubMed Central

Papasavvas, Christoforos A.; Wang, Yujiang; Trevelyan, Andrew J.; Kaiser, Marcus

2016-01-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics. PMID:26465514

Generalized epidemic process on modular networks.

PubMed

Chung, Kihong; Baek, Yongjoo; Kim, Daniel; Ha, Meesoon; Jeong, Hawoong

2014-05-01

Social reinforcement and modular structure are two salient features observed in the spreading of behavior through social contacts. In order to investigate the interplay between these two features, we study the generalized epidemic process on modular networks with equal-sized finite communities and adjustable modularity. Using the analytical approach originally applied to clique-based random networks, we show that the system exhibits a bond-percolation type continuous phase transition for weak social reinforcement, whereas a discontinuous phase transition occurs for sufficiently strong social reinforcement. Our findings are numerically verified using the finite-size scaling analysis and the crossings of the bimodality coefficient.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Spiral Wave in Small-World Networks of Hodgkin-Huxley Neurons

NASA Astrophysics Data System (ADS)

Ma, Jun; Yang, Li-Jian; Wu, Ying; Zhang, Cai-Rong

2010-09-01

The effect of small-world connection and noise on the formation and transition of spiral wave in the networks of Hodgkin-Huxley neurons are investigated in detail. Some interesting results are found in our numerical studies. i) The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii) A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise. iii) Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv) Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Gaussian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave.

The Hetu'u Global Network: Measuring the Distance to the Sun Using the June 5th/6th Transit of Venus

ERIC Educational Resources Information Center

Faherty, Jacqueline K.; Rodriguez, David R.; Miller, Scott T.

2012-01-01

In the spirit of historic astronomical endeavors, we invited school groups across the globe to collaborate in a solar distance measurement using the rare June 5/6th transit of Venus. In total, we recruited 19 school groups spread over 6 continents and 10 countries to participate in our Hetu'u Global Network. Applying the methods of French…

a Numerical Investigation of the Jamming Transition in Traffic Flow on Diluted Planar Networks

NASA Astrophysics Data System (ADS)

Achler, Gabriele; Barra, Adriano

In order to develop a toy model for car's traffic in cities, in this paper we analyze, by means of numerical simulations, the transition among fluid regimes and a congested jammed phase of the flow of kinetically constrained hard spheres in planar random networks similar to urban roads. In order to explore as timescales as possible, at a microscopic level we implement an event driven dynamics as the infinite time limit of a class of already existing model (Follow the Leader) on an Erdos-Renyi two-dimensional graph, the crossroads being accounted by standard Kirchoff density conservations. We define a dynamical order parameter as the ratio among the moving spheres versus the total number and by varying two control parameters (density of the spheres and coordination number of the network) we study the phase transition. At a mesoscopic level it respects an, again suitable, adapted version of the Lighthill-Whitham model, which belongs to the fluid-dynamical approach to the problem. At a macroscopic level, the model seems to display a continuous transition from a fluid phase to a jammed phase when varying the density of the spheres (the amount of cars in a city-like scenario) and a discontinuous jump when varying the connectivity of the underlying network.

Simulation of Z(3) walls and string production via bubble nucleation in a quark-hadron transition

NASA Astrophysics Data System (ADS)

Gupta, Uma Shankar; Mohapatra, Ranjita K.; Srivastava, Ajit M.; Tiwari, Vivek K.

2010-10-01

We study the dynamics of confinement-deconfinement phase transition in the context of relativistic heavy-ion collisions within the framework of effective models for the Polyakov loop order parameter. We study the formation of Z(3) walls and associated strings in the initial transition from the confining (hadronic) phase to the deconfining [quark-gluon plasma (QGP)] phase via the so-called Kibble mechanism. Essential physics of the Kibble mechanism is contained in a sort of domain structure arising after any phase transition which represents random variation of the order parameter at distances beyond the typical correlation length. We implement this domain structure by using the Polyakov loop effective model with a first order phase transition and confine ourselves with temperature/time ranges so that the first order confinement-deconfinement transition proceeds via bubble nucleation, leading to a well defined domain structure. The formation of Z(3) walls and associated strings results from the coalescence of QGP bubbles expanding in the confining background. We investigate the evolution of the Z(3) wall and string network. We also calculate the energy density fluctuations associated with Z(3) wall network and strings which decay away after the temperature drops below the quark-hadron transition temperature during the expansion of QGP. We discuss evolution of these quantities with changing temperature via Bjorken’s hydrodynamical model and discuss possible experimental signatures resulting from the presence of Z(3) wall network and associate strings.

Simulation of Z(3) walls and string production via bubble nucleation in a quark-hadron transition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gupta, Uma Shankar; Tiwari, Vivek K.; Mohapatra, Ranjita K.

2010-10-01

We study the dynamics of confinement-deconfinement phase transition in the context of relativistic heavy-ion collisions within the framework of effective models for the Polyakov loop order parameter. We study the formation of Z(3) walls and associated strings in the initial transition from the confining (hadronic) phase to the deconfining [quark-gluon plasma (QGP)] phase via the so-called Kibble mechanism. Essential physics of the Kibble mechanism is contained in a sort of domain structure arising after any phase transition which represents random variation of the order parameter at distances beyond the typical correlation length. We implement this domain structure by using themore » Polyakov loop effective model with a first order phase transition and confine ourselves with temperature/time ranges so that the first order confinement-deconfinement transition proceeds via bubble nucleation, leading to a well defined domain structure. The formation of Z(3) walls and associated strings results from the coalescence of QGP bubbles expanding in the confining background. We investigate the evolution of the Z(3) wall and string network. We also calculate the energy density fluctuations associated with Z(3) wall network and strings which decay away after the temperature drops below the quark-hadron transition temperature during the expansion of QGP. We discuss evolution of these quantities with changing temperature via Bjorken's hydrodynamical model and discuss possible experimental signatures resulting from the presence of Z(3) wall network and associate strings.« less

Electrohydrodynamic convective heat transfer in a square duct.

PubMed

Grassi, Walter; Testi, Daniele

2009-04-01

Laminar to weakly turbulent forced convection in a square duct heated from the bottom is strengthened by ion injection from an array of high-voltage points opposite the heated strip. Both positive and negative ion injection are activated within the working liquid HFE-7100 (C(4)F(9)OCH(3)), with transiting electrical currents on the order of 0.1 mA. Local temperatures on the heated wall are measured by liquid crystal thermography. The tests are conducted in a Reynolds number range from 510 to 12,100. In any case, heat transfer is dramatically augmented, almost independently from the flow rate. The pressure drop increase caused by the electrohydrodynamically induced flow is also measured. A profitable implementation of the technique in the design of heat sinks and heat exchangers is foreseen; possible benefits are pumping power reduction, size reduction, and heat exchange capability augmentation.

Optical wireless communication in data centers

NASA Astrophysics Data System (ADS)

Arnon, Shlomi

2018-01-01

In the last decade data centers have become a crucial element in modern human society. However, to keep pace with internet data rate growth, new technologies supporting data center should develop. Integration of optical wireless communication (OWC) in data centers is one of the proposed technologies as augmented technology to the fiber network. One implementation of the OWC technology is deployment of optical wireless transceiver on top of the existing cable/fiber network as extension to the top of rack (TOR) switch; in this way, a dynamic and flexible network is created. Optical wireless communication could reduce energy consumption, increase the data rate, reduce the communication latency, increase flexibility and scalability, and reduce maintenance time and cost, in comparison to extra fiber network deployment. In this paper we review up to date literature in the field, propose an implementation scheme of OWC network, discuss ways to reduce energy consumption by parallel link communication and report preliminary measurement result of university data center environment.

Feasibility of a Friendship Network-Based Pediatric Obesity Intervention.

PubMed

Giannini, Courtney M; Irby, Megan B; Skelton, Joseph A; Gesell, Sabina B

2017-02-01

There is growing evidence supporting social network-based interventions for adolescents with obesity. This study's aim was to determine the feasibility of a social network-based intervention by assessing adolescents' friendship networks, willingness to involve friends in treatment, and how these factors influence enjoyment. Adolescents (N = 42) were recruited from a tertiary care obesity clinic. Participants gave a list of closest friends, friendship characteristics, and which of their friends they would involve in treatment. A subset (N = 14) participated in group treatment, were encouraged to bring friends, and invited to a second interview. Participants nominated a mean of 4.0 (standard deviation [SD] = 1.6) friends and were more likely to nominate closer friends (p = 0.003). Friends who attended group sessions were more likely to have multiple friendships in common with the participant's own network (p = 0.04). Involving friends in treatment is feasible and desired by adolescents and may be a novel approach for augmenting obesity treatment outcomes.

Parvalbumin-expressing interneurons coordinate hippocampal network dynamics required for memory consolidation

NASA Astrophysics Data System (ADS)

Ognjanovski, Nicolette; Schaeffer, Samantha; Wu, Jiaxing; Mofakham, Sima; Maruyama, Daniel; Zochowski, Michal; Aton, Sara J.

2017-04-01

Activity in hippocampal area CA1 is essential for consolidating episodic memories, but it is unclear how CA1 activity patterns drive memory formation. We find that in the hours following single-trial contextual fear conditioning (CFC), fast-spiking interneurons (which typically express parvalbumin (PV)) show greater firing coherence with CA1 network oscillations. Post-CFC inhibition of PV+ interneurons blocks fear memory consolidation. This effect is associated with loss of two network changes associated with normal consolidation: (1) augmented sleep-associated delta (0.5-4 Hz), theta (4-12 Hz) and ripple (150-250 Hz) oscillations; and (2) stabilization of CA1 neurons' functional connectivity patterns. Rhythmic activation of PV+ interneurons increases CA1 network coherence and leads to a sustained increase in the strength and stability of functional connections between neurons. Our results suggest that immediately following learning, PV+ interneurons drive CA1 oscillations and reactivation of CA1 ensembles, which directly promotes network plasticity and long-term memory formation.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

Fireballs in the Sky: an Augmented Reality Citizen Science Program

NASA Astrophysics Data System (ADS)

Day, B. H.; Bland, P.; Sayers, R.

2017-12-01

Fireballs in the Sky is an innovative Australian citizen science program that connects the public with the research of the Desert Fireball Network (DFN). This research aims to understand the early workings of the solar system, and Fireballs in the Sky invites people around the world to learn about this science, contributing fireball sightings via a user-friendly augmented reality mobile app. Tens of thousands of people have downloaded the app world-wide and participated in the science of meteoritics. The Fireballs in the Sky app allows users to get involved with the Desert Fireball Network research, supplementing DFN observations and providing enhanced coverage by reporting their own meteor sightings to DFN scientists. Fireballs in the Sky reports are used to track the trajectories of meteors - from their orbit in space to where they might have landed on Earth. Led by Phil Bland at Curtin University in Australia, the Desert Fireball Network (DFN) uses automated observatories across Australia to triangulate trajectories of meteorites entering the atmosphere, determine pre-entry orbits, and pinpoint their fall positions. Each observatory is an autonomous intelligent imaging system, taking 1000×36Megapixel all-sky images throughout the night, using neural network algorithms to recognize events. They are capable of operating for 12 months in a harsh environment, and store all imagery collected. We developed a completely automated software pipeline for data reduction, and built a supercomputer database for storage, allowing us to process our entire archive. The DFN currently stands at 50 stations distributed across the Australian continent, covering an area of 2.5 million km^2. Working with DFN's partners at NASA's Solar System Exploration Research Virtual Institute, the team is expanding the network beyond Australia to locations around the world. Fireballs in the Sky allows a growing public base to learn about and participate in this exciting research.

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis.

PubMed

Pitkänen, Esa; Akerlund, Arto; Rantanen, Ari; Jouhten, Paula; Ukkonen, Esko

2008-08-25

ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate (13)C metabolic flux analysis. The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in (13)C metabolic flux analysis, where the mappings of carbon atoms from substrates into products in the model are required. ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool. The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Fireballs in the Sky: An Augmented Reality Citizen Science Program

NASA Technical Reports Server (NTRS)

Day, Brian

2017-01-01

Fireballs in the Sky is an innovative Australian citizen science program that connects the public with the research of the Desert Fireball Network (DFN). This research aims to understand the early workings of the solar system, and Fireballs in the Sky invites people around the world to learn about this science, contributing fireball sightings via a user-friendly augmented reality mobile app. Tens of thousands of people have downloaded the app world-wide and participated in the science of meteoritics. The Fireballs in the Sky app allows users to get involved with the Desert Fireball Network research, supplementing DFN observations and providing enhanced coverage by reporting their own meteor sightings to DFN scientists. Fireballs in the Sky reports are used to track the trajectories of meteors - from their orbit in space to where they might have landed on Earth. Led by Phil Bland at Curtin University in Australia, the Desert Fireball Network (DFN) uses automated observatories across Australia to triangulate trajectories of meteorites entering the atmosphere, determine pre-entry orbits, and pinpoint their fall positions. Each observatory is an autonomous intelligent imaging system, taking 1000 by 36 megapixel all-sky images throughout the night, using neural network algorithms to recognize events. They are capable of operating for 12 months in a harsh environment, and store all imagery collected. We developed a completely automated software pipeline for data reduction, and built a supercomputer database for storage, allowing us to process our entire archive. The DFN currently stands at 50 stations distributed across the Australian continent, covering an area of 2.5 million square kilometers. Working with DFN's partners at NASA's Solar System Exploration Research Virtual Institute, the team is expanding the network beyond Australia to locations around the world. Fireballs in the Sky allows a growing public base to learn about and participate in this exciting research.

Evolutionary transitions in controls reconcile adaptation with continuity of evolution.

PubMed

Badyaev, Alexander V

2018-05-19

Evolution proceeds by accumulating functional solutions, necessarily forming an uninterrupted lineage from past solutions of ancestors to the current design of extant forms. At the population level, this process requires an organismal architecture in which the maintenance of local adaptation does not preclude the ability to innovate in the same traits and their continuous evolution. Representing complex traits as networks enables us to visualize a fundamental principle that resolves tension between adaptation and continuous evolution: phenotypic states encompassing adaptations traverse the continuous multi-layered landscape of past physical, developmental and functional associations among traits. The key concept that captures such traversing is network controllability - the ability to move a network from one state into another while maintaining its functionality (reflecting evolvability) and to efficiently propagate information or products through the network within a phenotypic state (maintaining its robustness). Here I suggest that transitions in network controllability - specifically in the topology of controls - help to explain how robustness and evolvability are balanced during evolution. I will focus on evolutionary transitions in degeneracy of metabolic networks - a ubiquitous property of phenotypic robustness where distinct pathways achieve the same end product - to suggest that associated changes in network controls is a common rule underlying phenomena as distinct as phenotypic plasticity, organismal accommodation of novelties, genetic assimilation, and macroevolutionary diversification. Capitalizing on well understood principles by which network structure translates into function of control nodes, I show that accumulating redundancy in one type of network controls inevitably leads to the emergence of another type of controls, forming evolutionary cycles of network controllability that, ultimately, reconcile local adaptation with continuity of evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

A neural network approach for the blind deconvolution of turbulent flows

NASA Astrophysics Data System (ADS)

Maulik, R.; San, O.

2017-11-01

We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a-priori testing of both two-dimensional Kraichnan and three-dimensional Kolmogorov turbulence and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier-Stokes equations.

OLED microdisplays in near-to-eye applications: challenges and solutions

NASA Astrophysics Data System (ADS)

Vogel, Uwe; Richter, Bernd; Wartenberg, Philipp; König, Peter; Hild, Olaf R.; Fehse, Karsten; Schober, Matthias; Bodenstein, Elisabeth; Beyer, Beatrice

2017-06-01

Wearable augmented-reality (AR) has already started to be used productively mainly in manufacturing industry and logistics. Next step will be to move wearable AR from "professionals to citizens" by enabling networked, everywhere augmented-reality (in-/outdoor localisation, scene recognition, cloud access,…) which is non-intrusive, exhibits intuitive user-interaction, anytime safe and secure use, and considers personal privacy protection (user's and others). Various hardware improvements (e.g., low-power, seamless interactivity, small form factor, ergonomics,…), as well as connectivity and network integration will become vital for consumer adoption. Smart-Glasses (i.e., near-to-eye (NTE) displays) have evolved as major devices for wearable AR, that hold potential to become adopted by consumers soon. Tiny microdisplays are a key component of smart-glasses, e.g., creating images from organic light emitting diodes (OLED), that have become popular in mobile phone displays. All microdisplay technologies on the market comprise an image-creating pixel modulation, but only the emissive ones (for example, OLED and LED) feature the image and light source in a single device, and therefore do not require an external light source. This minimizes system size and power consumption, while providing exceptional contrast and color space. These advantages make OLED microdisplays a perfect fit for near-eye applications. Low-power active-matrix circuitry CMOS backplane architecture, embedded sensors, emission spectra outside the visible and high-resolution sub-pixel micro-patterning address some of the application challenges (e.g., long battery life, sun-light readability, user interaction modes) and enable advanced features for OLED microdisplays in near-to-eye displays, e.g., upcoming connected augmented-reality smart glasses. This report is to analyze the challenges in addressing those features and discuss solutions.

Complex networks: Effect of subtle changes in nature of randomness

NASA Astrophysics Data System (ADS)

Goswami, Sanchari; Biswas, Soham; Sen, Parongama

2011-03-01

In two different classes of network models, namely, the Watts Strogatz type and the Euclidean type, subtle changes have been introduced in the randomness. In the Watts Strogatz type network, rewiring has been done in different ways and although the qualitative results remain the same, finite differences in the exponents are observed. In the Euclidean type networks, where at least one finite phase transition occurs, two models differing in a similar way have been considered. The results show a possible shift in one of the phase transition points but no change in the values of the exponents. The WS and Euclidean type models are equivalent for extreme values of the parameters; we compare their behaviour for intermediate values.

Limited ability driven phase transitions in the coevolution process in Axelrod's model

NASA Astrophysics Data System (ADS)

Wang, Bing; Han, Yuexing; Chen, Luonan; Aihara, Kazuyuki

2009-04-01

We study the coevolution process in Axelrod's model by taking into account of agents' abilities to access information, which is described by a parameter α to control the geographical range of communication. We observe two kinds of phase transitions in both cultural domains and network fragments, which depend on the parameter α. By simulation, we find that not all rewiring processes pervade the dissemination of culture, that is, a very limited ability to access information constrains the cultural dissemination, while an exceptional ability to access information aids the dissemination of culture. Furthermore, by analyzing the network characteristics at the frozen states, we find that there exists a stage at which the network develops to be a small-world network with community structures.

Increasing cellular coverage within integrated terrestrial/satellite mobile networks

NASA Technical Reports Server (NTRS)

Castro, Jonathan P.

1995-01-01

When applying the hierarchical cellular concept, the satellite acts as giant umbrella cell covering a region with some terrestrial cells. If a mobile terminal traversing the region arrives to the border-line or limits of a regular cellular ground service, network transition occurs and the satellite system continues the mobile coverage. To adequately assess the boundaries of service of a mobile satellite system an a cellular network within an integrated environment, this paper provides an optimized scheme to predict when a network transition may be necessary. Under the assumption of a classified propagation phenomenon and Lognormal shadowing, the study applies an analytical approach to estimate the location of a mobile terminal based on a reception of the signal strength emitted by a base station.

An internet-based communication network for information transfer during patient transitions from skilled nursing facility to the emergency department.

PubMed

Hustey, Fredric M; Palmer, Robert M

2010-06-01

To determine whether the implementation of an Internet-based communication system improves the amount of essential information conveyed between a skilled nursing facility (SNF) and the emergency department (ED) during patient care transitions. Interventional; before and after. ED of an urban teaching hospital with approximately 55,000 visits per year and a 55-bed subacute free-standing rehabilitation facility (the SNF). All patients transferred from the SNF to the ED over 16 months. An Internet-based communication network with SNF-ED transfer form for communication during patient care transitions. Nine elements of patient information assessed before and after intervention through chart review. changes in efficiency of information transfer and staff satisfaction. Two hundred thirty-four of 237 preintervention and all 276 postintervention care transitions were reviewed. The Internet communication network was used in 78 (26%) of all care transitions, peaking at 40% by the end of the study. There was more critical patient information (1.85 vs 4.29 of 9 elements; P<.001) contained within fewer pages of transfer documents (24.47 vs 5.15; P<.001) after the intervention. Staff satisfaction with communication was higher among ED physicians after the intervention. The use of an Internet-based system increased the amount of information communicated during SNF-ED care transitions and significantly reduced the number of pages in which this information was contained.

Fundamental ingredients for discontinuous phase transitions in the inertial majority vote model.

PubMed

Encinas, Jesus M; Harunari, Pedro E; de Oliveira, M M; Fiore, Carlos E

2018-06-19

Discontinuous transitions have received considerable interest due to the uncovering that many phenomena such as catastrophic changes, epidemic outbreaks and synchronization present a behavior signed by abrupt (macroscopic) changes (instead of smooth ones) as a tuning parameter is changed. However, in different cases there are still scarce microscopic models reproducing such above trademarks. With these ideas in mind, we investigate the key ingredients underpinning the discontinuous transition in one of the simplest systems with up-down Z 2 symmetry recently ascertained in [Phys. Rev. E 95, 042304 (2017)]. Such system, in the presence of an extra ingredient-the inertia- has its continuous transition being switched to a discontinuous one in complex networks. We scrutinize the role of three central ingredients: inertia, system degree, and the lattice topology. Our analysis has been carried out for regular lattices and random regular networks with different node degrees (interacting neighborhood) through mean-field theory (MFT) treatment and numerical simulations. Our findings reveal that not only the inertia but also the connectivity constitute essential elements for shifting the phase transition. Astoundingly, they also manifest in low-dimensional regular topologies, exposing a scaling behavior entirely different than those from the complex networks case. Therefore, our findings put on firmer bases the essential issues for the manifestation of discontinuous transitions in such relevant class of systems with Z 2 symmetry.

Defining and characterizing the critical transition state prior to the type 2 diabetes disease

PubMed Central

Zhu, Chunqing; Zhou, Xin; Chen, Pei; Fu, Tianyun; Hu, Zhongkai; Wu, Qian; Liu, Wei; Liu, Daowei; Yu, Yunxian; Zhang, Yan; McElhinney, Doff B.; Li, Yu-Ming; Culver, Devore S; Alfreds, Shaun T.; Stearns, Frank; Sylvester, Karl G.; Widen, Eric

2017-01-01

Background Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis. Method We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort’s EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted. Results Analysis of these patients’ pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state. Conclusions This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM. PMID:28686739

The protonation of N2O reexamined - A case study on the reliability of various electron correlation methods for minima and transition states

NASA Technical Reports Server (NTRS)

Martin, J. M. L.; Lee, Timothy J.

1993-01-01

The protonation of N2O and the intramolecular proton transfer in N2OH(+) are studied using various basis sets and a variety of methods, including second-order many-body perturbation theory (MP2), singles and doubles coupled cluster (CCSD), the augmented coupled cluster (CCSD/T/), and complete active space self-consistent field (CASSCF) methods. For geometries, MP2 leads to serious errors even for HNNO(+); for the transition state, only CCSD/T/ produces a reliable geometry due to serious nondynamical correlation effects. The proton affinity at 298.15 K is estimated at 137.6 kcal/mol, in close agreement with recent experimental determinations of 137.3 +/- 1 kcal/mol.

The Future of the United States Antarctic Program

NASA Astrophysics Data System (ADS)

Thom, J. E.; Weidner, G. A.; Lazzara, M. A.; Knuth, S. L.; Cassano, J. J.

2009-04-01

The last three decades have seen Antarctic surface meteorological observations augmented by an increasing number of automated weather stations (AWS). Since 1980, the University of Wisconsin-Madison has managed an expanding array of AWS in Antarctica that are funded through the United States' National Science Foundation. The AWS network began with six stations and has grown to approximately 60 stations. The majority of the AWS use a custom electronics package designed in the 1970s and modified over approximately 20 years. However, dramatic changes in the electronics industry have led the UW-Madison to transition its AWS to commercial-off-the-shelf (COTS) components capable of integrating on-station storage, varied sensors, multiple data telemetry options, and a flexible operating system. Among the important technical issues arising from adopting a COTS-based AWS system are limited temperature certification for Antarctic conditions; non-standard integration of the varied telecommunications equipment; potentially inflexible data acquisition schemes; and frequent product upgrades, changes, and obsolescence. The UW-Madison presents the current status of its AWS system; its recent experience with new data loggers, sensors, and communication options; and its attempts to obtain a standardized AWS. The intent is to encourage the development of a forum where groups can document their experiences with varied AWS systems in the extreme polar climate. Recent events have added another challenge within the United States Antarctic Program, as it has become clear that budgetary and logistic limitations will drastically impact the AWS program. With logistical costs playing a bigger factor in funding AWS operations, international coordination and cooperation will be important in deploying and maintaining the AWS networks (such as GCOS) that are critical to monitoring the world's climate.

Intelligent device management in the selfcare marketplace.

PubMed

Biniaris, Christos G; Marsh, Andrew J

2008-01-01

Over the last ten years the Internet has emerged as a key infrastructure for service innovation, enabling IP (Internet Protocol) to become the wide area network communication protocol of choice. The natural result of this choice is that service providers and their customers are looking for ways to optimise costs by migrating existing services and applications onto IP as well. A good example is the medical industry, which is transitioning to Internet-based communications as the field of telemedicine broadens to preventative and self healthcare. However, technology is changing quickly and consumers face an array of choices to satisfy their healthcare needs with numerous devices from different vendors. Seamless healthcare device networking can play a major role in automating and safeguarding the process of collecting and transferring medical data, remote patient monitoring and reducing costs through remote equipment monitoring. In this scope, we describe an approach augmenting the Session Initiation Protocol (SIP) with healthcare services in order to form a framework for efficient collection and storage of measurements, aiming to address the issues of the lack of a standardised data interface for consumer healthcare technologies (including hardware and protocols) and the lack of a standardised format for self-collected healthcare data (including the storage medium). In this framework, measurements can be seamlessly collected and stored as XML notes located virtually anywhere, such as the user's home or mobile device. Additionally, these notes can be accessed locally or remotely by doctors and specialists. Also, we discuss how this approach supports user mobility by proxying and redirecting requests to the user's current location and how it can remove the complexity of using consumer healthcare technologies from different vendors connected to different devices and the opportunities for Independent Software Vendors to develop additional services.

Periadolescent exposure to the NMDA antagonist MK-801 impairs the functional maturation of local GABAergic circuits in the adult prefrontal cortex

PubMed Central

Thomases, Daniel R.; Cass, Daryn K.; Tseng, Kuei Y.

2012-01-01

A developmental disruption of prefrontal cortical (PFC) inhibitory circuits is thought to contribute to the adolescent onset of cognitive deficits observed in schizophrenia. However, the developmental mechanisms underlying such a disruption remain elusive. The goal of this study is to examine how repeated exposure to the NMDA receptor antagonist dizocilpine maleate (MK-801) during periadolescence (from postnatal days -PD- 35-40) impacts the normative development of local prefrontal network response in rats. In vivo electrophysiological analyses revealed that MK-801 administration during periadolescence elicits an enduring disinhibited prefrontal local field potential response to ventral hippocampal stimulation at 20Hz (beta) and 40Hz (gamma) in adulthood (PD65-85). Such a disinhibition was not observed when MK-801 was given during adulthood, indicating that the periadolescent transition is indeed a sensitive period for the functional maturation of prefrontal inhibitory control. Accordingly, the pattern of prefrontal local field potential disinhibition induced by periadolescent MK-801 treatment resembles that observed in the normal PD30-40 PFC. Further pharmacological manipulations revealed that these developmentally immature prefrontal responses can be mimicked by single microinfusion of the GABA-A receptor antagonist picrotoxin into the normal adult PFC. Importantly, acute administration of the GABA-A positive allosteric modulator Indiplon into the PFC reversed the prefrontal disinhibitory state induced by periadolescent MK-801 to normal levels. Together, these results indicate a critical role of NMDA receptors in regulating the periadolescent maturation of GABAergic networks in the PFC, and that pharmacologically-induced augmentation of local GABA-A receptor-mediated transmission is sufficient to overcome the disinhibitory prefrontal state associated with the periadolescent MK-801 exposure. PMID:23283319

Experimental study of thin film sensor networks for wind turbine blade damage detection

NASA Astrophysics Data System (ADS)

Downey, A.; Laflamme, S.; Ubertini, F.; Sauder, H.; Sarkar, P.

2017-02-01

Damage detection of wind turbine blades is difficult due to their complex geometry and large size, for which large deployment of sensing systems is typically not economical. A solution is to develop and deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel skin-type strain gauge for measuring strain over very large surfaces. The skin, a type of large-area electronics, is constituted from a network of soft elastomeric capacitors. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a dense network of soft elastomeric capacitors to detect, localize, and quantify damage on wind turbine blades. We also leverage mature off-the-shelf technologies, in particular resistive strain gauges, to augment such dense sensor network with high accuracy data at key locations, therefore constituting a hybrid dense sensor network. The proposed hybrid dense sensor network is installed inside a wind turbine blade model, and tested in a wind tunnel to simulate an operational environment. Results demonstrate the ability of the hybrid dense sensor network to detect, localize, and quantify damage.

Traffic sign recognition based on deep convolutional neural network

NASA Astrophysics Data System (ADS)

Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

2017-11-01

Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

Glass-Glass Transitions by Means of an Acceptor-Donor Percolating Electric-Dipole Network

NASA Astrophysics Data System (ADS)

Zhang, Le; Lou, Xiaojie; Wang, Dong; Zhou, Yan; Yang, Yang; Kuball, Martin; Carpenter, Michael A.; Ren, Xiaobing

2017-11-01

We report the ferroelectric glass-glass transitions in KN (K+/Nb5 +) -doped BaTiO3 ferroelectric ceramics, which have been proved by x-ray diffraction profile and Raman spectra data. The formation of glass-glass transitions can be attributed to the existence of cubic (C )-tetragonal (T )-orthorhombic (O )-rhombohedral (R ) ferroelectric transitions in short-range order. These abnormal glass-glass transitions can perform very small thermal hysteresis (approximately 1.0 K ) with a large dielectric constant (approximately 3000), small remanent polarization Pr , and relative high maximum polarization Pm remaining over a wide temperature range (220-350 K) under an electrical stimulus, indicating the potential applications in dielectric recoverable energy-storage devices with high thermal reliability. Further phase field simulations suggest that these glass-glass transitions are induced by the formation of a percolating electric defect-dipole network (PEDN). This proper PEDN breaks the long-range ordered ferroelectric domain pattern and results in the local phase transitions at the nanoscale. Our work may further stimulate the fundamental physical theory and accelerate the development of dielectric energy-storing devices.

Professionals' and Parents' Shared Learning in Blended Learning Networks Related to Communication and Augmentative and Alternative Communication for People with Severe Disabilities

ERIC Educational Resources Information Center

Wilder, Jenny; Magnusson, Lennart; Hanson, Elizabeth

2015-01-01

People with severe disabilities (SD) communicate in complex ways, and their teachers, parents and other involved professionals find it difficult to gain knowledge and share their experiences regarding the person with SD's communication methods. The purpose of this study is to contribute to our understanding of how parents and professionals share…

Radio Signal Augmentation for Improved Training of a Convolutional Neural Network

DTIC Science & Technology

2016-09-01

official government endorsement or approval of commercial products or services referenced in this report. Bluetooth ® is a registered...trademark of Bluetooth SIG, Inc.. Nuand™ and blade RF™ are trademarks of Nurand, LLC. Released by E. R. Buckland, Head IO Support to National... Bluetooth ® computer mouse, and Bluetooth ® search from a mobile cellular phone. Qualitatively, model Moffset dramatically outperformed model Mclean in

Active 2D materials for on-chip nanophotonics and quantum optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shiue, Ren-Jye; Efetov, Dmitri K.; Grosso, Gabriele

Abstract Two-dimensional materials have emerged as promising candidates to augment existing optical networks for metrology, sensing, and telecommunication, both in the classical and quantum mechanical regimes. Here, we review the development of several on-chip photonic components ranging from electro-optic modulators, photodetectors, bolometers, and light sources that are essential building blocks for a fully integrated nanophotonic and quantum photonic circuit.