Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

NASA Astrophysics Data System (ADS)

Sammouda, Rachid; Niki, Noboru; Nishitani, Hiroshi; Nakamura, S.; Mori, Shinichiro

1997-04-01

The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Multivariate Cryptography Based on Clipped Hopfield Neural Network.

PubMed

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2018-02-01

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

Graph-theoretic quantum system modelling for neuronal microtubules as hierarchical clustered quantum Hopfield networks

NASA Astrophysics Data System (ADS)

Srivastava, D. P.; Sahni, V.; Satsangi, P. S.

2014-08-01

Graph-theoretic quantum system modelling (GTQSM) is facilitated by considering the fundamental unit of quantum computation and information, viz. a quantum bit or qubit as a basic building block. Unit directional vectors "ket 0" and "ket 1" constitute two distinct fundamental quantum across variable orthonormal basis vectors, for the Hilbert space, specifying the direction of propagation of information, or computation data, while complementary fundamental quantum through, or flow rate, variables specify probability parameters, or amplitudes, as surrogates for scalar quantum information measure (von Neumann entropy). This paper applies GTQSM in continuum of protein heterodimer tubulin molecules of self-assembling polymers, viz. microtubules in the brain as a holistic system of interacting components representing hierarchical clustered quantum Hopfield network, hQHN, of networks. The quantum input/output ports of the constituent elemental interaction components, or processes, of tunnelling interactions and Coulombic bidirectional interactions are in cascade and parallel interconnections with each other, while the classical output ports of all elemental components are interconnected in parallel to accumulate micro-energy functions generated in the system as Hamiltonian, or Lyapunov, energy function. The paper presents an insight, otherwise difficult to gain, for the complex system of systems represented by clustered quantum Hopfield network, hQHN, through the application of GTQSM construct.

Phase diagram of restricted Boltzmann machines and generalized Hopfield networks with arbitrary priors.

PubMed

Barra, Adriano; Genovese, Giuseppe; Sollich, Peter; Tantari, Daniele

2018-02-01

Restricted Boltzmann machines are described by the Gibbs measure of a bipartite spin glass, which in turn can be seen as a generalized Hopfield network. This equivalence allows us to characterize the state of these systems in terms of their retrieval capabilities, both at low and high load, of pure states. We study the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as the pattern (i.e., weight) distribution and spin (i.e., unit) priors vary smoothly from Gaussian real variables to Boolean discrete variables. Our analysis shows that the presence of a retrieval phase is robust and not peculiar to the standard Hopfield model with Boolean patterns. The retrieval region becomes larger when the pattern entries and retrieval units get more peaked and, conversely, when the hidden units acquire a broader prior and therefore have a stronger response to high fields. Moreover, at low load retrieval always exists below some critical temperature, for every pattern distribution ranging from the Boolean to the Gaussian case.

Dynamical analysis of continuous higher-order hopfield networks for combinatorial optimization.

PubMed

Atencia, Miguel; Joya, Gonzalo; Sandoval, Francisco

2005-08-01

In this letter, the ability of higher-order Hopfield networks to solve combinatorial optimization problems is assessed by means of a rigorous analysis of their properties. The stability of the continuous network is almost completely clarified: (1) hyperbolic interior equilibria, which are unfeasible, are unstable; (2) the state cannot escape from the unitary hypercube; and (3) a Lyapunov function exists. Numerical methods used to implement the continuous equation on a computer should be designed with the aim of preserving these favorable properties. The case of nonhyperbolic fixed points, which occur when the Hessian of the target function is the null matrix, requires further study. We prove that these nonhyperbolic interior fixed points are unstable in networks with three neurons and order two. The conjecture that interior equilibria are unstable in the general case is left open.

Lagrange constraint neural network for audio varying BSS

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Hsu, Charles C.

2002-03-01

Lagrange Constraint Neural Network (LCNN) is a statistical-mechanical ab-initio model without assuming the artificial neural network (ANN) model at all but derived it from the first principle of Hamilton and Lagrange Methodology: H(S,A)= f(S)- (lambda) C(s,A(x,t)) that incorporates measurement constraint C(S,A(x,t))= (lambda) ([A]S-X)+((lambda) 0-1)((Sigma) isi -1) using the vector Lagrange multiplier-(lambda) and a- priori Shannon Entropy f(S) = -(Sigma) i si log si as the Contrast function of unknown number of independent sources si. Szu et al. have first solved in 1997 the general Blind Source Separation (BSS) problem for spatial-temporal varying mixing matrix for the real world remote sensing where a large pixel footprint implies the mixing matrix [A(x,t)] necessarily fill with diurnal and seasonal variations. Because the ground truth is difficult to be ascertained in the remote sensing, we have thus illustrated in this paper, each step of the LCNN algorithm for the simulated spatial-temporal varying BSS in speech, music audio mixing. We review and compare LCNN with other popular a-posteriori Maximum Entropy methodologies defined by ANN weight matrix-[W] sigmoid-(sigma) post processing H(Y=(sigma) ([W]X)) by Bell-Sejnowski, Amari and Oja (BSAO) called Independent Component Analysis (ICA). Both are mirror symmetric of the MaxEnt methodologies and work for a constant unknown mixing matrix [A], but the major difference is whether the ensemble average is taken at neighborhood pixel data X's in BASO or at the a priori sources S variables in LCNN that dictates which method works for spatial-temporal varying [A(x,t)] that would not allow the neighborhood pixel average. We expected the success of sharper de-mixing by the LCNN method in terms of a controlled ground truth experiment in the simulation of variant mixture of two music of similar Kurtosis (15 seconds composed of Saint-Saens Swan and Rachmaninov cello concerto).

On the Maximum Storage Capacity of the Hopfield Model

PubMed Central

Folli, Viola; Leonetti, Marco; Ruocco, Giancarlo

2017-01-01

Recurrent neural networks (RNN) have traditionally been of great interest for their capacity to store memories. In past years, several works have been devoted to determine the maximum storage capacity of RNN, especially for the case of the Hopfield network, the most popular kind of RNN. Analyzing the thermodynamic limit of the statistical properties of the Hamiltonian corresponding to the Hopfield neural network, it has been shown in the literature that the retrieval errors diverge when the number of stored memory patterns (P) exceeds a fraction (≈ 14%) of the network size N. In this paper, we study the storage performance of a generalized Hopfield model, where the diagonal elements of the connection matrix are allowed to be different from zero. We investigate this model at finite N. We give an analytical expression for the number of retrieval errors and show that, by increasing the number of stored patterns over a certain threshold, the errors start to decrease and reach values below unit for P ≫ N. We demonstrate that the strongest trade-off between efficiency and effectiveness relies on the number of patterns (P) that are stored in the network by appropriately fixing the connection weights. When P≫N and the diagonal elements of the adjacency matrix are not forced to be zero, the optimal storage capacity is obtained with a number of stored memories much larger than previously reported. This theory paves the way to the design of RNN with high storage capacity and able to retrieve the desired pattern without distortions. PMID:28119595

Stochastic architecture for Hopfield neural nets

NASA Technical Reports Server (NTRS)

Pavel, Sandy

1992-01-01

An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.

Distributed Fault-Tolerant Control of Networked Uncertain Euler-Lagrange Systems Under Actuator Faults.

PubMed

Chen, Gang; Song, Yongduan; Lewis, Frank L

2016-05-03

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

NASA Astrophysics Data System (ADS)

Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

2016-06-01

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

Modelling microtubules in the brain as n-qudit quantum Hopfield network and beyond

NASA Astrophysics Data System (ADS)

Pyari Srivastava, Dayal; Sahni, Vishal; Saran Satsangi, Prem

2016-01-01

The scientific approach to understand the nature of consciousness revolves around the study of the human brain. Neurobiological studies that compare the nervous system of different species have accorded the highest place to humans on account of various factors that include a highly developed cortical area comprising of approximately 100 billion neurons, that are intrinsically connected to form a highly complex network. Quantum theories of consciousness are based on mathematical abstraction and the Penrose-Hameroff Orch-OR theory is one of the most promising ones. Inspired by the Penrose-Hameroff Orch-OR theory, Behrman et al. have simulated a quantum Hopfield neural network with the structure of a microtubule. They have used an extremely simplified model of the tubulin dimers with each dimer represented simply as a qubit, a single quantum two-state system. The extension of this model to n-dimensional quantum states or n-qudits presented in this work holds considerable promise for even higher mathematical abstraction in modelling consciousness systems.

Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

PubMed

Huang, Haiying; Du, Qiaosheng; Kang, Xibing

2013-11-01

In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

Scale-Limited Lagrange Stability and Finite-Time Synchronization for Memristive Recurrent Neural Networks on Time Scales.

PubMed

Xiao, Qiang; Zeng, Zhigang

2017-10-01

The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison principles are established by means of inequality techniques and induction principle on time scales. Then the results concerning Lagrange stability and global finite-time synchronization of MRNNs on time scales are obtained. Scaled-limited Lagrange stability criteria are derived, in detail, via nonsmooth analysis and theory of time scales. Moreover, novel criteria for achieving the global finite-time synchronization are acquired. In addition, the derived method can also be used to study global finite-time stabilization. The proposed results extend or improve the existed ones in the literatures. Two numerical examples are chosen to show the effectiveness of the obtained results.

Construction of a magnetostrictive hysteresis operator using a tripod-like primitive hopfield neural network

NASA Astrophysics Data System (ADS)

Adly, A. A.; Abd-El-Hafiz, S. K.

2018-05-01

It is well known that accurate modeling of magnetostrictive hysteresis is crucial to different industrial applications. Although several magnetostrictive models have been developed in the past, the accuracy-efficiency balance has always been crucial. Recently, the possibility of constructing a primitive vector hysteresis operator using a tri-node Hopfield Neural Network (HNN) was demonstrated. Based upon the fact that mechanical stress along a certain direction results in dimensional deformation, this paper introduces a novel extension to the aforementioned recently developed approach. More specifically, a stress-driven evolution of a tri-node HNN hysteresis operator pair is proposed, thus yielding a tripod-like HNN pair having different input offset values. Model identification, sample simulation results and comparison with experimental measurements are given in the paper.

A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping

PubMed Central

Kzar, Ahmed Asal; Mat Jafri, Mohd Zubir; Mutter, Kussay N.; Syahreza, Saumi

2015-01-01

Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted

Hopfield's Model of Patterns Recognition and Laws of Artistic Perception

NASA Astrophysics Data System (ADS)

Yevin, Igor; Koblyakov, Alexander

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

Augmented Lagrange Programming Neural Network for Localization Using Time-Difference-of-Arrival Measurements.

PubMed

Han, Zifa; Leung, Chi Sing; So, Hing Cheung; Constantinides, Anthony George

2017-08-15

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

PubMed Central

Adly, Amr A.; Abd-El-Hafiz, Salwa K.

2012-01-01

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper. PMID:25685446

Mean-field message-passing equations in the Hopfield model and its generalizations

NASA Astrophysics Data System (ADS)

Mézard, Marc

2017-02-01

Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations. This makes the latter method much better suited for applications in the learning process of restricted Boltzmann machines. In the case where the patterns memorized in the Hopfield model are not independent, but are correlated through a combinatorial structure, we show that the TAP equations have to be modified. This modification can be seen either as an alteration of the reaction term in TAP equations or, more interestingly, as the consequence of message passing on a graphical model with several hidden layers, where the number of hidden layers depends on the depth of the correlations in the memorized patterns. This layered structure is actually necessary when one deals with more general restricted Boltzmann machines.

Recognition of partially occluded threat objects using the annealed Hopefield network

NASA Technical Reports Server (NTRS)

Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

1992-01-01

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

NASA Astrophysics Data System (ADS)

Chang, Wen-Li

2010-01-01

We investigate the influence of blurred ways on pattern recognition of a Barabási-Albert scale-free Hopfield neural network (SFHN) with a small amount of errors. Pattern recognition is an important function of information processing in brain. Due to heterogeneous degree of scale-free network, different blurred ways have different influences on pattern recognition with same errors. Simulation shows that among partial recognition, the larger loading ratio (the number of patterns to average degree P/langlekrangle) is, the smaller the overlap of SFHN is. The influence of directed (large) way is largest and the directed (small) way is smallest while random way is intermediate between them. Under the ratio of the numbers of stored patterns to the size of the network P/N is less than 0. 1 conditions, there are three families curves of the overlap corresponding to directed (small), random and directed (large) blurred ways of patterns and these curves are not associated with the size of network and the number of patterns. This phenomenon only occurs in the SFHN. These conclusions are benefit for understanding the relation between neural network structure and brain function.

Mathematical improvement of the Hopfield model for feasible solutions to the traveling salesman problem by a synapse dynamical system.

PubMed

Takahashi, Y

1998-01-01

It is well known that the Hopfield Model (HM) for neural networks to solve the Traveling Salesman Problem (TSP) suffers from three major drawbacks. (1) It can converge on nonoptimal locally minimum solutions. (2) It can converge on infeasible solutions. (3) Results are very sensitive to the careful tuning of its parameters. A number of methods have been proposed to overcome (a) well. In contrast, work on (b) and (c) has not been sufficient; techniques have not been generalized to more general optimization problems. Thus this paper mathematically resolves (b) and (c) to such an extent that the resolution can be applied to solving with some general network continuous optimization problems including the Hopfield version of the TSP. It first constructs an Extended HM (E-HM) that overcomes both (b) and (c). Fundamental techniques of the E-HM lie in the addition of a synapse dynamical system cooperated with the current HM unit dynamical system. It is this synapse dynamical system that makes the TSP constraint hold at any final states for whatever choices of the IIM parameters and an initial state. The paper then generalizes the E-HM further to a network that can solve a class of continuous optimization problems with a constraint equation where both of the objective function and the constraint function are nonnegative and continuously differentiable.

A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks.

PubMed

Shang, Fengjun; Jiang, Yi; Xiong, Anping; Su, Wen; He, Li

2016-11-18

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed. When the unknown node is inside positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is outside positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Defense Against Chip Cloning Attacks Based on Fractional Hopfield Neural Networks.

PubMed

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-06-01

This paper presents a state-of-the-art application of fractional hopfield neural networks (FHNNs) to defend against chip cloning attacks, and provides insight into the reason that the proposed method is superior to physically unclonable functions (PUFs). In the past decade, PUFs have been evolving as one of the best types of hardware security. However, the development of the PUFs has been somewhat limited by its implementation cost, its temperature variation effect, its electromagnetic interference effect, the amount of entropy in it, etc. Therefore, it is imperative to discover, through promising mathematical methods and physical modules, some novel mechanisms to overcome the aforementioned weaknesses of the PUFs. Motivated by this need, in this paper, we propose applying the FHNNs to defend against chip cloning attacks. At first, we implement the arbitrary-order fractor of a FHNN. Secondly, we describe the implementation cost of the FHNNs. Thirdly, we propose the achievement of the constant-order performance of a FHNN when ambient temperature varies. Fourthly, we analyze the electrical performance stability of the FHNNs under electromagnetic disturbance conditions. Fifthly, we study the amount of entropy of the FHNNs. Lastly, we perform experiments to analyze the pass-band width of the fractor of an arbitrary-order FHNN and the defense against chip cloning attacks capability of the FHNNs. In particular, the capabilities of defense against chip cloning attacks, anti-electromagnetic interference, and anti-temperature variation of a FHNN are illustrated experimentally in detail. Some significant advantages of the FHNNs are that their implementation cost is considerably lower than that of the PUFs, their electrical performance is much more stable than that of the PUFs under different temperature conditions, their electrical performance stability of the FHNNs under electromagnetic disturbance conditions is much more robust than that of the PUFs, and their amount of

Recognition of Telugu characters using neural networks.

PubMed

Sukhaswami, M B; Seetharamulu, P; Pujari, A K

1995-09-01

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

A solution to neural field equations by a recurrent neural network method

NASA Astrophysics Data System (ADS)

Alharbi, Abir

2012-09-01

Neural field equations (NFE) are used to model the activity of neurons in the brain, it is introduced from a single neuron 'integrate-and-fire model' starting point. The neural continuum is spatially discretized for numerical studies, and the governing equations are modeled as a system of ordinary differential equations. In this article the recurrent neural network approach is used to solve this system of ODEs. This consists of a technique developed by combining the standard numerical method of finite-differences with the Hopfield neural network. The architecture of the net, energy function, updating equations, and algorithms are developed for the NFE model. A Hopfield Neural Network is then designed to minimize the energy function modeling the NFE. Results obtained from the Hopfield-finite-differences net show excellent performance in terms of accuracy and speed. The parallelism nature of the Hopfield approaches may make them easier to implement on fast parallel computers and give them the speed advantage over the traditional methods.

A hypercube compact neural network

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

Rostykus, P.L.; Somani, A.K.

1988-09-01

A major problem facing implementation of neural networks is the connection problem. One popular tradeoff is to remove connections. Random disconnection severely degrades the capabilities. The hypercube based Compact Neural Network (CNN) has structured architecture combined with a rearrangement of the memory vectors gives a larger input space and better degradation than a cost equivalent network with more connections. The CNNs are based on a Hopfield network. The changes from the Hopfield net include states of -1 and +1 and when a node was evaluated to 0, it was not biased either positive or negative, instead it resumed its previousmore » state. L = PEs, N = memories and t/sub ij/s is the weights between i and j.« less

Memory Retrieval Time and Memory Capacity of the CA3 Network: Role of Gamma Frequency Oscillations

ERIC Educational Resources Information Center

de Almeida, Licurgo; Idiart, Marco; Lisman, John E.

2007-01-01

The existence of recurrent synaptic connections in CA3 led to the hypothesis that CA3 is an autoassociative network similar to the Hopfield networks studied by theorists. CA3 undergoes gamma frequency periodic inhibition that prevents a persistent attractor state. This argues against the analogy to Hopfield nets, in which an attractor state can be…

The Lagrange Points

ERIC Educational Resources Information Center

Lovell, M.S.

2007-01-01

This paper presents a derivation of all five Lagrange points by methods accessible to sixth-form students, and provides a further opportunity to match Newtonian gravity with centripetal force. The predictive powers of good scientific theories are also discussed with regard to the philosophy of science. Methods for calculating the positions of the…

Variational Integrators for Interconnected Lagrange-Dirac Systems

NASA Astrophysics Data System (ADS)

Parks, Helen; Leok, Melvin

2017-10-01

Interconnected systems are an important class of mathematical models, as they allow for the construction of complex, hierarchical, multiphysics, and multiscale models by the interconnection of simpler subsystems. Lagrange-Dirac mechanical systems provide a broad category of mathematical models that are closed under interconnection, and in this paper, we develop a framework for the interconnection of discrete Lagrange-Dirac mechanical systems, with a view toward constructing geometric structure-preserving discretizations of interconnected systems. This work builds on previous work on the interconnection of continuous Lagrange-Dirac systems (Jacobs and Yoshimura in J Geom Mech 6(1):67-98, 2014) and discrete Dirac variational integrators (Leok and Ohsawa in Found Comput Math 11(5), 529-562, 2011). We test our results by simulating some of the continuous examples given in Jacobs and Yoshimura (2014).

Fixed weight Hopfield Neural Network based on optical implementation of all-optical MZI-XNOR logic gate

NASA Astrophysics Data System (ADS)

Nugamesh Mutter, Kussay; Mat Jafri, Mohd Zubir; Abdul Aziz, Azlan

2010-05-01

Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed and memory capacity in different approaches. However, there is still a significant scope of developing HNN using Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The primary operation and simulation of the proposal HNN is demonstrated.

Self-organization in neural networks - Applications in structural optimization

NASA Technical Reports Server (NTRS)

Hajela, Prabhat; Fu, B.; Berke, Laszlo

1993-01-01

The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.

Weighted-outer-product associative neural network

NASA Astrophysics Data System (ADS)

Ji, Han-Bing

1991-11-01

A weighted outer product learning (WOPL) scheme for associative memory neural network is presented in which learning orders are incorporated to the Hopfield model. WOPL can be guaranteed to achieve correct recall of some stored datums no matter whether or not they are stable in the Hopfield model, and whether the number of stored datums is small or large. A technically sufficient condition is also discussed for how to suitably choose learning orders to fully utilize WOPL for correct recall of as many stored datums as possible.

Comparison of neural network applications for channel assignment in cellular TDMA networks and dynamically sectored PCS networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

1997-04-01

The use of artificial neural networks (NNs) to address the channel assignment problem (CAP) for cellular time-division multiple access and code-division multiple access networks has previously been investigated by this author and many others. The investigations to date have been based on a hexagonal cell structure established by omnidirectional antennas at the base stations. No account was taken of the use of spatial isolation enabled by directional antennas to reduce interference between mobiles. Any reduction in interference translates into increased capacity and consequently alters the performance of the NNs. Previous studies have sought to improve the performance of Hopfield- Tank network algorithms and self-organizing feature map algorithms applied primarily to static channel assignment (SCA) for cellular networks that handle uniformly distributed, stationary traffic in each cell for a single type of service. The resulting algorithms minimize energy functions representing interference constraint and ad hoc conditions that promote convergence to optimal solutions. While the structures of the derived neural network algorithms (NNAs) offer the potential advantages of inherent parallelism and adaptability to changing system conditions, this potential has yet to be fulfilled the CAP for emerging mobile networks. The next-generation communication infrastructures must accommodate dynamic operating conditions. Macrocell topologies are being refined to microcells and picocells that can be dynamically sectored by adaptively controlled, directional antennas and programmable transceivers. These networks must support the time-varying demands for personal communication services (PCS) that simultaneously carry voice, data and video and, thus, require new dynamic channel assignment (DCA) algorithms. This paper examines the impact of dynamic cell sectoring and geometric conditioning on NNAs developed for SCA in omnicell networks with stationary traffic to improve the metrics

Sequential and parallel image restoration: neural network implementations.

PubMed

Figueiredo, M T; Leitao, J N

1994-01-01

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.

An Artificial Neural Networks Method for Solving Partial Differential Equations

NASA Astrophysics Data System (ADS)

Alharbi, Abir

2010-09-01

While there already exists many analytical and numerical techniques for solving PDEs, this paper introduces an approach using artificial neural networks. The approach consists of a technique developed by combining the standard numerical method, finite-difference, with the Hopfield neural network. The method is denoted Hopfield-finite-difference (HFD). The architecture of the nets, energy function, updating equations, and algorithms are developed for the method. The HFD method has been used successfully to approximate the solution of classical PDEs, such as the Wave, Heat, Poisson and the Diffusion equations, and on a system of PDEs. The software Matlab is used to obtain the results in both tabular and graphical form. The results are similar in terms of accuracy to those obtained by standard numerical methods. In terms of speed, the parallel nature of the Hopfield nets methods makes them easier to implement on fast parallel computers while some numerical methods need extra effort for parallelization.

16QAM Blind Equalization via Maximum Entropy Density Approximation Technique and Nonlinear Lagrange Multipliers

PubMed Central

Mauda, R.; Pinchas, M.

2014-01-01

Recently a new blind equalization method was proposed for the 16QAM constellation input inspired by the maximum entropy density approximation technique with improved equalization performance compared to the maximum entropy approach, Godard's algorithm, and others. In addition, an approximated expression for the minimum mean square error (MSE) was obtained. The idea was to find those Lagrange multipliers that bring the approximated MSE to minimum. Since the derivation of the obtained MSE with respect to the Lagrange multipliers leads to a nonlinear equation for the Lagrange multipliers, the part in the MSE expression that caused the nonlinearity in the equation for the Lagrange multipliers was ignored. Thus, the obtained Lagrange multipliers were not those Lagrange multipliers that bring the approximated MSE to minimum. In this paper, we derive a new set of Lagrange multipliers based on the nonlinear expression for the Lagrange multipliers obtained from minimizing the approximated MSE with respect to the Lagrange multipliers. Simulation results indicate that for the high signal to noise ratio (SNR) case, a faster convergence rate is obtained for a channel causing a high initial intersymbol interference (ISI) while the same equalization performance is obtained for an easy channel (initial ISI low). PMID:24723813

Neural networks and MIMD-multiprocessors

NASA Technical Reports Server (NTRS)

Vanhala, Jukka; Kaski, Kimmo

1990-01-01

Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.

Lagrange constraint neural networks for massive pixel parallel image demixing

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Hsu, Charles C.

2002-03-01

We have shown that the remote sensing optical imaging to achieve detailed sub-pixel decomposition is a unique application of blind source separation (BSS) that is truly linear of far away weak signal, instantaneous speed of light without delay, and along the line of sight without multiple paths. In early papers, we have presented a direct application of statistical mechanical de-mixing method called Lagrange Constraint Neural Network (LCNN). While the BSAO algorithm (using a posteriori MaxEnt ANN and neighborhood pixel average) is not acceptable for remote sensing, a mirror symmetric LCNN approach is all right assuming a priori MaxEnt for unknown sources to be averaged over the source statistics (not neighborhood pixel data) in a pixel-by-pixel independent fashion. LCNN reduces the computation complexity, save a great number of memory devices, and cut the cost of implementation. The Landsat system is designed to measure the radiation to deduce surface conditions and materials. For any given material, the amount of emitted and reflected radiation varies by the wavelength. In practice, a single pixel of a Landsat image has seven channels receiving 0.1 to 12 microns of radiation from the ground within a 20x20 meter footprint containing a variety of radiation materials. A-priori LCNN algorithm provides the spatial-temporal variation of mixture that is hardly de-mixable by other a-posteriori BSS or ICA methods. We have already compared the Landsat remote sensing using both methods in WCCI 2002 Hawaii. Unfortunately the absolute benchmark is not possible because of lacking of the ground truth. We will arbitrarily mix two incoherent sampled images as the ground truth. However, the constant total probability of co-located sources within the pixel footprint is necessary for the remote sensing constraint (since on a clear day the total reflecting energy is constant in neighborhood receiving pixel sensors), we have to normalized two image pixel-by-pixel as well. Then, the

Local interconnection neural networks

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

Zhang Jiajun; Zhang Li; Yan Dapen

1993-06-01

The idea of a local interconnection neural network (LINN) is presentd and compared with the globally interconnected Hopfield model. Under the storage limit requirement, LINN is shown to offer the same associative memory capability as the global interconnection neural network while having a much smaller interconnection matrix. LINN can be readily implemented optically using the currently available spatial light modulators. 15 refs.

A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

PubMed Central

Zheng, Wei; Zhang, Xiaoya; Lu, Qi

2015-01-01

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones. PMID:26011618

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

PubMed

Woodward, Alexander; Froese, Tom; Ikegami, Takashi

2015-02-01

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optical neural networks based on holographic correlators

NASA Astrophysics Data System (ADS)

Sokolov, V. K.; Shubnikov, E. I.

1995-10-01

The three most important models of neural networks — a bidirectional associative memory, Hopfield networks, and adaptive resonance networks — are used as examples to show that a holographic correlator has its place in the neural computing paradigm.

On Macroscopic Quantum Phenomena in Biomolecules and Cells: From Levinthal to Hopfield

PubMed Central

Raković, Dejan; Dugić, Miroljub; Jeknić-Dugić, Jasmina; Plavšić, Milenko; Jaćimovski, Stevo; Šetrajčić, Jovan

2014-01-01

In the context of the macroscopic quantum phenomena of the second kind, we hereby seek for a solution-in-principle of the long standing problem of the polymer folding, which was considered by Levinthal as (semi)classically intractable. To illuminate it, we applied quantum-chemical and quantum decoherence approaches to conformational transitions. Our analyses imply the existence of novel macroscopic quantum biomolecular phenomena, with biomolecular chain folding in an open environment considered as a subtle interplay between energy and conformation eigenstates of this biomolecule, governed by quantum-chemical and quantum decoherence laws. On the other hand, within an open biological cell, a system of all identical (noninteracting and dynamically noncoupled) biomolecular proteins might be considered as corresponding spatial quantum ensemble of these identical biomolecular processors, providing spatially distributed quantum solution to a single corresponding biomolecular chain folding, whose density of conformational states might be represented as Hopfield-like quantum-holographic associative neural network too (providing an equivalent global quantum-informational alternative to standard molecular-biology local biochemical approach in biomolecules and cells and higher hierarchical levels of organism, as well). PMID:25028662

Qualitative analysis of Cohen-Grossberg neural networks with multiple delays

NASA Astrophysics Data System (ADS)

Ye, Hui; Michel, Anthony N.; Wang, Kaining

1995-03-01

It is well known that a class of artificial neural networks with symmetric interconnections and without transmission delays, known as Cohen-Grossberg neural networks, possesses global stability (i.e., all trajectories tend to some equilibrium). We demonstrate in the present paper that many of the qualitative properties of Cohen-Grossberg networks will not be affected by the introduction of sufficiently small delays. Specifically, we establish some bound conditions for the time delays under which a given Cohen-Grossberg network with multiple delays is globally stable and possesses the same asymptotically stable equilibria as the corresponding network without delays. An effective method of determining the asymptotic stability of an equilibrium of a Cohen-Grossberg network with multiple delays is also presented. The present results are motivated by some of the authors earlier work [Phys. Rev. E 50, 4206 (1994)] and by some of the work of Marcus and Westervelt [Phys. Rev. A 39, 347 (1989)]. These works address qualitative analyses of Hopfield neural networks with one time delay. The present work generalizes these results to Cohen-Grossberg neural networks with multiple time delays. Hopfield neural networks constitute special cases of Cohen-Grossberg neural networks.

Artificial neural networks as quantum associative memory

NASA Astrophysics Data System (ADS)

Hamilton, Kathleen; Schrock, Jonathan; Imam, Neena; Humble, Travis

We present results related to the recall accuracy and capacity of Hopfield networks implemented on commercially available quantum annealers. The use of Hopfield networks and artificial neural networks as content-addressable memories offer robust storage and retrieval of classical information, however, implementation of these models using currently available quantum annealers faces several challenges: the limits of precision when setting synaptic weights, the effects of spurious spin-glass states and minor embedding of densely connected graphs into fixed-connectivity hardware. We consider neural networks which are less than fully-connected, and also consider neural networks which contain multiple sparsely connected clusters. We discuss the effect of weak edge dilution on the accuracy of memory recall, and discuss how the multiple clique structure affects the storage capacity. Our work focuses on storage of patterns which can be embedded into physical hardware containing n < 1000 qubits. This work was supported by the United States Department of Defense and used resources of the Computational Research and Development Programs as Oak Ridge National Laboratory under Contract No. DE-AC0500OR22725 with the U. S. Department of Energy.

Analog "neuronal" networks in early vision.

PubMed Central

Koch, C; Marroquin, J; Yuille, A

1986-01-01

Many problems in early vision can be formulated in terms of minimizing a cost function. Examples are shape from shading, edge detection, motion analysis, structure from motion, and surface interpolation. As shown by Poggio and Koch [Poggio, T. & Koch, C. (1985) Proc. R. Soc. London, Ser. B 226, 303-323], quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical, or chemical networks. However, in the presence of discontinuities, the cost function is nonquadratic, raising the question of designing efficient algorithms for computing the optimal solution. Recently, Hopfield and Tank [Hopfield, J. J. & Tank, D. W. (1985) Biol. Cybern. 52, 141-152] have shown that networks of nonlinear analog "neurons" can be effective in computing the solution of optimization problems. We show how these networks can be generalized to solve the nonconvex energy functionals of early vision. We illustrate this approach by implementing a specific analog network, solving the problem of reconstructing a smooth surface from sparse data while preserving its discontinuities. These results suggest a novel computational strategy for solving early vision problems in both biological and real-time artificial vision systems. PMID:3459172

What Did We Think Could Be Learned About Earth From Lagrange Point Observations?

NASA Technical Reports Server (NTRS)

Wiscombe, Warren

2011-01-01

The scientific excitement surrounding the NASA Lagrange point mission Triana, now called DSCOVR, tended to be forgotten in the brouhaha over other aspects of the mission. Yet a small band of scientists in 1998 got very excited about the possibilities offered by the Lagrange-point perspective on our planet. As one of the original co-investigators on the Triana mission, I witnessed that scientific excitement firsthand. I will bring to life the early period, circa 1998 to 2000, and share the reasons that we thought the Lagrange-point perspective on Earth would be scientifically revolutionary.

Unlearning of Mixed States in the Hopfield Model —Extensive Loading Case—

NASA Astrophysics Data System (ADS)

Hayashi, Kao; Hashimoto, Chinami; Kimoto, Tomoyuki; Uezu, Tatsuya

2018-05-01

We study the unlearning of mixed states in the Hopfield model for the extensive loading case. Firstly, we focus on case I, where several embedded patterns are correlated with each other, whereas the rest are uncorrelated. Secondly, we study case II, where patterns are divided into clusters in such a way that patterns in any cluster are correlated but those in two different clusters are not correlated. By using the replica method, we derive the saddle point equations for order parameters under the ansatz of replica symmetry. The same equations are also derived by self-consistent signal-to-noise analysis in case I. In both cases I and II, we find that when the correlation between patterns is large, the network loses its ability to retrieve the embedded patterns and, depending on the parameters, a confused memory, which is a mixed state and/or spin glass state, emerges. By unlearning the mixed state, the network acquires the ability to retrieve the embedded patterns again in some parameter regions. We find that to delete the mixed state and to retrieve the embedded patterns, the coefficient of unlearning should be chosen appropriately. We perform Markov chain Monte Carlo simulations and find that the simulation and theoretical results agree reasonably well, except for the spin glass solution in a parameter region due to the replica symmetry breaking. Furthermore, we find that the existence of many correlated clusters reduces the stabilities of both embedded patterns and mixed states.

High storage capacity in the Hopfield model with auto-interactions—stability analysis

NASA Astrophysics Data System (ADS)

Rocchi, Jacopo; Saad, David; Tantari, Daniele

2017-11-01

Recent studies point to the potential storage of a large number of patterns in the celebrated Hopfield associative memory model, well beyond the limits obtained previously. We investigate the properties of new fixed points to discover that they exhibit instabilities for small perturbations and are therefore of limited value as associative memories. Moreover, a large deviations approach also shows that errors introduced to the original patterns induce additional errors and increased corruption with respect to the stored patterns.

A neural network approach to job-shop scheduling.

PubMed

Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

1991-01-01

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

Collective Computation of Neural Network

DTIC Science & Technology

1990-03-15

Sciences, Beijing ABSTRACT Computational neuroscience is a new branch of neuroscience originating from current research on the theory of computer...scientists working in artificial intelligence engineering and neuroscience . The paper introduces the collective computational properties of model neural...vision research. On this basis, the authors analyzed the significance of the Hopfield model. Key phrases: Computational Neuroscience , Neural Network, Model

Hopfield-Kerr model and analogue black hole radiation in dielectrics

NASA Astrophysics Data System (ADS)

Belgiorno, F.; Cacciatori, S. L.; Dalla Piazza, F.; Doronzo, M.

2017-11-01

In the context of the interaction between the electromagnetic field and a dielectric dispersive lossless medium, we present a nonlinear version of the relativistically covariant Hopfield model, which is suitable for the description of a dielectric Kerr perturbation propagating in a dielectric medium. The nonlinearity is introduced in the Lagrangian through a self-interacting term proportional to the fourth power of the polarization field. We find an exact solution for the nonlinear equations describing a propagating perturbation in the dielectric medium. Furthermore, the presence of an analogue Hawking effect, as well as the thermal properties of the model, are discussed, confirming and improving the results achieved in the scalar case.

Integración automatizada de las ecuaciones de Lagrange en el movimiento orbital.

NASA Astrophysics Data System (ADS)

Abad, A.; San Juan, J. F.

The new techniques of algebraic manipulation, especially the Poisson Series Processor, permit the analytical integration of the more and more complex problems of celestial mechanics. The authors are developing a new Poisson Series Processor, PSPC, and they use it to solve the Lagrange equation of the orbital motion. They integrate the Lagrange equation by using the stroboscopic method, and apply it to the main problem of the artificial satellite theory.

Lagrange multiplier for perishable inventory model considering warehouse capacity planning

NASA Astrophysics Data System (ADS)

Amran, Tiena Gustina; Fatima, Zenny

2017-06-01

This paper presented Lagrange Muktiplier approach for solving perishable raw material inventory planning considering warehouse capacity. A food company faced an issue of managing perishable raw materials and marinades which have limited shelf life. Another constraint to be considered was the capacity of the warehouse. Therefore, an inventory model considering shelf life and raw material warehouse capacity are needed in order to minimize the company's inventory cost. The inventory model implemented in this study was the adapted economic order quantity (EOQ) model which is optimized using Lagrange multiplier. The model and solution approach were applied to solve a case industry in a food manufacturer. The result showed that the total inventory cost decreased 2.42% after applying the proposed approach.

How to use the Sun-Earth Lagrange points for fundamental physics and navigation

NASA Astrophysics Data System (ADS)

Tartaglia, A.; Lorenzini, E. C.; Lucchesi, D.; Pucacco, G.; Ruggiero, M. L.; Valko, P.

2018-01-01

We illustrate the proposal, nicknamed LAGRANGE, to use spacecraft, located at the Sun-Earth Lagrange points, as a physical reference frame. Performing time of flight measurements of electromagnetic signals traveling on closed paths between the points, we show that it would be possible: (a) to refine gravitational time delay knowledge due both to the Sun and the Earth; (b) to detect the gravito-magnetic frame dragging of the Sun, so deducing information about the interior of the star; (c) to check the possible existence of a galactic gravitomagnetic field, which would imply a revision of the properties of a dark matter halo; (d) to set up a relativistic positioning and navigation system at the scale of the inner solar system. The paper presents estimated values for the relevant quantities and discusses the feasibility of the project analyzing the behavior of the space devices close to the Lagrange points.

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

Neural network modeling of associative memory: Beyond the Hopfield model

NASA Astrophysics Data System (ADS)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

Retrieval Capabilities of Hierarchical Networks: From Dyson to Hopfield

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

2015-01-01

We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

Voting procedures from the perspective of theory of neural networks

NASA Astrophysics Data System (ADS)

Suleimenov, Ibragim; Panchenko, Sergey; Gabrielyan, Oleg; Pak, Ivan

2016-11-01

It is shown that voting procedure in any authority can be treated as Hopfield neural network analogue. It was revealed that weight coefficients of neural network which has discrete outputs -1 and 1 can be replaced by coefficients of a discrete set (-1, 0, 1). This gives us the opportunity to qualitatively analyze the voting procedure on the basis of limited data about mutual influence of members. It also proves that result of voting procedure is actually taken by network formed by voting members.

Technique to eliminate computational instability in multibody simulations employing the Lagrange multiplier

NASA Technical Reports Server (NTRS)

Watts, G.

1992-01-01

A programming technique to eliminate computational instability in multibody simulations that use the Lagrange multiplier is presented. The computational instability occurs when the attached bodies drift apart and violate the constraints. The programming technique uses the constraint equation, instead of integration, to determine the coordinates that are not independent. Although the equations of motion are unchanged, a complete derivation of the incorporation of the Lagrange multiplier into the equation of motion for two bodies is presented. A listing of a digital computer program which uses the programming technique to eliminate computational instability is also presented. The computer program simulates a solid rocket booster and parachute connected by a frictionless swivel.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Accuracy of Lagrange-sinc functions as a basis set for electronic structure calculations of atoms and molecules

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

Choi, Sunghwan; Hong, Kwangwoo; Kim, Jaewook

2015-03-07

We developed a self-consistent field program based on Kohn-Sham density functional theory using Lagrange-sinc functions as a basis set and examined its numerical accuracy for atoms and molecules through comparison with the results of Gaussian basis sets. The result of the Kohn-Sham inversion formula from the Lagrange-sinc basis set manifests that the pseudopotential method is essential for cost-effective calculations. The Lagrange-sinc basis set shows faster convergence of the kinetic and correlation energies of benzene as its size increases than the finite difference method does, though both share the same uniform grid. Using a scaling factor smaller than or equal tomore » 0.226 bohr and pseudopotentials with nonlinear core correction, its accuracy for the atomization energies of the G2-1 set is comparable to all-electron complete basis set limits (mean absolute deviation ≤1 kcal/mol). The same basis set also shows small mean absolute deviations in the ionization energies, electron affinities, and static polarizabilities of atoms in the G2-1 set. In particular, the Lagrange-sinc basis set shows high accuracy with rapid convergence in describing density or orbital changes by an external electric field. Moreover, the Lagrange-sinc basis set can readily improve its accuracy toward a complete basis set limit by simply decreasing the scaling factor regardless of systems.« less

Cooperatively surrounding control for multiple Euler-Lagrange systems subjected to uncertain dynamics and input constraints

NASA Astrophysics Data System (ADS)

Chen, Liang-Ming; Lv, Yue-Yong; Li, Chuan-Jiang; Ma, Guang-Fu

2016-12-01

In this paper, we investigate cooperatively surrounding control (CSC) of multi-agent systems modeled by Euler-Lagrange (EL) equations under a directed graph. With the consideration of the uncertain dynamics in an EL system, a backstepping CSC algorithm combined with neural-networks is proposed first such that the agents can move cooperatively to surround the stationary target. Then, a command filtered backstepping CSC algorithm is further proposed to deal with the constraints on control input and the absence of neighbors’ velocity information. Numerical examples of eight satellites surrounding one space target illustrate the effectiveness of the theoretical results. Project supported by the National Basic Research Program of China (Grant No. 2012CB720000) and the National Natural Science Foundation of China (Grant Nos. 61304005 and 61403103).

Neural Networks For Demodulation Of Phase-Modulated Signals

NASA Technical Reports Server (NTRS)

Altes, Richard A.

1995-01-01

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

A globally convergent Lagrange and barrier function iterative algorithm for the traveling salesman problem.

PubMed

Dang, C; Xu, L

2001-03-01

In this paper a globally convergent Lagrange and barrier function iterative algorithm is proposed for approximating a solution of the traveling salesman problem. The algorithm employs an entropy-type barrier function to deal with nonnegativity constraints and Lagrange multipliers to handle linear equality constraints, and attempts to produce a solution of high quality by generating a minimum point of a barrier problem for a sequence of descending values of the barrier parameter. For any given value of the barrier parameter, the algorithm searches for a minimum point of the barrier problem in a feasible descent direction, which has a desired property that the nonnegativity constraints are always satisfied automatically if the step length is a number between zero and one. At each iteration the feasible descent direction is found by updating Lagrange multipliers with a globally convergent iterative procedure. For any given value of the barrier parameter, the algorithm converges to a stationary point of the barrier problem without any condition on the objective function. Theoretical and numerical results show that the algorithm seems more effective and efficient than the softassign algorithm.

Visualizing and Understanding the Components of Lagrange and Newton Interpolation

ERIC Educational Resources Information Center

Yang, Yajun; Gordon, Sheldon P.

2016-01-01

This article takes a close look at Lagrange and Newton interpolation by graphically examining the component functions of each of these formulas. Although interpolation methods are often considered simply to be computational procedures, we demonstrate how the components of the polynomial terms in these formulas provide insight into where these…

Modified Lagrange invariants and their role in determining transverse and axial imaging resolutions of self-interference incoherent holographic systems.

PubMed

Rosen, Joseph; Kelner, Roy

2014-11-17

The Lagrange invariant is a well-known law for optical imaging systems formulated in the frame of ray optics. In this study, we reformulate this law in terms of wave optics and relate it to the resolution limits of various imaging systems. Furthermore, this modified Lagrange invariant is generalized for imaging along the z axis, resulting with the axial Lagrange invariant which can be used to analyze the axial resolution of various imaging systems. To demonstrate the effectiveness of the theory, analysis of the lateral and the axial imaging resolutions is provided for Fresnel incoherent correlation holography (FINCH) systems.

Performance evaluation of coherent Ising machines against classical neural networks

NASA Astrophysics Data System (ADS)

Haribara, Yoshitaka; Ishikawa, Hitoshi; Utsunomiya, Shoko; Aihara, Kazuyuki; Yamamoto, Yoshihisa

2017-12-01

The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

Euler-Lagrange formulas for pseudo-Kähler manifolds

NASA Astrophysics Data System (ADS)

Park, JeongHyeong

2016-01-01

Let c be a characteristic form of degree k which is defined on a Kähler manifold of real dimension m > 2 k. Taking the inner product with the Kähler form Ωk gives a scalar invariant which can be considered as a generalized Lovelock functional. The associated Euler-Lagrange equations are a generalized Einstein-Gauss-Bonnet gravity theory; this theory restricts to the canonical formalism if c =c2 is the second Chern form. We extend previous work studying these equations from the Kähler to the pseudo-Kähler setting.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

On the stability, storage capacity, and design of nonlinear continuous neural networks

NASA Technical Reports Server (NTRS)

Guez, Allon; Protopopsecu, Vladimir; Barhen, Jacob

1988-01-01

The stability, capacity, and design of a nonlinear continuous neural network are analyzed. Sufficient conditions for existence and asymptotic stability of the network's equilibria are reduced to a set of piecewise-linear inequality relations that can be solved by a feedforward binary network, or by methods such as Fourier elimination. The stability and capacity of the network is characterized by the post synaptic firing rate function. An N-neuron network with sigmoidal firing function is shown to have up to 3N equilibrium points. This offers a higher capacity than the (0.1-0.2)N obtained in the binary Hopfield network. Moreover, it is shown that by a proper selection of the postsynaptic firing rate function, one can significantly extend the capacity storage of the network.

A neural networks-based hybrid routing protocol for wireless mesh networks.

PubMed

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

PubMed Central

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

LAGRANGE: LAser GRavitational-wave ANtenna in GEodetic Orbit

NASA Astrophysics Data System (ADS)

Buchman, S.; Conklin, J. W.; Balakrishnan, K.; Aguero, V.; Alfauwaz, A.; Aljadaan, A.; Almajed, M.; Altwaijry, H.; Saud, T. A.; Byer, R. L.; Bower, K.; Costello, B.; Cutler, G. D.; DeBra, D. B.; Faied, D. M.; Foster, C.; Genova, A. L.; Hanson, J.; Hooper, K.; Hultgren, E.; Klavins, A.; Lantz, B.; Lipa, J. A.; Palmer, A.; Plante, B.; Sanchez, H. S.; Saraf, S.; Schaechter, D.; Shu, K.; Smith, E.; Tenerelli, D.; Vanbezooijen, R.; Vasudevan, G.; Williams, S. D.; Worden, S. P.; Zhou, J.; Zoellner, A.

2013-01-01

We describe a new space gravitational wave observatory design called LAG-RANGE that maintains all important LISA science at about half the cost and with reduced technical risk. It consists of three drag-free spacecraft in a geocentric formation. Fixed antennas allow continuous contact with the Earth, solving the problem of communications bandwidth and latency. A 70 mm diameter sphere with a 35 mm gap to its enclosure serves as the single inertial reference per spacecraft, operating in “true” drag-free mode (no test mass forcing). Other advantages are: a simple caging design based on the DISCOS 1972 drag-free mission, an all optical read-out with pm fine and nm coarse sensors, and the extensive technology heritage from the Honeywell gyroscopes, and the DISCOS and Gravity Probe B drag-free sensors. An Interferometric Measurement System, designed with reflective optics and a highly stabilized frequency standard, performs the ranging between test masses and requires a single optical bench with one laser per spacecraft. Two 20 cm diameter telescopes per spacecraft, each with infield pointing, incorporate novel technology developed for advanced optical systems by Lockheed Martin, who also designed the spacecraft based on a multi-flight proven bus structure. Additional technological advancements include updated drag-free propulsion, thermal control, charge management systems, and materials. LAGRANGE subsystems are designed to be scalable and modular, making them interchangeable with those of LISA or other gravitational science missions. We plan to space qualify critical technologies on small and nano satellite flights, with the first launch (UV-LED Sat) in 2013.

Necessary conditions for weighted mean convergence of Lagrange interpolation for exponential weights

NASA Astrophysics Data System (ADS)

Damelin, S. B.; Jung, H. S.; Kwon, K. H.

2001-07-01

Given a continuous real-valued function f which vanishes outside a fixed finite interval, we establish necessary conditions for weighted mean convergence of Lagrange interpolation for a general class of even weights w which are of exponential decay on the real line or at the endpoints of (-1,1).

Pointwise convergence of derivatives of Lagrange interpolation polynomials for exponential weights

NASA Astrophysics Data System (ADS)

Damelin, S. B.; Jung, H. S.

2005-01-01

For a general class of exponential weights on the line and on (-1,1), we study pointwise convergence of the derivatives of Lagrange interpolation. Our weights include even weights of smooth polynomial decay near +/-[infinity] (Freud weights), even weights of faster than smooth polynomial decay near +/-[infinity] (Erdos weights) and even weights which vanish strongly near +/-1, for example Pollaczek type weights.

On the commutator of C^{\\infty}} -symmetries and the reduction of Euler-Lagrange equations

NASA Astrophysics Data System (ADS)

Ruiz, A.; Muriel, C.; Olver, P. J.

2018-04-01

A novel procedure to reduce by four the order of Euler-Lagrange equations associated to nth order variational problems involving single variable integrals is presented. In preparation, a new formula for the commutator of two \

Comments on "The multisynapse neural network and its application to fuzzy clustering".

PubMed

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Electron Correlation in the Ionization Continuum of Molecules: Photoionization of N2 in the Vicinity of the Hopfield Series of Autoionizing States.

PubMed

Klinker, Markus; Marante, Carlos; Argenti, Luca; González-Vázquez, Jesús; Martín, Fernando

2018-02-15

Direct measurement of autoionization lifetimes by using time-resolved experimental techniques is a promising approach when energy-resolved spectroscopic methods do not work. Attosecond time-resolved experiments have recently provided the first quantitative determination of autoionization lifetimes of the lowest members of the well-known Hopfield series of resonances in N 2 . In this work, we have used the recently developed XCHEM approach to study photoionization of the N 2 molecule in the vicinity of these resonances. The XCHEM approach allows us to describe electron correlation in the molecular electronic continuum at a level similar to that provided by multireference configuration interaction methods in bound state calculations, a necessary condition to accurately describe autoionization, shakeup, and interchannel couplings occurring in this range of photon energies. Our results show that electron correlation leading to interchannel mixing is the main factor that determines the magnitude and shape of the N 2 photoionization cross sections, as well as the lifetimes of the Hopfield resonances. At variance with recent speculations, nonadiabatic effects do not seem to play a significant role. These conclusions are supported by the very good agreement between the calculated cross sections and those determined in synchrotron radiation and attosecond experiments.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Centrifuge Rotor Models: A Comparison of the Euler-Lagrange and the Bond Graph Modeling Approach

NASA Technical Reports Server (NTRS)

Granda, Jose J.; Ramakrishnan, Jayant; Nguyen, Louis H.

2006-01-01

A viewgraph presentation on centrifuge rotor models with a comparison using Euler-Lagrange and bond graph methods is shown. The topics include: 1) Objectives; 2) MOdeling Approach Comparisons; 3) Model Structures; and 4) Application.

A Fluid Structure Algorithm with Lagrange Multipliers to Model Free Swimming

NASA Astrophysics Data System (ADS)

Sahin, Mehmet; Dilek, Ezgi

2017-11-01

A new monolithic approach is prosed to solve the fluid-structure interaction (FSI) problem with Lagrange multipliers in order to model free swimming/flying. In the present approach, the fluid domain is modeled by the incompressible Navier-Stokes equations and discretized using an Arbitrary Lagrangian-Eulerian (ALE) formulation based on the stable side-centered unstructured finite volume method. The solid domain is modeled by the constitutive laws for the nonlinear Saint Venant-Kirchhoff material and the classical Galerkin finite element method is used to discretize the governing equations in a Lagrangian frame. In order to impose the body motion/deformation, the distance between the constraint pair nodes is imposed using the Lagrange multipliers, which is independent from the frame of reference. The resulting algebraic linear equations are solved in a fully coupled manner using a dual approach (null space method). The present numerical algorithm is initially validated for the classical FSI benchmark problems and then applied to the free swimming of three linked ellipses. The authors are grateful for the use of the computing resources provided by the National Center for High Performance Computing (UYBHM) under Grant Number 10752009 and the computing facilities at TUBITAK-ULAKBIM, High Performance and Grid Computing Center.

Fault-tolerance of a neural network solving the traveling salesman problem

NASA Technical Reports Server (NTRS)

Protzel, P.; Palumbo, D.; Arras, M.

1989-01-01

This study presents the results of a fault-injection experiment that stimulates a neural network solving the Traveling Salesman Problem (TSP). The network is based on a modified version of Hopfield's and Tank's original method. We define a performance characteristic for the TSP that allows an overall assessment of the solution quality for different city-distributions and problem sizes. Five different 10-, 20-, and 30- city cases are sued for the injection of up to 13 simultaneous stuck-at-0 and stuck-at-1 faults. The results of more than 4000 simulation-runs show the extreme fault-tolerance of the network, especially with respect to stuck-at-0 faults. One possible explanation for the overall surprising result is the redundancy of the problem representation.

Investigating Trojan Asteroids at the L4/L5 Sun-Earth Lagrange Points

NASA Technical Reports Server (NTRS)

John, K. K.; Graham, L. D.; Abell, P. A.

2015-01-01

Investigations of Earth's Trojan asteroids will have benefits for science, exploration, and resource utilization. By sending a small spacecraft to the Sun-Earth L4 or L5 Lagrange points to investigate near-Earth objects, Earth's Trojan population can be better understood. This could lead to future missions for larger precursor spacecraft as well as human missions. The presence of objects in the Sun-Earth L4 and L5 Lagrange points has long been suspected, and in 2010 NASA's Wide-field Infrared Survey Explorer (WISE) detected a 300 m object. To investigate these Earth Trojan asteroid objects, it is both essential and feasible to send spacecraft to these regions. By exploring a wide field area, a small spacecraft equipped with an IR camera could hunt for Trojan asteroids and other Earth co-orbiting objects at the L4 or L5 Lagrange points in the near-term. By surveying the region, a zeroth-order approximation of the number of objects could be obtained with some rough constraints on their diameters, which may lead to the identification of potential candidates for further study. This would serve as a precursor for additional future robotic and human exploration targets. Depending on the inclination of these potential objects, they could be used as proving areas for future missions in the sense that the delta-V's to get to these targets are relatively low as compared to other rendezvous missions. They can serve as platforms for extended operations in deep space while interacting with a natural object in microgravity. Theoretically, such low inclination Earth Trojan asteroids exist. By sending a spacecraft to L4 or L5, these likely and potentially accessible targets could be identified.

PID position regulation in one-degree-of-freedom Euler-Lagrange systems actuated by a PMSM

NASA Astrophysics Data System (ADS)

Verastegui-Galván, J.; Hernández-Guzmán, V. M.; Orrante-Sakanassi, J.

2018-02-01

This paper is concerned with position regulation in one-degree-of-freedom Euler-Lagrange Systems. We consider that the mechanical subsystem is actuated by a permanent magnet synchronous motor (PMSM). Our proposal consists of a Proportional-Integral-Derivative (PID) controller for the mechanical subsystem and a slight variation of field oriented control for the PMSM. We take into account the motor electric dynamics during the stability analysis. We present, for the first time, a global asymptotic stability proof for such a control scheme without requiring the mechanical subsystem to naturally possess viscous friction. Finally, as a corollary of our main result we prove global asymptotic stability for output feedback PID regulation of one-degree-of-freedom Euler-Lagrange systems when generated torque is considered as the system input, i.e. when the electric dynamics of PMSM's is not taken into account.

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Learning in stochastic neural networks for constraint satisfaction problems

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Adorf, Hans-Martin

1989-01-01

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

A curve of growth determination of the f-values for the fourth positive system of CO and the Lyman-Birge-Hopfield system of N2.

NASA Technical Reports Server (NTRS)

Pilling, M. J.; Bass, A. M.; Braun, W.

1971-01-01

The curve of growth method has been employed to determine f-values for the fourth positive system of CO and the magnetic dipole and electric quadrupole components of the Lyman-Birge-Hopfield system of N2. No significant dependence on r-centroid was found. The mean value of the ratio of the electric quadrupole to magnetic dipole f-values was 0.076.

A novel constructive-optimizer neural network for the traveling salesman problem.

PubMed

Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami

2007-08-01

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

NASA Astrophysics Data System (ADS)

Kirst, Christoph

It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

Projection-based stabilization of interface Lagrange multipliers in immersogeometric fluid-thin structure interaction analysis, with application to heart valve modeling.

PubMed

Kamensky, David; Evans, John A; Hsu, Ming-Chen; Bazilevs, Yuri

2017-11-01

This paper discusses a method of stabilizing Lagrange multiplier fields used to couple thin immersed shell structures and surrounding fluids. The method retains essential conservation properties by stabilizing only the portion of the constraint orthogonal to a coarse multiplier space. This stabilization can easily be applied within iterative methods or semi-implicit time integrators that avoid directly solving a saddle point problem for the Lagrange multiplier field. Heart valve simulations demonstrate applicability of the proposed method to 3D unsteady simulations. An appendix sketches the relation between the proposed method and a high-order-accurate approach for simpler model problems.

Optimal exponential synchronization of general chaotic delayed neural networks: an LMI approach.

PubMed

Liu, Meiqin

2009-09-01

This paper investigates the optimal exponential synchronization problem of general chaotic neural networks with or without time delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. This general model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, and recurrent multilayer perceptrons (RMLPs) with or without delays. Using the drive-response concept, time-delay feedback controllers are designed to synchronize two identical chaotic neural networks as quickly as possible. The control design equations are shown to be a generalized eigenvalue problem (GEVP) which can be easily solved by various convex optimization algorithms to determine the optimal control law and the optimal exponential synchronization rate. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Maximal switchability of centralized networks

NASA Astrophysics Data System (ADS)

Vakulenko, Sergei; Morozov, Ivan; Radulescu, Ovidiu

2016-08-01

We consider continuous time Hopfield-like recurrent networks as dynamical models for gene regulation and neural networks. We are interested in networks that contain n high-degree nodes preferably connected to a large number of N s weakly connected satellites, a property that we call n/N s -centrality. If the hub dynamics is slow, we obtain that the large time network dynamics is completely defined by the hub dynamics. Moreover, such networks are maximally flexible and switchable, in the sense that they can switch from a globally attractive rest state to any structurally stable dynamics when the response time of a special controller hub is changed. In particular, we show that a decrease of the controller hub response time can lead to a sharp variation in the network attractor structure: we can obtain a set of new local attractors, whose number can increase exponentially with N, the total number of nodes of the nework. These new attractors can be periodic or even chaotic. We provide an algorithm, which allows us to design networks with the desired switching properties, or to learn them from time series, by adjusting the interactions between hubs and satellites. Such switchable networks could be used as models for context dependent adaptation in functional genetics or as models for cognitive functions in neuroscience.

Convergence of Asymptotic Systems of Non-autonomous Neural Network Models with Infinite Distributed Delays

NASA Astrophysics Data System (ADS)

Oliveira, José J.

2017-10-01

In this paper, we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.

Free-Lagrange methods for compressible hydrodynamics in two space dimensions

NASA Astrophysics Data System (ADS)

Crowley, W. E.

1985-03-01

Since 1970 a research and development program in Free-Lagrange methods has been active at Livermore. The initial steps were taken with incompressible flows for simplicity. Since then the effort has been concentrated on compressible flows with shocks in two space dimensions and time. In general, the line integral method has been used to evaluate derivatives and the artificial viscosity method has been used to deal with shocks. Basically, two Free-Lagrange formulations for compressible flows in two space dimensions and time have been tested and both will be described. In method one, all prognostic quantities were node centered and staggered in time. The artificial viscosity was zone centered. One mesh reconnection philosphy was that the mesh should be optimized so that nearest neighbors were connected together. Another was that vertex angles should tend toward equality. In method one, all mesh elements were triangles. In method two, both quadrilateral and triangular mesh elements are permitted. The mesh variables are staggered in space and time as suggested originally by Richtmyer and von Neumann. The mesh reconnection strategy is entirely different in method two. In contrast to the global strategy of nearest neighbors, we now have a more local strategy that reconnects in order to keep the integration time step above a user chosen threshold. An additional strategy reconnects in the vicinity of large relative fluid motions. Mesh reconnection consists of two parts: (1) the tools that permits nodes to be merged and quads to be split into triangles etc. and; (2) the strategy that dictates how and when to use the tools. Both tools and strategies change with time in a continuing effort to expand the capabilities of the method. New ideas are continually being tried and evaluated.

Statistical Mechanics of Coherent Ising Machine — The Case of Ferromagnetic and Finite-Loading Hopfield Models —

NASA Astrophysics Data System (ADS)

Aonishi, Toru; Mimura, Kazushi; Utsunomiya, Shoko; Okada, Masato; Yamamoto, Yoshihisa

2017-10-01

The coherent Ising machine (CIM) has attracted attention as one of the most effective Ising computing architectures for solving large scale optimization problems because of its scalability and high-speed computational ability. However, it is difficult to implement the Ising computation in the CIM because the theories and techniques of classical thermodynamic equilibrium Ising spin systems cannot be directly applied to the CIM. This means we have to adapt these theories and techniques to the CIM. Here we focus on a ferromagnetic model and a finite loading Hopfield model, which are canonical models sharing a common mathematical structure with almost all other Ising models. We derive macroscopic equations to capture nonequilibrium phase transitions in these models. The statistical mechanical methods developed here constitute a basis for constructing evaluation methods for other Ising computation models.

Modeling of Mixing Behavior in a Combined Blowing Steelmaking Converter with a Filter-Based Euler-Lagrange Model

NASA Astrophysics Data System (ADS)

Li, Mingming; Li, Lin; Li, Qiang; Zou, Zongshu

2018-05-01

A filter-based Euler-Lagrange multiphase flow model is used to study the mixing behavior in a combined blowing steelmaking converter. The Euler-based volume of fluid approach is employed to simulate the top blowing, while the Lagrange-based discrete phase model that embeds the local volume change of rising bubbles for the bottom blowing. A filter-based turbulence method based on the local meshing resolution is proposed aiming to improve the modeling of turbulent eddy viscosities. The model validity is verified through comparison with physical experiments in terms of mixing curves and mixing times. The effects of the bottom gas flow rate on bath flow and mixing behavior are investigated and the inherent reasons for the mixing result are clarified in terms of the characteristics of bottom-blowing plumes, the interaction between plumes and top-blowing jets, and the change of bath flow structure.

Structures and Boolean Dynamics in Gene Regulatory Networks

NASA Astrophysics Data System (ADS)

Szedlak, Anthony

This dissertation discusses the topological and dynamical properties of GRNs in cancer, and is divided into four main chapters. First, the basic tools of modern complex network theory are introduced. These traditional tools as well as those developed by myself (set efficiency, interset efficiency, and nested communities) are crucial for understanding the intricate topological properties of GRNs, and later chapters recall these concepts. Second, the biology of gene regulation is discussed, and a method for disease-specific GRN reconstruction developed by our collaboration is presented. This complements the traditional exhaustive experimental approach of building GRNs edge-by-edge by quickly inferring the existence of as of yet undiscovered edges using correlations across sets of gene expression data. This method also provides insight into the distribution of common mutations across GRNs. Third, I demonstrate that the structures present in these reconstructed networks are strongly related to the evolutionary histories of their constituent genes. Investigation of how the forces of evolution shaped the topology of GRNs in multicellular organisms by growing outward from a core of ancient, conserved genes can shed light upon the ''reverse evolution'' of normal cells into unicellular-like cancer states. Next, I simulate the dynamics of the GRNs of cancer cells using the Hopfield model, an infinite range spin-glass model designed with the ability to encode Boolean data as attractor states. This attractor-driven approach facilitates the integration of gene expression data into predictive mathematical models. Perturbations representing therapeutic interventions are applied to sets of genes, and the resulting deviations from their attractor states are recorded, suggesting new potential drug targets for experimentation. Finally, I extend the Hopfield model to modular networks, cyclic attractors, and complex attractors, and apply these concepts to simulations of the cell cycle

Parallel processing a three-dimensional free-lagrange code

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

Mandell, D.A.; Trease, H.E.

1989-01-01

A three-dimensional, time-dependent free-Lagrange hydrodynamics code has been multitasked and autotasked on a CRAY X-MP/416. The multitasking was done by using the Los Alamos Multitasking Control Library, which is a superset of the CRAY multitasking library. Autotasking is done by using constructs which are only comment cards if the source code is not run through a preprocessor. The three-dimensional algorithm has presented a number of problems that simpler algorithms, such as those for one-dimensional hydrodynamics, did not exhibit. Problems in converting the serial code, originally written for a CRAY-1, to a multitasking code are discussed. Autotasking of a rewritten versionmore » of the code is discussed. Timing results for subroutines and hot spots in the serial code are presented and suggestions for additional tools and debugging aids are given. Theoretical speedup results obtained from Amdahl's law and actual speedup results obtained on a dedicated machine are presented. Suggestions for designing large parallel codes are given.« less

Voidage correction algorithm for unresolved Euler-Lagrange simulations

NASA Astrophysics Data System (ADS)

Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan

2018-04-01

The effect of grid coarsening on the predicted total drag force and heat exchange rate in dense gas-particle flows is investigated using Euler-Lagrange (EL) approach. We demonstrate that grid coarsening may reduce the predicted total drag force and exchange rate. Surprisingly, exchange coefficients predicted by the EL approach deviate more significantly from the exact value compared to results of Euler-Euler (EE)-based calculations. The voidage gradient is identified as the root cause of this peculiar behavior. Consequently, we propose a correction algorithm based on a sigmoidal function to predict the voidage experienced by individual particles. Our correction algorithm can significantly improve the prediction of exchange coefficients in EL models, which is tested for simulations involving Euler grid cell sizes between 2d_p and 12d_p . It is most relevant in simulations of dense polydisperse particle suspensions featuring steep voidage profiles. For these suspensions, classical approaches may result in an error of the total exchange rate of up to 30%.

The Lagrange Points in a Binary Black Hole System: Applications to Electromagnetic Signatures

NASA Technical Reports Server (NTRS)

Schnittman, Jeremy

2010-01-01

We study the stability and evolution of the Lagrange points L_4 and L-5 in a black hole (BH) binary system, including gravitational radiation. We find that gas and stars can be shepherded in with the BH system until the final moments before merger, providing the fuel for a bright electromagnetic counterpart to a gravitational wave signal. Other astrophysical signatures include the ejection of hyper-velocity stars, gravitational collapse of globular clusters, and the periodic shift of narrow emission lines in AGN.

Lagrange formula for differential operators on a tree-graph and the resolvents of well-posed restrictions of operator

NASA Astrophysics Data System (ADS)

Koshkarbayev, Nurbol; Kanguzhin, Baltabek

2017-09-01

In this paper we study the question on the full description of well-posed restrictions of given maximal differential operator on a tree-graph. Lagrange formula for differential operator on a tree with Kirchhoff conditions at its internal vertices is presented.

Error estimates of Lagrange interpolation and orthonormal expansions for Freud weights

NASA Astrophysics Data System (ADS)

Kwon, K. H.; Lee, D. W.

2001-08-01

Let Sn[f] be the nth partial sum of the orthonormal polynomials expansion with respect to a Freud weight. Then we obtain sufficient conditions for the boundedness of Sn[f] and discuss the speed of the convergence of Sn[f] in weighted Lp space. We also find sufficient conditions for the boundedness of the Lagrange interpolation polynomial Ln[f], whose nodal points are the zeros of orthonormal polynomials with respect to a Freud weight. In particular, if W(x)=e-(1/2)x2 is the Hermite weight function, then we obtain sufficient conditions for the inequalities to hold:andwhere and k=0,1,2...,r.

Domain decomposition methods for nonconforming finite element spaces of Lagrange-type

NASA Technical Reports Server (NTRS)

Cowsar, Lawrence C.

1993-01-01

In this article, we consider the application of three popular domain decomposition methods to Lagrange-type nonconforming finite element discretizations of scalar, self-adjoint, second order elliptic equations. The additive Schwarz method of Dryja and Widlund, the vertex space method of Smith, and the balancing method of Mandel applied to nonconforming elements are shown to converge at a rate no worse than their applications to the standard conforming piecewise linear Galerkin discretization. Essentially, the theory for the nonconforming elements is inherited from the existing theory for the conforming elements with only modest modification by constructing an isomorphism between the nonconforming finite element space and a space of continuous piecewise linear functions.

Generalized Lagrange Jacobi Gauss-Lobatto (GLJGL) Collocation Method for Solving Linear and Nonlinear Fokker-Planck Equations

NASA Astrophysics Data System (ADS)

Parand, K.; Latifi, S.; Moayeri, M. M.; Delkhosh, M.

2018-05-01

In this study, we have constructed a new numerical approach for solving the time-dependent linear and nonlinear Fokker-Planck equations. In fact, we have discretized the time variable with Crank-Nicolson method and for the space variable, a numerical method based on Generalized Lagrange Jacobi Gauss-Lobatto (GLJGL) collocation method is applied. It leads to in solving the equation in a series of time steps and at each time step, the problem is reduced to a problem consisting of a system of algebraic equations that greatly simplifies the problem. One can observe that the proposed method is simple and accurate. Indeed, one of its merits is that it is derivative-free and by proposing a formula for derivative matrices, the difficulty aroused in calculation is overcome, along with that it does not need to calculate the General Lagrange basis and matrices; they have Kronecker property. Linear and nonlinear Fokker-Planck equations are given as examples and the results amply demonstrate that the presented method is very valid, effective, reliable and does not require any restrictive assumptions for nonlinear terms.

Prediction of a Densely Loaded Particle-Laden Jet using a Euler-Lagrange Dense Spray Model

NASA Astrophysics Data System (ADS)

Pakseresht, Pedram; Apte, Sourabh V.

2017-11-01

Modeling of a dense spray regime using an Euler-Lagrange discrete-element approach is challenging because of local high volume loading. A subgrid cluster of droplets can lead to locally high void fractions for the disperse phase. Under these conditions, spatio-temporal changes in the carrier phase volume fractions, which are commonly neglected in spray simulations in an Euler-Lagrange two-way coupling model, could become important. Accounting for the carrier phase volume fraction variations, leads to zero-Mach number, variable density governing equations. Using pressure-based solvers, this gives rise to a source term in the pressure Poisson equation and a non-divergence free velocity field. To test the validity and predictive capability of such an approach, a round jet laden with solid particles is investigated using Direct Numerical Simulation and compared with available experimental data for different loadings. Various volume fractions spanning from dilute to dense regimes are investigated with and without taking into account the volume displacement effects. The predictions of the two approaches are compared and analyzed to investigate the effectiveness of the dense spray model. Financial support was provided by National Aeronautics and Space Administration (NASA).

Identifying fMRI Model Violations with Lagrange Multiplier Tests

PubMed Central

Cassidy, Ben; Long, Christopher J; Rae, Caroline; Solo, Victor

2013-01-01

The standard modeling framework in Functional Magnetic Resonance Imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialised software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange Multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as non-linearity, non-stationarity and validity of the common Double Gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples. PMID:22542665

CarbonTracker-Lagrange: A Framework for Greenhouse Gas Flux Estimation at Regional to Continental Scales

NASA Astrophysics Data System (ADS)

Andrews, A. E.

2016-12-01

CarbonTracker-Lagrange (CT-L) is a flexible modeling framework developed to take advantage of newly available atmospheric data for CO2 and other long-lived gases such as CH4 and N2O. The North American atmospheric CO2 measurement network has grown from three sites in 2004 to >100 sites in 2015. The US network includes tall tower, mountaintop, surface, and aircraft sites in the NOAA Global Greenhouse Gas Reference Network along with sites maintained by university, government and private sector researchers. The Canadian network is operated by Environment and Climate Change Canada. This unprecedented dataset can provide spatially and temporally resolved CO2 emissions and uptake flux estimates and quantitative information about drivers of variability, such as drought and temperature. CT-L is a platform for systematic comparison of data assimilation techniques and evaluation of assumed prior, model and observation errors. A novel feature of CT-L is the optimization of boundary values along with surface fluxes, leveraging vertically resolved data available from NOAA's aircraft sampling program. CT-L uses observation footprints (influence functions) from the Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) modeling system to relate atmospheric measurements to upwind fluxes and boundary values. Footprints are pre-computed and the optimization algorithms are efficient, so many variants of the calculation can be performed. Fluxes are adjusted using Bayesian or Geostatistical methods to provide optimal agreement with observations. Satellite measurements of CO2 and CH4 from GOSAT are available starting in July 2009 and from OCO-2 since September 2014. With support from the NASA Carbon Monitoring System, we are developing flux estimation strategies that use remote sensing and in situ data together, including geostatistical inversions using satellite retrievals of solar-induced chlorophyll fluorescence. CT-L enables quantitative

Attractor Metabolic Networks

PubMed Central

De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

2013-01-01

Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency

Memory recall and spike-frequency adaptation

NASA Astrophysics Data System (ADS)

Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

2016-05-01

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

On a Lagrange-Hamilton formalism describing position and momentum uncertainties

NASA Technical Reports Server (NTRS)

Schuch, Dieter

1993-01-01

According to Heisenberg's uncertainty relation, in quantum mechanics it is not possible to determine, simultaneously, exact values for the position and the momentum of a material system. Calculating the mean value of the Hamiltonian operator with the aid of exact analytic Gaussian wave packet solutions, these uncertainties cause an energy contribution additional to the classical energy of the system. For the harmonic oscillator, e.g., this nonclassical energy represents the ground state energy. It will be shown that this additional energy contribution can be considered as a Hamiltonian function, if it is written in appropriate variables. With the help of the usual Lagrange-Hamilton formalism known from classical particle mechanics, but now considering this new Hamiltonian function, it is possible to obtain the equations of motion for position and momentum uncertainties.

[Cognitive advantages of the third age: a neural network model of brain aging].

PubMed

Karpenko, M P; Kachalova, L M; Budilova, E V; Terekhin, A T

2009-01-01

We consider a neural network model of age-related cognitive changes in aging brain based on Hopfield network with a sigmoid function of neuron activation. Age is included in the activation function as a parameter in the form of exponential rate denominator, which makes it possible to take into account the weakening of interneuronal links really observed in the aging brain. Analysis of properties of the Lyapunov function associated with the network shows that, with increasing parameter of age, its relief becomes smoother and the number of local minima (network attractors) decreases. As a result, the network gets less frequently stuck in the nearest local minima of the Lyapunov function and reaches a global minimum corresponding to the most effective solution of the cognitive task. It is reasonable to assume that similar changes really occur in the aging brain. Phenomenologically, these changes can be manifested as emergence in aged people of a cognitive quality such as wisdom i.e. ability to find optimal decisions in difficult controversial situations, to distract from secondary aspects and to see the problem as a whole.

Exponential H(infinity) synchronization of general discrete-time chaotic neural networks with or without time delays.

PubMed

Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

2010-08-01

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

Image resolution enhancement via image restoration using neural network

NASA Astrophysics Data System (ADS)

Zhang, Shuangteng; Lu, Yihong

2011-04-01

Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant.

Application of parallel distributed Lagrange multiplier technique to simulate coupled Fluid-Granular flows in pipes with varying Cross-Sectional area

DOE PAGES

Kanarska, Yuliya; Walton, Otis

2015-11-30

Fluid-granular flows are common phenomena in nature and industry. Here, an efficient computational technique based on the distributed Lagrange multiplier method is utilized to simulate complex fluid-granular flows. Each particle is explicitly resolved on an Eulerian grid as a separate domain, using solid volume fractions. The fluid equations are solved through the entire computational domain, however, Lagrange multiplier constrains are applied inside the particle domain such that the fluid within any volume associated with a solid particle moves as an incompressible rigid body. The particle–particle interactions are implemented using explicit force-displacement interactions for frictional inelastic particles similar to the DEMmore » method with some modifications using the volume of an overlapping region as an input to the contact forces. Here, a parallel implementation of the method is based on the SAMRAI (Structured Adaptive Mesh Refinement Application Infrastructure) library.« less

Robust adaptive uniform exact tracking control for uncertain Euler-Lagrange system

NASA Astrophysics Data System (ADS)

Yang, Yana; Hua, Changchun; Li, Junpeng; Guan, Xinping

2017-12-01

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler-Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.

Convergence and rate analysis of neural networks for sparse approximation.

PubMed

Balavoine, Aurèle; Romberg, Justin; Rozell, Christopher J

2012-09-01

We present an analysis of the Locally Competitive Algorithm (LCA), which is a Hopfield-style neural network that efficiently solves sparse approximation problems (e.g., approximating a vector from a dictionary using just a few nonzero coefficients). This class of problems plays a significant role in both theories of neural coding and applications in signal processing. However, the LCA lacks analysis of its convergence properties, and previous results on neural networks for nonsmooth optimization do not apply to the specifics of the LCA architecture. We show that the LCA has desirable convergence properties, such as stability and global convergence to the optimum of the objective function when it is unique. Under some mild conditions, the support of the solution is also proven to be reached in finite time. Furthermore, some restrictions on the problem specifics allow us to characterize the convergence rate of the system by showing that the LCA converges exponentially fast with an analytically bounded convergence rate. We support our analysis with several illustrative simulations.

Complex-valued multistate associative memory with nonlinear multilevel functions for gray-level image reconstruction.

PubMed

Tanaka, Gouhei; Aihara, Kazuyuki

2009-09-01

A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages.

Protein complexes and functional modules in molecular networks

NASA Astrophysics Data System (ADS)

Spirin, Victor; Mirny, Leonid A.

2003-10-01

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

Nonequilibrium landscape theory of neural networks

PubMed Central

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-01-01

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

Nonequilibrium landscape theory of neural networks.

PubMed

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-11-05

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

Parallel and orthogonal stimulus in ultradiluted neural networks

NASA Astrophysics Data System (ADS)

Sobral, G. A., Jr.; Vieira, V. M.; Lyra, M. L.; da Silva, C. R.

2006-10-01

Extending a model due to Derrida, Gardner, and Zippelius, we have studied the recognition ability of an extreme and asymmetrically diluted version of the Hopfield model for associative memory by including the effect of a stimulus in the dynamics of the system. We obtain exact results for the dynamic evolution of the average network superposition. The stimulus field was considered as proportional to the overlapping of the state of the system with a particular stimulated pattern. Two situations were analyzed, namely, the external stimulus acting on the initialization pattern (parallel stimulus) and the external stimulus acting on a pattern orthogonal to the initialization one (orthogonal stimulus). In both cases, we obtained the complete phase diagram in the parameter space composed of the stimulus field, thermal noise, and network capacity. Our results show that the system improves its recognition ability for parallel stimulus. For orthogonal stimulus two recognition phases emerge with the system locking at the initialization or stimulated pattern. We confront our analytical results with numerical simulations for the noiseless case T=0 .

The Relation Among the Likelihood Ratio-, Wald-, and Lagrange Multiplier Tests and Their Applicability to Small Samples,

DTIC Science & Technology

1982-04-01

S. (1979), "Conflict Among Criteria for Testing Hypothesis: Extension and Comments," Econometrica, 47, 203-207 Breusch , T. S. and Pagan , A. R. (1980...Savin, N. E. (1977), "Conflict Among Criteria for Testing Hypothesis in the Multivariate Linear Regression Model," Econometrica, 45, 1263-1278 Breusch , T...VNCLASSIFIED RAND//-6756NL U l~ I- THE RELATION AMONG THE LIKELIHOOD RATIO-, WALD-, AND LAGRANGE MULTIPLIER TESTS AND THEIR APPLICABILITY TO SMALL SAMPLES

Variational tricomplex of a local gauge system, Lagrange structure and weak Poisson bracket

NASA Astrophysics Data System (ADS)

Sharapov, A. A.

2015-09-01

We introduce the concept of a variational tricomplex, which is applicable both to variational and nonvariational gauge systems. Assigning this tricomplex with an appropriate symplectic structure and a Cauchy foliation, we establish a general correspondence between the Lagrangian and Hamiltonian pictures of one and the same (not necessarily variational) dynamics. In practical terms, this correspondence allows one to construct the generating functional of a weak Poisson structure starting from that of a Lagrange structure. As a byproduct, a covariant procedure is proposed for deriving the classical BRST charge of the BFV formalism by a given BV master action. The general approach is illustrated by the examples of Maxwell’s electrodynamics and chiral bosons in two dimensions.

Production of N2 Vegard-Kaplan and Lyman-Birge-Hopfield emissions on Pluto

NASA Astrophysics Data System (ADS)

Jain, Sonal Kumar; Bhardwaj, Anil

2015-01-01

We have developed a model to calculate the emission intensities of various vibrational transitions of N2 triplet band and Lyman-Birge-Hopfield (LBH) band emissions in the dayglow of Pluto for solar minimum, moderate, and maximum conditions. The calculated overhead intensities of Vegard-Kaplan (A3Σu+ -X1 Σg+) , First Positive (B3Πg -A3 Σu+), Second Positive (C3Πu -B3Πg) , Wu-Benesch (W3Δu -B3Πg) , Reverse First Positive, and LBH (a1Πg -X1 Σg+) bands of N2 are 17 (74), 14.8 (64), 2.4 (10.8), 2.9 (12.7), 2.9 (12.5), and 2.3 (10) R, respectively, for solar minimum (maximum) condition. We have predicted the overhead and limb intensities of VK (150-190 nm) and LBH (120-190 nm) bands of N2 on Pluto for the New Horizons (NH) flyby condition that can be observed by ALICE: the ultraviolet imaging spectrograph also know as P-ALICE. The predicted limb intensities of VK and LBH bands peak at radial distance of ∼2000 km with the value of about 5 (13) and 9.5 (22) R for solar zenith angle 60° (0°), respectively. We have also calculated overhead and limb intensities of few prominent transition of CO Fourth Positive bands for NH flyby condition.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models

NASA Technical Reports Server (NTRS)

Keeler, James D.

1987-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models. [Sparse, Distributed Memory

NASA Technical Reports Server (NTRS)

Keeler, James D.

1988-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

A suggested trajectory for a Venus-sun, earth-sun Lagrange points mission, Vela

NASA Technical Reports Server (NTRS)

Bender, D. F.

1979-01-01

The possibility is suggested of investigating the existence of small, as-yet undiscovered, asteroids orbiting in the solar system near the earth-sun or Venus-sun stable Lagrange points by means of a spacecraft which traverses these regions. The type of trajectory suggested lies in the ecliptic plane and has a period of 5/6 years and a perihelion at the Venus orbital distance. The regions in which stable orbits associated with the earth and with Venus may lie are estimated to be a thin and tadpole-shaped area extending from 35 deg to 100 deg from the planet. Crossings of the regions by the trajectory are described, and the requirements for detecting the presence of 1 km sized asteroids are presented and shown to be attainable.

A composite experimental dynamic substructuring method based on partitioned algorithms and localized Lagrange multipliers

NASA Astrophysics Data System (ADS)

Abbiati, Giuseppe; La Salandra, Vincenzo; Bursi, Oreste S.; Caracoglia, Luca

2018-02-01

Successful online hybrid (numerical/physical) dynamic substructuring simulations have shown their potential in enabling realistic dynamic analysis of almost any type of non-linear structural system (e.g., an as-built/isolated viaduct, a petrochemical piping system subjected to non-stationary seismic loading, etc.). Moreover, owing to faster and more accurate testing equipment, a number of different offline experimental substructuring methods, operating both in time (e.g. the impulse-based substructuring) and frequency domains (i.e. the Lagrange multiplier frequency-based substructuring), have been employed in mechanical engineering to examine dynamic substructure coupling. Numerous studies have dealt with the above-mentioned methods and with consequent uncertainty propagation issues, either associated with experimental errors or modelling assumptions. Nonetheless, a limited number of publications have systematically cross-examined the performance of the various Experimental Dynamic Substructuring (EDS) methods and the possibility of their exploitation in a complementary way to expedite a hybrid experiment/numerical simulation. From this perspective, this paper performs a comparative uncertainty propagation analysis of three EDS algorithms for coupling physical and numerical subdomains with a dual assembly approach based on localized Lagrange multipliers. The main results and comparisons are based on a series of Monte Carlo simulations carried out on a five-DoF linear/non-linear chain-like systems that include typical aleatoric uncertainties emerging from measurement errors and excitation loads. In addition, we propose a new Composite-EDS (C-EDS) method to fuse both online and offline algorithms into a unique simulator. Capitalizing from the results of a more complex case study composed of a coupled isolated tank-piping system, we provide a feasible way to employ the C-EDS method when nonlinearities and multi-point constraints are present in the emulated system.

Spike frequency adaptation is a possible mechanism for control of attractor preference in auto-associative neural networks

NASA Astrophysics Data System (ADS)

Roach, James; Sander, Leonard; Zochowski, Michal

Auto-associative memory is the ability to retrieve a pattern from a small fraction of the pattern and is an important function of neural networks. Within this context, memories that are stored within the synaptic strengths of networks act as dynamical attractors for network firing patterns. In networks with many encoded memories, some attractors will be stronger than others. This presents the problem of how networks switch between attractors depending on the situation. We suggest that regulation of neuronal spike-frequency adaptation (SFA) provides a universal mechanism for network-wide attractor selectivity. Here we demonstrate in a Hopfield type attractor network that neurons minimal SFA will reliably activate in the pattern corresponding to a local attractor and that a moderate increase in SFA leads to the network to converge to the strongest attractor state. Furthermore, we show that on long time scales SFA allows for temporal sequences of activation to emerge. Finally, using a model of cholinergic modulation within the cortex we argue that dynamic regulation of attractor preference by SFA could be critical for the role of acetylcholine in attention or for arousal states in general. This work was supported by: NSF Graduate Research Fellowship Program under Grant No. DGE 1256260 (JPR), NSF CMMI 1029388 (MRZ) and NSF PoLS 1058034 (MRZ & LMS).

The generalized quadratic knapsack problem. A neuronal network approach.

PubMed

Talaván, Pedro M; Yáñez, Javier

2006-05-01

The solution of an optimization problem through the continuous Hopfield network (CHN) is based on some energy or Lyapunov function, which decreases as the system evolves until a local minimum value is attained. A new energy function is proposed in this paper so that any 0-1 linear constrains programming with quadratic objective function can be solved. This problem, denoted as the generalized quadratic knapsack problem (GQKP), includes as particular cases well-known problems such as the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). This new energy function generalizes those proposed by other authors. Through this energy function, any GQKP can be solved with an appropriate parameter setting procedure, which is detailed in this paper. As a particular case, and in order to test this generalized energy function, some computational experiments solving the traveling salesman problem are also included.

Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework.

PubMed

Kwok, T; Smith, K A

2000-09-01

The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.

Self-organizing feature maps for dynamic control of radio resources in CDMA microcellular networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

1998-03-01

The application of artificial neural networks to the channel assignment problem for cellular code-division multiple access (CDMA) cellular networks has previously been investigated. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth-limited. Any reduction in interference in CDMA translates linearly into increased capacity. To satisfy the high demands for new services and improved connectivity for mobile communications, microcellular and picocellular systems are being introduced. For these systems, there is a need to develop robust and efficient management procedures for the allocation of power and spectrum to maximize radio capacity. Topology-conserving mappings play an important role in the biological processing of sensory inputs. The same principles underlying Kohonen's self-organizing feature maps (SOFMs) are applied to the adaptive control of radio resources to minimize interference, hence, maximize capacity in direct-sequence (DS) CDMA networks. The approach based on SOFMs is applied to some published examples of both theoretical and empirical models of DS/CDMA microcellular networks in metropolitan areas. The results of the approach for these examples are informally compared to the performance of algorithms, based on Hopfield- Tank neural networks and on genetic algorithms, for the channel assignment problem.

Three dimensional elements with Lagrange multipliers for the modified couple stress theory

NASA Astrophysics Data System (ADS)

Kwon, Young-Rok; Lee, Byung-Chai

2018-07-01

Three dimensional mixed elements for the modified couple stress theory are proposed. The C1 continuity for the displacement field, which is required because of the curvature term in the variational form of the theory, is satisfied weakly by introducing a supplementary rotation as an independent variable and constraining the relation between the rotation and the displacement with a Lagrange multiplier vector. An additional constraint about the deviatoric curvature is also considered for three dimensional problems. Weak forms with one constraint and two constraints are derived, and four elements satisfying convergence criteria are developed by applying different approximations to each field of independent variables. The elements pass a [InlineEquation not available: see fulltext.] patch test for three dimensional problems. Numerical examples show that the additional constraint could be considered essential for the three dimensional elements, and one of the elements is recommended for practical applications via the comparison of the performances of the elements. In addition, all the proposed elements can represent the size effect well.

Adiabatic quantum optimization for associative memory recall

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

Seddiqi, Hadayat; Humble, Travis S.

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

Adiabatic Quantum Optimization for Associative Memory Recall

NASA Astrophysics Data System (ADS)

Seddiqi, Hadayat; Humble, Travis

2014-12-01

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

Adiabatic quantum optimization for associative memory recall

DOE PAGES

Seddiqi, Hadayat; Humble, Travis S.

2014-12-22

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

Complex dynamics of a delayed discrete neural network of two nonidentical neurons.

PubMed

Chen, Yuanlong; Huang, Tingwen; Huang, Yu

2014-03-01

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291-303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415-432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869-1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

Distributed-Lagrange-Multiplier-based computational method for particulate flow with collisions

NASA Astrophysics Data System (ADS)

Ardekani, Arezoo; Rangel, Roger

2006-11-01

A Distributed-Lagrange-Multiplier-based computational method is developed for colliding particles in a solid-fluid system. A numerical simulation is conducted in two dimensions using the finite volume method. The entire domain is treated as a fluid but the fluid in the particle domains satisfies a rigidity constraint. We present an efficient method for predicting the collision between particles. In earlier methods, a repulsive force was applied to the particles when their distance was less than a critical value. In this method, an impulsive force is computed. During the frictionless collision process between two particles, linear momentum is conserved while the tangential forces are zero. Thus, instead of satisfying a condition of rigid body motion for each particle separately, as done when particles are not in contact, both particles are rigidified together along their line of centers. Particles separate from each other when the impulsive force is less than zero and after this time, a rigidity constraint is satisfied for each particle separately. Grid independency is implemented to ensure the accuracy of the numerical simulation. A comparison between this method and previous collision strategies is presented and discussed.

The Ising Decision Maker: a binary stochastic network for choice response time.

PubMed

Verdonck, Stijn; Tuerlinckx, Francis

2014-07-01

The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. (c) 2014 APA, all rights reserved.

Wavelet evolutionary network for complex-constrained portfolio rebalancing

NASA Astrophysics Data System (ADS)

Suganya, N. C.; Vijayalakshmi Pai, G. A.

2012-07-01

Portfolio rebalancing problem deals with resetting the proportion of different assets in a portfolio with respect to changing market conditions. The constraints included in the portfolio rebalancing problem are basic, cardinality, bounding, class and proportional transaction cost. In this study, a new heuristic algorithm named wavelet evolutionary network (WEN) is proposed for the solution of complex-constrained portfolio rebalancing problem. Initially, the empirical covariance matrix, one of the key inputs to the problem, is estimated using the wavelet shrinkage denoising technique to obtain better optimal portfolios. Secondly, the complex cardinality constraint is eliminated using k-means cluster analysis. Finally, WEN strategy with logical procedures is employed to find the initial proportion of investment in portfolio of assets and also rebalance them after certain period. Experimental studies of WEN are undertaken on Bombay Stock Exchange, India (BSE200 index, period: July 2001-July 2006) and Tokyo Stock Exchange, Japan (Nikkei225 index, period: March 2002-March 2007) data sets. The result obtained using WEN is compared with the only existing counterpart named Hopfield evolutionary network (HEN) strategy and also verifies that WEN performs better than HEN. In addition, different performance metrics and data envelopment analysis are carried out to prove the robustness and efficiency of WEN over HEN strategy.

Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics

PubMed Central

Zou, Weiyao; Burns, Stephen A.

2012-01-01

A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. PMID:22441462

Distributed Multi-Cell Resource Allocation with Price Based ICI Coordination in Downlink OFDMA Networks

NASA Astrophysics Data System (ADS)

Lv, Gangming; Zhu, Shihua; Hui, Hui

Multi-cell resource allocation under minimum rate request for each user in OFDMA networks is addressed in this paper. Based on Lagrange dual decomposition theory, the joint multi-cell resource allocation problem is decomposed and modeled as a limited-cooperative game, and a distributed multi-cell resource allocation algorithm is thus proposed. Analysis and simulation results show that, compared with non-cooperative iterative water-filling algorithm, the proposed algorithm can remarkably reduce the ICI level and improve overall system performances.

Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

NASA Astrophysics Data System (ADS)

Vasant, P.; Ganesan, T.; Elamvazuthi, I.

2012-11-01

A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics.

PubMed

Zou, Weiyao; Burns, Stephen A

2012-03-20

A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. © 2012 Optical Society of America

A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

PubMed

Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju

2004-10-01

Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

Complex dynamics of a delayed discrete neural network of two nonidentical neurons

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

Chen, Yuanlong; Huang, Tingwen; Huang, Yu, E-mail: stshyu@mail.sysu.edu.cn

2014-03-15

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zoumore » [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results.« less

Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties

PubMed Central

Xu, Yongjun; Hu, Yuan; Li, Guoquan

2018-01-01

Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance. PMID:29466315

Predicting the safe load on backpacker's arm using Lagrange multipliers method

NASA Astrophysics Data System (ADS)

Abdalla, Faisal Saleh; Rambely, Azmin Sham

2014-09-01

In this study, a technique has been suggested to reduce a backpack load by transmitting determined loads to the children arm. The purpose of this paper is to estimate school children arm muscles while load carriage as well as to determine the safe load can be carried at wrist while walking with backpack. A mathematical model, as three DOFs model, was investigated in the sagittal plane and Lagrange multipliers method (LMM) was utilized to minimize a quadratic objective function of muscle forces. The muscle forces were minimized with three different load conditions which are termed as 0-L=0 N, 1-L=21.95 N, and 2-L=43.9 N. The investigated muscles were estimated and compared to their maximum forces throughout the load conditions. Flexor and extensor muscles were estimated and the results showed that flexor muscles were active while extensor muscles showed inactivity. The estimated muscle forces were didn't exceed their maximum forces with 0-L and 1-L conditions whereas biceps and FCR muscles exceeded their maximum forces with 2-L condition. Consequently, 1-L condition is quiet safe to be carried by hand whereas 2-L condition is not. Thus to reduce the load in the backpack the transmitted load shouldn't exceed 1-L condition.

Employing Tropospheric Numerical Weather Prediction Model for High-Precision GNSS Positioning

NASA Astrophysics Data System (ADS)

Alves, Daniele; Gouveia, Tayna; Abreu, Pedro; Magário, Jackes

2014-05-01

In the past few years is increasing the necessity of realizing high accuracy positioning. In this sense, the spatial technologies have being widely used. The GNSS (Global Navigation Satellite System) has revolutionized the geodetic positioning activities. Among the existent methods one can emphasize the Precise Point Positioning (PPP) and network-based positioning. But, to get high accuracy employing these methods, mainly in real time, is indispensable to realize the atmospheric modeling (ionosphere and troposphere) accordingly. Related to troposphere, there are the empirical models (for example Saastamoinen and Hopfield). But when highly accuracy results (error of few centimeters) are desired, maybe these models are not appropriated to the Brazilian reality. In order to minimize this limitation arises the NWP (Numerical Weather Prediction) models. In Brazil the CPTEC/INPE (Center for Weather Prediction and Climate Studies / Brazilian Institute for Spatial Researches) provides a regional NWP model, currently used to produce Zenithal Tropospheric Delay (ZTD) predictions (http://satelite.cptec.inpe.br/zenital/). The actual version, called eta15km model, has a spatial resolution of 15 km and temporal resolution of 3 hours. In this paper the main goal is to accomplish experiments and analysis concerning the use of troposphere NWP model (eta15km model) in PPP and network-based positioning. Concerning PPP it was used data from dozens of stations over the Brazilian territory, including Amazon forest. The results obtained with NWP model were compared with Hopfield one. NWP model presented the best results in all experiments. Related to network-based positioning it was used data from GNSS/SP Network in São Paulo State, Brazil. This network presents the best configuration in the country to realize this kind of positioning. Actually the network is composed by twenty stations (http://www.fct.unesp.br/#!/pesquisa/grupos-de-estudo-e-pesquisa/gege//gnss-sp-network2789/). The

Relativistic semiempirical-core-potential calculations in Ca+,Sr+ , and Ba+ ions on Lagrange meshes

NASA Astrophysics Data System (ADS)

Filippin, Livio; Schiffmann, Sacha; Dohet-Eraly, Jérémy; Baye, Daniel; Godefroid, Michel

2018-01-01

Relativistic atomic structure calculations are carried out in alkaline-earth-metal ions using a semiempirical-core-potential approach. The systems are partitioned into frozen-core electrons and an active valence electron. The core orbitals are defined by a Dirac-Hartree-Fock calculation using the grasp2k package. The valence electron is described by a Dirac-like Hamiltonian involving a core-polarization potential to simulate the core-valence electron correlation. The associated equation is solved with the Lagrange-mesh method, which is an approximate variational approach having the form of a mesh calculation because of the use of a Gauss quadrature to calculate matrix elements. Properties involving the low-lying metastable D 3 /2 ,5 /2 2 states of Ca+, Sr+, and Ba+ are studied, such as polarizabilities, one- and two-photon decay rates, and lifetimes. Good agreement is found with other theory and observation, which is promising for further applications in alkalilike systems.

Optical neural network system for pose determination of spinning satellites

NASA Technical Reports Server (NTRS)

Lee, Andrew; Casasent, David

1990-01-01

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

Woofer-tweeter adaptive optics scanning laser ophthalmoscopic imaging based on Lagrange-multiplier damped least-squares algorithm.

PubMed

Zou, Weiyao; Qi, Xiaofeng; Burns, Stephen A

2011-07-01

We implemented a Lagrange-multiplier (LM)-based damped least-squares (DLS) control algorithm in a woofer-tweeter dual deformable-mirror (DM) adaptive optics scanning laser ophthalmoscope (AOSLO). The algorithm uses data from a single Shack-Hartmann wavefront sensor to simultaneously correct large-amplitude low-order aberrations by a woofer DM and small-amplitude higher-order aberrations by a tweeter DM. We measured the in vivo performance of high resolution retinal imaging with the dual DM AOSLO. We compared the simultaneous LM-based DLS dual DM controller with both single DM controller, and a successive dual DM controller. We evaluated performance using both wavefront (RMS) and image quality metrics including brightness and power spectrum. The simultaneous LM-based dual DM AO can consistently provide near diffraction-limited in vivo routine imaging of human retina.

Adapting the Euler-Lagrange equation to study one-dimensional motions under the action of a constant force

NASA Astrophysics Data System (ADS)

Dias, Clenilda F.; Araújo, Maria A. S.; Carvalho-Santos, Vagson L.

2018-01-01

The Euler-Lagrange equations (ELE) are very important in the theoretical description of several physical systems. In this work we have used a simplified form of ELE to study one-dimensional motions under the action of a constant force. From the use of the definition of partial derivative, we have proposed two operators, here called mean delta operators, which may be used to solve the ELE in a simplest way. We have applied this simplification to solve three simple mechanical problems in which the particle is under the action of the gravitational field: a free fall body, the Atwood’s machine and the inclined plan. The proposed simplification can be used to introduce the lagrangian formalism in teaching classical mechanics in introductory physics courses.

An Euler-Lagrange method considering bubble radial dynamics for modeling sonochemical reactors.

PubMed

Jamshidi, Rashid; Brenner, Gunther

2014-01-01

Unsteady numerical computations are performed to investigate the flow field, wave propagation and the structure of bubbles in sonochemical reactors. The turbulent flow field is simulated using a two-equation Reynolds-Averaged Navier-Stokes (RANS) model. The distribution of the acoustic pressure is solved based on the Helmholtz equation using a finite volume method (FVM). The radial dynamics of a single bubble are considered by applying the Keller-Miksis equation to consider the compressibility of the liquid to the first order of acoustical Mach number. To investigate the structure of bubbles, a one-way coupling Euler-Lagrange approach is used to simulate the bulk medium and the bubbles as the dispersed phase. Drag, gravity, buoyancy, added mass, volume change and first Bjerknes forces are considered and their orders of magnitude are compared. To verify the implemented numerical algorithms, results for one- and two-dimensional simplified test cases are compared with analytical solutions. The results show good agreement with experimental results for the relationship between the acoustic pressure amplitude and the volume fraction of the bubbles. The two-dimensional axi-symmetric results are in good agreement with experimentally observed structure of bubbles close to sonotrode. Copyright © 2013 Elsevier B.V. All rights reserved.

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

PubMed

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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

Lee, Y.C.; Doolen, G.; Chen, H.H.

A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

Generalized concurrence measure for faithful quantification of multiparticle pure state entanglement using Lagrange's identity and wedge product

NASA Astrophysics Data System (ADS)

Bhaskara, Vineeth S.; Panigrahi, Prasanta K.

2017-05-01

Concurrence, introduced by Hill and Wootters (Phys Rev Lett 78:5022, 1997), provides an important measure of entanglement for a general pair of qubits that is faithful: strictly positive for entangled states and vanishing for all separable states. Such a measure captures the entire content of entanglement, providing necessary and sufficient conditions for separability. We present an extension of concurrence to multiparticle pure states in arbitrary dimensions by a new framework using the Lagrange's identity and wedge product representation of separability conditions, which coincides with the "I-concurrence" of Rungta et al. (Phys Rev A 64:042315, 2001) who proposed by extending Wootters's spin-flip operator to a so-called universal inverter superoperator. Our framework exposes an inherent geometry of entanglement and may be useful for the further extensions to mixed and continuous variable states.

Transit detection of a `starshade' at the inner lagrange point of an exoplanet

NASA Astrophysics Data System (ADS)

Gaidos, E.

2017-08-01

All water-covered rocky planets in the inner habitable zones of solar-type stars will inevitably experience a catastrophic runaway climate due to increasing stellar luminosity and limits to outgoing infrared radiation from wet greenhouse atmospheres. Reflectors or scatterers placed near Earth's inner Lagrange point (L_1) have been proposed as a "geoengineering' solution to anthropogenic climate change and an advanced version of this could modulate incident irradiation over many Gyr or `rescue' a planet from the interior of the habitable zone. The distance of the starshade from the planet that minimizes its mass is 1.6 times the Earth-L_1 distance. Such a starshade would have to be similar in size to the planet and the mutual occultations during planetary transits could produce a characteristic maximum at mid-transit in the light curve. Because of a fortuitous ratio of densities, Earth-size planets around G dwarf stars present the best opportunity to detect such an artefact. The signal would be persistent and is potentially detectable by a future space photometry mission to characterize transiting planets. The signal could be distinguished from natural phenomenon, I.e. starspots or cometary dust clouds, by its shape, persistence and transmission spectrum.

Elements of the cellular metabolic structure

PubMed Central

De la Fuente, Ildefonso M.

2015-01-01

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell. PMID:25988183

Analysis of complex elastic structures by a Rayleigh-Ritz component modes method using Lagrange multipliers. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Klein, L. R.

1974-01-01

The free vibrations of elastic structures of arbitrary complexity were analyzed in terms of their component modes. The method was based upon the use of the normal unconstrained modes of the components in a Rayleigh-Ritz analysis. The continuity conditions were enforced by means of Lagrange Multipliers. Examples of the structures considered are: (1) beams with nonuniform properties; (2) airplane structures with high or low aspect ratio lifting surface components; (3) the oblique wing airplane; and (4) plate structures. The method was also applied to the analysis of modal damping of linear elastic structures. Convergence of the method versus the number of modes per component and/or the number of components is discussed and compared to more conventional approaches, ad-hoc methods, and experimental results.

Evaluation of a transient, simultaneous, arbitrary Lagrange-Euler based multi-physics method for simulating the mitral heart valve.

PubMed

Espino, Daniel M; Shepherd, Duncan E T; Hukins, David W L

2014-01-01

A transient multi-physics model of the mitral heart valve has been developed, which allows simultaneous calculation of fluid flow and structural deformation. A recently developed contact method has been applied to enable simulation of systole (the stage when blood pressure is elevated within the heart to pump blood to the body). The geometry was simplified to represent the mitral valve within the heart walls in two dimensions. Only the mitral valve undergoes deformation. A moving arbitrary Lagrange-Euler mesh is used to allow true fluid-structure interaction (FSI). The FSI model requires blood flow to induce valve closure by inducing strains in the region of 10-20%. Model predictions were found to be consistent with existing literature and will undergo further development.

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

Sathasivam, Saratha

New activation function is examined for its ability to accelerate the performance of doing logic programming in Hopfield network. This method has a higher capacity and upgrades the neuro symbolic integration. Computer simulations are carried out to validate the effectiveness of the new activation function. Empirical results obtained support our theory.

Two neural network algorithms for designing optimal terminal controllers with open final time

NASA Technical Reports Server (NTRS)

Plumer, Edward S.

1992-01-01

Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

NASA Astrophysics Data System (ADS)

Calvo-Fullana, Miguel; Anton-Haro, Carles; Matamoros, Javier; Ribeiro, Alejandro

2018-07-01

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.

A generalized locomotion CPG architecture based on oscillatory building blocks.

PubMed

Yang, Zhijun; França, Felipe M G

2003-07-01

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines

PubMed Central

Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

2016-01-01

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. PMID:26828500

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines.

PubMed

Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

2016-01-28

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes' placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Convergence dynamics and pseudo almost periodicity of a class of nonautonomous RFDEs with applications

NASA Astrophysics Data System (ADS)

Fan, Meng; Ye, Dan

2005-09-01

This paper studies the dynamics of a system of retarded functional differential equations (i.e., RF=Es), which generalize the Hopfield neural network models, the bidirectional associative memory neural networks, the hybrid network models of the cellular neural network type, and some population growth model. Sufficient criteria are established for the globally exponential stability and the existence and uniqueness of pseudo almost periodic solution. The approaches are based on constructing suitable Lyapunov functionals and the well-known Banach contraction mapping principle. The paper ends with some applications of the main results to some neural network models and population growth models and numerical simulations.

Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks.

PubMed

Liang, Junli; Yu, Guoyang; Chen, Badong; Zhao, Minghua

2016-11-01

This paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks. The main contributions of this paper are as follows. First, conventional centralized methods, which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode, are not suitable for the network environment. Here, we relax the simultaneous processing manner into the one-vector-by-one-vector (OVBOV) manner, i.e., determining the projection vectors (PVs) related to each tensor mode one by one. Second, we prove that in the OVBOV manner each PV can be determined without modifying any tensor data, which simplifies corresponding computations. Third, we cast the decentralized PV determination problem as a set of subproblems with consensus constraints, so that it can be solved in the network environment only by local computations and information communications among neighboring nodes. Fourth, we introduce the null space and transform the PV determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint, which can be solved by the Lagrange multiplier method. Finally, experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks.

A low diffusive Lagrange-remap scheme for the simulation of violent air-water free-surface flows

NASA Astrophysics Data System (ADS)

Bernard-Champmartin, Aude; De Vuyst, Florian

2014-10-01

In 2002, Després and Lagoutière [17] proposed a low-diffusive advection scheme for pure transport equation problems, which is particularly accurate for step-shaped solutions, and thus suited for interface tracking procedure by a color function. This has been extended by Kokh and Lagoutière [28] in the context of compressible multifluid flows using a five-equation model. In this paper, we explore a simplified variant approach for gas-liquid three-equation models. The Eulerian numerical scheme has two ingredients: a robust remapped Lagrange solver for the solution of the volume-averaged equations, and a low diffusive compressive scheme for the advection of the gas mass fraction. Numerical experiments show the performance of the computational approach on various flow reference problems: dam break, sloshing of a tank filled with water, water-water impact and finally a case of Rayleigh-Taylor instability. One of the advantages of the present interface capturing solver is its natural implementation on parallel processors or computers.

Complex-valued Multidirectional Associative Memory

NASA Astrophysics Data System (ADS)

Kobayashi, Masaki; Yamazaki, Haruaki

Hopfield model is a representative associative memory. It was improved to Bidirectional Associative Memory(BAM) by Kosko and Multidirectional Associative Memory(MAM) by Hagiwara. They have two layers or multilayers. Since they have symmetric connections between layers, they ensure to converge. MAM can deal with multiples of many patterns, such as (x1, x2,…), where xm is the pattern on layer-m. Noest, Hirose and Nemoto proposed complex-valued Hopfield model. Lee proposed complex-valued Bidirectional Associative Memory. Zemel proved the rotation invariance of complex-valued Hopfield model. It means that the rotated pattern also stored. In this paper, the complex-valued Multidirectional Associative Memory is proposed. The rotation invariance is also proved. Moreover it is shown by computer simulation that the differences of angles of given patterns are automatically reduced. At first we define complex-valued Multidirectional Associative Memory. Then we define the energy function of network. By using energy function, we prove that the network ensures to converge. Next, we define the learning law and show the characteristic of recall process. The characteristic means that the differences of angles of given patterns are automatically reduced. Especially we prove the following theorem. In case that only a multiple of patterns is stored, if patterns with different angles are given to each layer, the differences are automatically reduced. Finally, we invest that the differences of angles influence the noise robustness. It reduce the noise robustness, because input to each layer become small. We show that by computer simulations.

Neural nets on the MPP

NASA Technical Reports Server (NTRS)

Hastings, Harold M.; Waner, Stefan

1987-01-01

The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman.

Science Enabled by the Ares V: A Large Monolithic Telescope Placed at the Second Sun-Earth Lagrange Point

NASA Technical Reports Server (NTRS)

Hopkins, Randall C.; Stahl, H. Philip

2007-01-01

The payload mass and volume capabilities of the planned Ares V launch vehicle provide the science community with unprecedented opportunities to place large science payloads into low earth orbit and beyond. One example, the outcome of a recent study conducted at the NASA Marshall Space Flight Center, is a large, monolithic telescope with a primary mirror diameter of 6.2 meters placed into a halo orbit about the second Sun-Earth Lagrange point, or L2, approximately 1.5 million kin beyond Earth's orbit. Operating in the visible and ultraviolet regions of the electromagnetic spectrum, such a large telescope would allow astronomers to detect bio-signatures and characterize the atmospheres of transiting exoplanets, provide high resolution imaging three or more times better than the Hubble Space Telescope and the James Webb Space Telescope, and observe the ultraviolet light from warm baryonic matter.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

PubMed Central

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-01-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

PubMed

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-08-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored

Revisiting the Tale of Hercules: How Stars Orbiting the Lagrange Points Visit the Sun

NASA Astrophysics Data System (ADS)

Pérez-Villegas, Angeles; Portail, Matthieu; Wegg, Christopher; Gerhard, Ortwin

2017-05-01

We propose a novel explanation for the Hercules stream consistent with recent measurements of the extent and pattern speed of the Galactic bar. We have adapted a made-to-measure dynamical model tailored for the Milky Way to investigate the kinematics of the solar neighborhood (SNd). The model matches the 3D density of the red clump giant stars (RCGs) in the bulge and bar as well as stellar kinematics in the inner Galaxy, with a pattern speed of 39 km s-1 kpc-1. Cross-matching this model with the Gaia DR1 TGAS data combined with RAVE and LAMOST radial velocities, we find that the model naturally predicts a bimodality in the U-V-velocity distribution for nearby stars which is in good agreement with the Hercules stream. In the model, the Hercules stream is made of stars orbiting the Lagrange points of the bar which move outward from the bar’s corotation radius to visit the SNd. While the model is not yet a quantitative fit of the velocity distribution, the new picture naturally predicts that the Hercules stream is more prominent inward from the Sun and nearly absent only a few 100 pc outward of the Sun, and plausibly explains that Hercules is prominent in old and metal-rich stars.

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

Modelling of Dynamics of a Wheeled Mobile Robot with Mecanum Wheels with the use of Lagrange Equations of the Second Kind

NASA Astrophysics Data System (ADS)

Hendzel, Z.; Rykała, Ł.

2017-02-01

The work presents the dynamic equations of motion of a wheeled mobile robot with mecanum wheels derived with the use of Lagrange equations of the second kind. Mecanum wheels are a new type of wheels used in wheeled mobile robots and they consist of freely rotating rollers attached to the circumference of the wheels. In order to derive dynamic equations of motion of a wheeled mobile robot, the kinetic energy of the system is determined, as well as the generalised forces affecting the system. The resulting mathematical model of a wheeled mobile robot was generated with the use of Maple V software. The results of a solution of inverse and forward problems of dynamics of the discussed object are also published.

Pattern recognition and classification of vibrational spectra by artificial neural networks

NASA Astrophysics Data System (ADS)

Yang, Husheng

1999-10-01

A drawback of current open-path Fourier transform infrared (OP/FT-IR) systems is that they need a human expert to determine those compounds that may be quantified from a given spectrum. In this study, three types of artificial neural networks were used to alleviate this problem. Firstly, multi-layer feed-forward neural networks were used to automatically recognize compounds in an OP/FT-IR spectrum. Each neural network was trained to recognize one compound in the presence of up to ten interferents in an OP/FT-IR spectrum. The networks were successfully used to recognize five alcohols and two chlorinated compounds in field-measured controlled-release OP/FT-IR spectra of mixtures of these compounds. It has also been demonstrated that a neural network could correctly identify a spectrum in the presence of an interferent that was not included in the training set and could also reject interferents it has not seen before. Secondly, the possibility of using one- and two- dimensional Kohonen self-organizing maps (SOMs) to recognize similarities in low-resolution vapor-phase infrared spectra without any additional information has been investigated. Both full-range reference spectra and open-path window reference spectra were used to train the networks and the trained networks were then used to classify the reference spectra into several groups. The results showed that the SOMs obtained from the two different training sets were quite different, and it is more appropriate to use the second SOM in OP/FT-IR spectrometry. Thirdly, vapor-phase FT-IR reference spectra of five alcohols along with four baseline spectra were encoded as prototype vectors for a Hopfield network. Inclusion of the baseline spectra allowed the network to classify spectra as unknowns, when the reference spectra of these compounds were not stored as prototype vectors in the network. The network could identify each of the 5 alcohols correctly even in the presence of noise and interfering compounds. Finally

NDRAM: nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns.

PubMed

Chartier, Sylvain; Proulx, Robert

2005-11-01

This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.

Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks

PubMed Central

Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan

2017-01-01

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint. PMID:28869546

A Two-dimensional Version of the Niblett-Bostick Transformation for Magnetotelluric Interpretations

NASA Astrophysics Data System (ADS)

Esparza, F.

2005-05-01

An imaging technique for two-dimensional magnetotelluric interpretations is developed following the well known Niblett-Bostick transformation for one-dimensional profiles. The algorithm uses a Hopfield artificial neural network to process series and parallel magnetotelluric impedances along with their analytical influence functions. The adaptive, weighted average approximation preserves part of the nonlinearity of the original problem. No initial model in the usual sense is required for the recovery of a functional model. Rather, the built-in relationship between model and data considers automatically, all at the same time, many half spaces whose electrical conductivities vary according to the data. The use of series and parallel impedances, a self-contained pair of invariants of the impedance tensor, avoids the need to decide on best angles of rotation for TE and TM separations. Field data from a given profile can thus be fed directly into the algorithm without much processing. The solutions offered by the Hopfield neural network correspond to spatial averages computed through rectangular windows that can be chosen at will. Applications of the algorithm to simple synthetic models and to the COPROD2 data set illustrate the performance of the approximation.

Vision-Based Navigation and Parallel Computing

DTIC Science & Technology

1990-08-01

33 5.8. Behizad Kamgar-Parsi and Behrooz Karngar-Parsi,"On Problem 5- lving with Hopfield Neural Networks", CAR-TR-462, CS-TR...Second. the hypercube connections support logarithmic implementations of fundamental parallel algorithms. such as grid permutations and scan...the pose space. It also uses a set of virtual processors to represent an orthogonal projection grid , and projections of the six dimensional pose space

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

PubMed

Neftci, Emre O; Pedroni, Bruno U; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

PubMed Central

Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

Algorithm for optimizing bipolar interconnection weights with applications in associative memories and multitarget classification.

PubMed

Chang, S; Wong, K W; Zhang, W; Zhang, Y

1999-08-10

An algorithm for optimizing a bipolar interconnection weight matrix with the Hopfield network is proposed. The effectiveness of this algorithm is demonstrated by computer simulation and optical implementation. In the optical implementation of the neural network the interconnection weights are biased to yield a nonnegative weight matrix. Moreover, a threshold subchannel is added so that the system can realize, in real time, the bipolar weighted summation in a single channel. Preliminary experimental results obtained from the applications in associative memories and multitarget classification with rotation invariance are shown.

Algorithm for Optimizing Bipolar Interconnection Weights with Applications in Associative Memories and Multitarget Classification

NASA Astrophysics Data System (ADS)

Chang, Shengjiang; Wong, Kwok-Wo; Zhang, Wenwei; Zhang, Yanxin

1999-08-01

An algorithm for optimizing a bipolar interconnection weight matrix with the Hopfield network is proposed. The effectiveness of this algorithm is demonstrated by computer simulation and optical implementation. In the optical implementation of the neural network the interconnection weights are biased to yield a nonnegative weight matrix. Moreover, a threshold subchannel is added so that the system can realize, in real time, the bipolar weighted summation in a single channel. Preliminary experimental results obtained from the applications in associative memories and multitarget classification with rotation invariance are shown.

Hetero-association for pattern translation

NASA Astrophysics Data System (ADS)

Yu, Francis T. S.; Lu, Thomas T.; Yang, Xiangyang

1991-09-01

A hetero-association neural network using an interpattern association algorithm is presented. By using simple logical rules, hetero-association memory can be constructed based on the association between the input-output reference patterns. For optical implementation, a compact size liquid crystal television neural network is used. Translations between the English letters and the Chinese characters as well as Arabic and Chinese numerics are demonstrated. The authors have shown that the hetero-association model can perform more effectively in comparison to the Hopfield model in retrieving large numbers of similar patterns.

On the error propagation of semi-Lagrange and Fourier methods for advection problems☆

PubMed Central

Einkemmer, Lukas; Ostermann, Alexander

2015-01-01

In this paper we study the error propagation of numerical schemes for the advection equation in the case where high precision is desired. The numerical methods considered are based on the fast Fourier transform, polynomial interpolation (semi-Lagrangian methods using a Lagrange or spline interpolation), and a discontinuous Galerkin semi-Lagrangian approach (which is conservative and has to store more than a single value per cell). We demonstrate, by carrying out numerical experiments, that the worst case error estimates given in the literature provide a good explanation for the error propagation of the interpolation-based semi-Lagrangian methods. For the discontinuous Galerkin semi-Lagrangian method, however, we find that the characteristic property of semi-Lagrangian error estimates (namely the fact that the error increases proportionally to the number of time steps) is not observed. We provide an explanation for this behavior and conduct numerical simulations that corroborate the different qualitative features of the error in the two respective types of semi-Lagrangian methods. The method based on the fast Fourier transform is exact but, due to round-off errors, susceptible to a linear increase of the error in the number of time steps. We show how to modify the Cooley–Tukey algorithm in order to obtain an error growth that is proportional to the square root of the number of time steps. Finally, we show, for a simple model, that our conclusions hold true if the advection solver is used as part of a splitting scheme. PMID:25844018

Cascaded neural networks for sequenced propagation estimation, multiuser detection, and adaptive radio resource control of third-generation wireless networks for multimedia services

NASA Astrophysics Data System (ADS)

Hortos, William S.

1999-03-01

A hybrid neural network approach is presented to estimate radio propagation characteristics and multiuser interference and to evaluate their combined impact on throughput, latency and information loss in third-generation (3G) wireless networks. The latter three performance parameters influence the quality of service (QoS) for multimedia services under consideration for 3G networks. These networks, based on a hierarchical architecture of overlaying macrocells on top of micro- and picocells, are planned to operate in mobile urban and indoor environments with service demands emanating from circuit-switched, packet-switched and satellite-based traffic sources. Candidate radio interfaces for these networks employ a form of wideband CDMA in 5-MHz and wider-bandwidth channels, with possible asynchronous operation of the mobile subscribers. The proposed neural network (NN) architecture allocates network resources to optimize QoS metrics. Parameters of the radio propagation channel are estimated, followed by control of an adaptive antenna array at the base station to minimize interference, and then joint multiuser detection is performed at the base station receiver. These adaptive processing stages are implemented as a sequence of NN techniques that provide their estimates as inputs to a final- stage Kohonen self-organizing feature map (SOFM). The SOFM optimizes the allocation of available network resources to satisfy QoS requirements for variable-rate voice, data and video services. As the first stage of the sequence, a modified feed-forward multilayer perceptron NN is trained on the pilot signals of the mobile subscribers to estimate the parameters of shadowing, multipath fading and delays on the uplinks. A recurrent NN (RNN) forms the second stage to control base stations' adaptive antenna arrays to minimize intra-cell interference. The third stage is based on a Hopfield NN (HNN), modified to detect multiple users on the uplink radio channels to mitigate multiaccess

Proceedings of the First International Symposium on Explosive Detection Technology

DTIC Science & Technology

1992-05-01

of these per unit volume is C, = 2.0 ct /cm 3/sec. For the 10 metals make the activity concentration significantly seconds that the bag is in the...Hopfield Network CI Chemical Ionization CL Chemiluminescence CLD Chemiluminescence Detector CPU Central Processing Unit CT Computed Tomography CW...NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the Interest of Information exchange. The United

Optical neural net for classifying imaging spectrometer data

NASA Technical Reports Server (NTRS)

Barnard, Etienne; Casasent, David P.

1989-01-01

The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.

The Human Mind As General Problem Solver

NASA Astrophysics Data System (ADS)

Gurr, Henry

2011-10-01

Since leaving U Cal Irvine Neutrino Research, I have been a University Physics Teacher, and an Informal Researcher Of Human Functionality. My talk will share what I discovered about the best ways to learn, many of which are regularities that are to be expected from the Neuronal Network Properties announced in the publications of physicist John Joseph Hopfield. Hopfield's Model of mammalian brain-body, provides solid instructive understanding of how best Learn, Solve Problems, Live! With it we understand many otherwise puzzling features of our intellect! Examples Why 1) Analogies and metaphors powerful in class instruction, ditto poems. 2) Best learning done in physical (Hands-On) situations with tight immediate dynamical feedback such as seen in learning to ride bike, drive car, speak language, etc. 3) Some of the best learning happens in seeming random exploration, bump around, trial and error. 4) Scientific discoveries happen, with no apparent effort, at odd moments. 5) Important discoveries DEPEND on considerable frustrating effort, then Flash of Insight AHA EURIKA.

A mixed analog/digital chaotic neuro-computer system for quadratic assignment problems.

PubMed

Horio, Yoshihiko; Ikeguchi, Tohru; Aihara, Kazuyuki

2005-01-01

We construct a mixed analog/digital chaotic neuro-computer prototype system for quadratic assignment problems (QAPs). The QAP is one of the difficult NP-hard problems, and includes several real-world applications. Chaotic neural networks have been used to solve combinatorial optimization problems through chaotic search dynamics, which efficiently searches optimal or near optimal solutions. However, preliminary experiments have shown that, although it obtained good feasible solutions, the Hopfield-type chaotic neuro-computer hardware system could not obtain the optimal solution of the QAP. Therefore, in the present study, we improve the system performance by adopting a solution construction method, which constructs a feasible solution using the analog internal state values of the chaotic neurons at each iteration. In order to include the construction method into our hardware, we install a multi-channel analog-to-digital conversion system to observe the internal states of the chaotic neurons. We show experimentally that a great improvement in the system performance over the original Hopfield-type chaotic neuro-computer is obtained. That is, we obtain the optimal solution for the size-10 QAP in less than 1000 iterations. In addition, we propose a guideline for parameter tuning of the chaotic neuro-computer system according to the observation of the internal states of several chaotic neurons in the network.

Criterion for correct recalls in associative-memory neural networks

NASA Astrophysics Data System (ADS)

Ji, Han-Bing

1992-12-01

A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Both the Hopfield and multiple training models are instances of the WOPL model with certain sets of learning weights. A necessary condition of choosing learning weights for the convergence property of the WOPL model is obtained through neural dynamics. A criterion for choosing learning weights for correct associative recalls of the fundamental memories is proposed. In this paper, an important parameter called signal to noise ratio gain (SNRG) is devised, and it is found out empirically that SNRGs have their own threshold values which means that any fundamental memory can be correctly recalled when its corresponding SNRG is greater than or equal to its threshold value. Furthermore, a theorem is given and some theoretical results on the conditions of SNRGs and learning weights for good associative recall performance of the WOPL model are accordingly obtained. In principle, when all SNRGs or learning weights chosen satisfy the theoretically obtained conditions, the asymptotic storage capacity of the WOPL model will grow at the greatest rate under certain known stochastic meaning for AMNNs, and thus the WOPL model can achieve correct recalls for all fundamental memories. The representative computer simulations confirm the criterion and theoretical analysis.

A Lattice Boltzmann Fictitious Domain Method for Modeling Red Blood Cell Deformation and Multiple-Cell Hydrodynamic Interactions in Flow

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

Shi, Xing; Lin, Guang; Zou, Jianfeng

To model red blood cell (RBC) deformation in flow, the recently developed LBM-DLM/FD method ([Shi and Lim, 2007)29], derived from the lattice Boltzmann method and the distributed Lagrange multiplier/fictitious domain methodthe fictitious domain method, is extended to employ the mesoscopic network model for simulations of red blood cell deformation. The flow is simulated by the lattice Boltzmann method with an external force, while the network model is used for modeling red blood cell deformation and the fluid-RBC interaction is enforced by the Lagrange multiplier. To validate parameters of the RBC network model, sThe stretching numerical tests on both coarse andmore » fine meshes are performed and compared with the corresponding experimental data to validate the parameters of the RBC network model. In addition, RBC deformation in pipe flow and in shear flow is simulated, revealing the capacity of the current method for modeling RBC deformation in various flows.« less

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks.

PubMed

Lin, Haifeng; Bai, Di; Gao, Demin; Liu, Yunfei

2016-07-30

In Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network's performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Optimal allocation of resources for suppressing epidemic spreading on networks

NASA Astrophysics Data System (ADS)

Chen, Hanshuang; Li, Guofeng; Zhang, Haifeng; Hou, Zhonghuai

2017-07-01

Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve the optimization problem of how best to allocate the limited resources so as to minimize prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that an epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate λ has a maximal threshold λcopt=1 / , where is the average degree of the underlying network. For a weak infection region (λ ≳λcopt ), we combine perturbation theory with the Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For a general infection region (λ >λcopt ), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize prevalence. Finally, we use simulated annealing to validate the theoretical results.

Spacecraft Formation Flying near Sun-Earth L2 Lagrange Point: Trajectory Generation and Adaptive Full-State Feedback Control

NASA Technical Reports Server (NTRS)

Wong, Hong; Kapila, Vikram

2004-01-01

In this paper, we present a method for trajectory generation and adaptive full-state feedback control to facilitate spacecraft formation flying near the Sun-Earth L2 Lagrange point. Specifically, the dynamics of a spacecraft in the neighborhood of a Halo orbit reveals that there exist quasi-periodic orbits surrounding the Halo orbit. Thus, a spacecraft formation is created by placing a leader spacecraft on a desired Halo orbit and placing follower spacecraft on desired quasi-periodic orbits. To produce a formation maintenance controller, we first develop the nonlinear dynamics of a follower spacecraft relative to the leader spacecraft. We assume that the leader spacecraft is on a desired Halo orbit trajectory and the follower spacecraft is to track a desired quasi-periodic orbit surrounding the Halo orbit. Then, we design an adaptive, full-state feedback position tracking controller for the follower spacecraft providing an adaptive compensation for the unknown mass of the follower spacecraft. The proposed control law is simulated for the case of the leader and follower spacecraft pair and is shown to yield global, asymptotic convergence of the relative position tracking errors.

Nitrous oxide emissions estimated with the Carbon Tracker Lagrange regional inversion framework suggest the North American source comes predominantly from agricultural regions

NASA Astrophysics Data System (ADS)

Nevison, C. D.; Andrews, A. E.; Thoning, K. W.; Saikawa, E.; Dlugokencky, E. J.; Sweeney, C.; Benmergui, J. S.

2016-12-01

The Carbon Tracker Lagrange (CTL) regional inversion framework is used to estimate North American nitrous oxide (N2O) emissions of 1.6 ± 0.4 Tg N/yr over 2008-2013. More than half of the North American emissions are estimated to come from the central agricultural belt, extending from southern Canada to Texas, and are strongest in spring and early summer, consistent with a nitrogen fertilizer-driven source. The estimated N2O flux from the Midwestern corn/soybean belt and the more northerly wheat belt corresponds to 5% of synthetic + organic N fertilizer applied to those regions. While earlier regional atmospheric inversion studies have suggested that global inventories such as EDGAR may be underestimating U.S. anthropogenic N2O emissions by a factor of 3 or more, our results, integrated over a full calendar year, are generally consistent with those inventories and with global inverse model results and budget constraints. The CTL framework is a Bayesian method based on footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model applied to atmospheric N2O data from the National Oceanic and Atmospheric Administration (NOAA) Global Greenhouse Gas Reference Network, including surface, aircraft and tall tower platforms. The CTL inversion results are sensitive to the prescribed boundary condition or background value of N2O, which is estimated based on a new Empirical BackGround (EBG) product derived from STILT back trajectories applied to NOAA data. Analysis of the N2O EBG products suggests a significant, seasonally-varying influence on surface N2O data due to the stratospheric influx of N2O-depleted air. Figure 1. Posterior annual mean N2O emissions for 2010 estimated with the CTL regional inversion framework. The locations of NOAA surface and aircraft data used in the inversion are superimposed as black circles and grey triangles, respectively. Mobile surface sites are indicated with asterisks.

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

PubMed Central

Lin, Haifeng; Bai, Di; Gao, Demin; Liu, Yunfei

2016-01-01

In Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network’s performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks. PMID:27483282

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

International Space Station Centrifuge Rotor Models A Comparison of the Euler-Lagrange and the Bond Graph Modeling Approach

NASA Technical Reports Server (NTRS)

Nguyen, Louis H.; Ramakrishnan, Jayant; Granda, Jose J.

2006-01-01

The assembly and operation of the International Space Station (ISS) require extensive testing and engineering analysis to verify that the Space Station system of systems would work together without any adverse interactions. Since the dynamic behavior of an entire Space Station cannot be tested on earth, math models of the Space Station structures and mechanical systems have to be built and integrated in computer simulations and analysis tools to analyze and predict what will happen in space. The ISS Centrifuge Rotor (CR) is one of many mechanical systems that need to be modeled and analyzed to verify the ISS integrated system performance on-orbit. This study investigates using Bond Graph modeling techniques as quick and simplified ways to generate models of the ISS Centrifuge Rotor. This paper outlines the steps used to generate simple and more complex models of the CR using Bond Graph Computer Aided Modeling Program with Graphical Input (CAMP-G). Comparisons of the Bond Graph CR models with those derived from Euler-Lagrange equations in MATLAB and those developed using multibody dynamic simulation at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) are presented to demonstrate the usefulness of the Bond Graph modeling approach for aeronautics and space applications.

Diffuse interface simulation of bubble rising process: a comparison of adaptive mesh refinement and arbitrary lagrange-euler methods

NASA Astrophysics Data System (ADS)

Wang, Ye; Cai, Jiejin; Li, Qiong; Yin, Huaqiang; Yang, Xingtuan

2018-06-01

Gas-liquid two phase flow exists in several industrial processes and light-water reactors (LWRs). A diffuse interface based finite element method with two different mesh generation methods namely, the Adaptive Mesh Refinement (AMR) and the Arbitrary Lagrange Euler (ALE) methods is used to model the shape and velocity changes in a rising bubble. Moreover, the calculating speed and mesh generation strategies of AMR and ALE are contrasted. The simulation results agree with the Bhagat's experiments, indicating that both mesh generation methods can simulate the characteristics of bubble accurately. We concluded that: the small bubble rises as elliptical with oscillation, whereas a larger bubble (11 mm > d > 7 mm) rises with a morphology between the elliptical and cap type with a larger oscillation. When the bubble is large (d > 11 mm), it rises up as a cap type, and the amplitude becomes smaller. Moreover, it takes longer to achieve the stable shape from the ellipsoid to the spherical cap type with the increase of the bubble diameter. The results also show that for smaller diameter case, the ALE method uses fewer grids and has a faster calculation speed, but the AMR method can solve the case of a large geometry deformation efficiently.

Effect of Finite Particle Size on Convergence of Point Particle Models in Euler-Lagrange Multiphase Dispersed Flow

NASA Astrophysics Data System (ADS)

Nili, Samaun; Park, Chanyoung; Haftka, Raphael T.; Kim, Nam H.; Balachandar, S.

2017-11-01

Point particle methods are extensively used in simulating Euler-Lagrange multiphase dispersed flow. When particles are much smaller than the Eulerian grid the point particle model is on firm theoretical ground. However, this standard approach of evaluating the gas-particle coupling at the particle center fails to converge as the Eulerian grid is reduced below particle size. We present an approach to model the interaction between particles and fluid for finite size particles that permits convergence. We use the generalized Faxen form to compute the force on a particle and compare the results against traditional point particle method. We apportion the different force components on the particle to fluid cells based on the fraction of particle volume or surface in the cell. The application is to a one-dimensional model of shock propagation through a particle-laden field at moderate volume fraction, where the convergence is achieved for a well-formulated force model and back coupling for finite size particles. Comparison with 3D direct fully resolved numerical simulations will be used to check if the approach also improves accuracy compared to the point particle model. Work supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program, under Contract No. DE-NA0002378.

Statistical Description of Associative Memory

NASA Astrophysics Data System (ADS)

Samengo, Inés

2003-03-01

The storage of memories, in the brain, induces some kind of modification in the structural and functional properties of a neural network. Here, a few neuropsychological and neurophysiological experiments are reviewed, suggesting that the plastic changes taking place during memory storage are governed, among other things, by the correlations in the activity of a set of neurons. The Hopfield model is briefly described, showing the way the methods of statistical physics can be useful to describe the storage and retrieval of memories.

Emergence of low noise frustrated states in E/I balanced neural networks.

PubMed

Recio, I; Torres, J J

2016-12-01

We study emerging phenomena in binary neural networks where, with a probability c synaptic intensities are chosen according with a Hebbian prescription, and with probability (1-c) there is an extra random contribution to synaptic weights. This new term, randomly taken from a Gaussian bimodal distribution, balances the synaptic population in the network so that one has 80%-20% relation in E/I population ratio, mimicking the balance observed in mammals cortex. For some regions of the relevant parameters, our system depicts standard memory (at low temperature) and non-memory attractors (at high temperature). However, as c decreases and the level of the underlying noise also decreases below a certain temperature T t , a kind of memory-frustrated state, which resembles spin-glass behavior, sharply emerges. Contrary to what occurs in Hopfield-like neural networks, the frustrated state appears here even in the limit of the loading parameter α→0. Moreover, we observed that the frustrated state in fact corresponds to two states of non-vanishing activity uncorrelated with stored memories, associated, respectively, to a high activity or Up state and to a low activity or Down state. Using a linear stability analysis, we found regions in the space of relevant parameters for locally stable steady states and demonstrated that frustrated states coexist with memory attractors below T t . Then, multistability between memory and frustrated states is present for relatively small c, and metastability of memory attractors can emerge as c decreases even more. We studied our system using standard mean-field techniques and with Monte Carlo simulations, obtaining a perfect agreement between theory and simulations. Our study can be useful to explain the role of synapse heterogeneity on the emergence of stable Up and Down states not associated to memory attractors, and to explore the conditions to induce transitions among them, as in sleep-wake transitions. Copyright © 2016 Elsevier Ltd

DSCOVR/EPIC Images and Science: A New Way to View the Entire Sunlit Earth From A Sun-Earth Lagrange-1 Orbit

NASA Astrophysics Data System (ADS)

Herman, J. R.; Marshak, A.; Szabo, A.

2015-12-01

The DSCOVR mission was launched into a Sun-Earth Lagrange-1 orbit 1.5 million kilometers from earth in February 2015 onboard a SpaceX Falcon-9 rocket. The solar wind and earth science instruments were tested during the 4.5 month journey to L-1. The first data were obtained during the June-July commissioning phase, which included the first moderate resolution (10 km) color images of the entire sunlit earth, color images of the Moon, and scientific data from 10 narrow band filters (317.5, 325, 340, 388, 443, 551, 680, 687.75, 764, and 779.5 nm). Three of these filters were used to construct the color images (443, 551, 680 nm) based on the average eye response histogram of the sunlit earth. This talk will discuss some of the issues involved in deriving science quality data for global ozone, the aerosol index (dust, smoke, and volcanic ash), cloud amounts and reflectivity, and cloud height (measured from the O2 A- and B-bands). As with most new satellites, the science data are preliminary.

Lagrange Point Missions: the Key to Next-Generation Integrated Earth Observations. DSCOVR Innovation

NASA Astrophysics Data System (ADS)

Valero, F. P. J.

2016-12-01

From L-1 DSCOVR is capable of new, unique observations potentially conducive to a deeper scientific understanding of the Earth sciences. At L-1 and L-2 the net gravitational pull of the Earth and Sun equals the centripetal force required to orbit the Sun with the same period as the Earth. Satellites at or near L-1 and L-2 keep the same position relative to the Sun and the Earth. DSCOVR does not orbit the Earth but the Sun in synchronism with Earth, acts like a planetoid (orbits the Sun in the ecliptic plane) while acquiring integrated plus spatially and time resolved scientific data as Earth rotates around its axis. Because of the planet's axial tilt relative to the ecliptic plane, the Polar Regions are visible during local summer from L-1 and local winter from L-2 (Fig. 1). DSCOVR's synoptic and continuous observations solve most of the temporal and spatial limitations associated with low Earth (LEO) and Geostationary (GEO) orbits. Two observatories, one at L-1 (daytime) and one at L-2 (nighttime), would acquire minute-by-minute climate quality data for essentially every point on Earth. The integration of L-1, L-2, LEO, and GEO satellites plus the Moon offers new scientific tools and enriched data sets for Earth sciences. Lagrange points observatories are key to next-generation integrated Earth observations. For example, DSCOVR at L-1 views the Earth plus the Moon (a reference) and simultaneously, at one time or another, all LEO and GEO satellites. The L-1 and L-2 satellites would be the link between the Moon, LEO and GEO satellites while providing the data needed to build an integrated Earth observational system. The above properties are the bases for DSCOVR's innovation and scientific approach that systematically observes climate drivers (radiation, aerosols, ozone, clouds, water vapor, vegetation) from L-1 in a way not possible but synergistic with other satellites. Next step: more capable L-1 plus L-2 satellites. The way of the future.

Resonant spatiotemporal learning in large random recurrent networks.

PubMed

Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

2002-09-01

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present

Dynamics of quality as a strategic variable in complex food supply chain network competition: The case of fresh produce

NASA Astrophysics Data System (ADS)

Nagurney, Anna; Besik, Deniz; Yu, Min

2018-04-01

In this paper, we construct a competitive food supply chain network model in which the profit-maximizing producers decide not only as to the volume of fresh produce produced and distributed using various supply chain network pathways, but they also decide, with the associated costs, on the initial quality of the fresh produce. Consumers, in turn, respond to the various producers' product outputs through the prices that they are willing to pay, given also the average quality associated with each producer or brand at the retail outlets. The quality of the fresh produce is captured through explicit formulae that incorporate time, temperature, and other link characteristics with links associated with processing, shipment, storage, etc. Capacities on links are also incorporated as well as upper bounds on the initial product quality of the firms at their production/harvesting sites. The governing concept of the competitive supply chain network model is that of Nash Equilibrium, for which alternative variational inequality formulations are derived, along with existence results. An algorithmic procedure, which can be interpreted as a discrete-time tatonnement process, is then described and applied to compute the equilibrium produce flow patterns and accompanying link Lagrange multipliers in a realistic case study, focusing on peaches, which includes disruptions.

Investigations on bending-torsional vibrations of rotor during rotor-stator rub using Lagrange multiplier method

NASA Astrophysics Data System (ADS)

Mokhtar, Md Asjad; Kamalakar Darpe, Ashish; Gupta, Kshitij

2017-08-01

The ever-increasing need of highly efficient rotating machinery causes reduction in the clearance between rotating and non-rotating parts and increase in the chances of interaction between these parts. The rotor-stator contact, known as rub, has always been recognized as one of the potential causes of rotor system malfunctions and a source of secondary failures. It is one of few causes that influence both lateral and torsional vibrations. In this paper, the rotor stator interaction phenomenon is investigated in the finite element framework using Lagrange multiplier based contact mechanics approach. The stator is modelled as a beam that can respond to axial penetration and lateral friction force during the contact with the rotor. It ensures dynamic stator contact boundary and more realistic contact conditions in contrast to most of the earlier approaches. The rotor bending-torsional mode coupling during contact is considered and the vibration response in bending and torsion are analysed. The effect of parameters such as clearance, friction coefficient and stator stiffness are studied at various operating speeds and it has been found that certain parameter values generate peculiar rub related features. Presence of sub-harmonics in the lateral vibration frequency spectra are prominently observed when the rotor operates near the integer multiple of its lateral critical speed. The spectrum cascade of torsional vibration shows the presence of bending critical speed along with the larger amplitudes of frequencies close to torsional natural frequency of the rotor. When m × 1/n X frequency component of rotational frequency comes closer to the torsional natural frequency, stronger torsional vibration amplitude is noticed in the spectrum cascade. The combined information from the stator vibration and rotor lateral-torsional vibration spectral features is proposed for robust rub identification.

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Eddy interaction model for turbulent suspension in Reynolds-averaged Euler-Lagrange simulations of steady sheet flow

NASA Astrophysics Data System (ADS)

Cheng, Zhen; Chauchat, Julien; Hsu, Tian-Jian; Calantoni, Joseph

2018-01-01

A Reynolds-averaged Euler-Lagrange sediment transport model (CFDEM-EIM) was developed for steady sheet flow, where the inter-granular interactions were resolved and the flow turbulence was modeled with a low Reynolds number corrected k - ω turbulence closure modified for two-phase flows. To model the effect of turbulence on the sediment suspension, the interaction between the turbulent eddies and particles was simulated with an eddy interaction model (EIM). The EIM was first calibrated with measurements from dilute suspension experiments. We demonstrated that the eddy-interaction model was able to reproduce the well-known Rouse profile for suspended sediment concentration. The model results were found to be sensitive to the choice of the coefficient, C0, associated with the turbulence-sediment interaction time. A value C0 = 3 was suggested to match the measured concentration in the dilute suspension. The calibrated CFDEM-EIM was used to model a steady sheet flow experiment of lightweight coarse particles and yielded reasonable agreements with measured velocity, concentration and turbulence kinetic energy profiles. Further numerical experiments for sheet flow suggested that when C0 was decreased to C0 < 3, the simulation under-predicted the amount of suspended sediment in the dilute region and the Schmidt number is over-predicted (Sc > 1.0). Additional simulations for a range of Shields parameters between 0.3 and 1.2 confirmed that CFDEM-EIM was capable of predicting sediment transport rates similar to empirical formulations. Based on the analysis of sediment transport rate and transport layer thickness, the EIM and the resulting suspended load were shown to be important when the fall parameter is less than 1.25.

Associative memory - An optimum binary neuron representation

NASA Technical Reports Server (NTRS)

Awwal, A. A.; Karim, M. A.; Liu, H. K.

1989-01-01

Convergence mechanism of vectors in the Hopfield's neural network is studied in terms of both weights (i.e., inner products) and Hamming distance. It is shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, weights (which in turn depend on the neuron representation) are found to play a more dominant role in the convergence mechanism. Consequently, a new binary neuron representation for associative memory is proposed. With the new neuron representation, the associative memory responds unambiguously to the partial input in retrieving the stored information.

Short-term memory

NASA Astrophysics Data System (ADS)

Toulouse, G.

This is a rather bold attempt to bridge the gap between neuron structure and psychological data. We try to answer the question: Is there a relation between the neuronal connectivity in the human cortex (around 5,000) and the short-term memory capacity (7±2)? Our starting point is the Hopfield model (Hopfield 1982), presented in this volume by D.J. Amit.

Auto- and hetero-associative memory using a 2-D optical logic gate

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1989-01-01

An optical associative memory system suitable for both auto- and hetero-associative recall is demonstrated. This system utilizes Hamming distance as the similarity measure between a binary input and a memory image with the aid of a two-dimensional optical EXCLUSIVE OR (XOR) gate and a parallel electronics comparator module. Based on the Hamming distance measurement, this optical associative memory performs a nearest neighbor search and the result is displayed in the output plane in real-time. This optical associative memory is fast and noniterative and produces no output spurious states as compared with that of the Hopfield neural network model.

Auto- and hetero-associative memory using a 2-D optical logic gate

NASA Astrophysics Data System (ADS)

Chao, Tien-Hsin

1989-06-01

An optical associative memory system suitable for both auto- and hetero-associative recall is demonstrated. This system utilizes Hamming distance as the similarity measure between a binary input and a memory image with the aid of a two-dimensional optical EXCLUSIVE OR (XOR) gate and a parallel electronics comparator module. Based on the Hamming distance measurement, this optical associative memory performs a nearest neighbor search and the result is displayed in the output plane in real-time. This optical associative memory is fast and noniterative and produces no output spurious states as compared with that of the Hopfield neural network model.

Feasibility of cooling the Earth with a cloud of small spacecraft near the inner Lagrange point (L1).

PubMed

Angel, Roger

2006-11-14

If it were to become apparent that dangerous changes in global climate were inevitable, despite greenhouse gas controls, active methods to cool the Earth on an emergency basis might be desirable. The concept considered here is to block 1.8% of the solar flux with a space sunshade orbited near the inner Lagrange point (L1), in-line between the Earth and sun. Following the work of J. Early [Early, JT (1989) J Br Interplanet Soc 42:567-569], transparent material would be used to deflect the sunlight, rather than to absorb it, to minimize the shift in balance out from L1 caused by radiation pressure. Three advances aimed at practical implementation are presented. First is an optical design for a very thin refractive screen with low reflectivity, leading to a total sunshade mass of approximately 20 million tons. Second is a concept aimed at reducing transportation cost to 50 dollars/kg by using electromagnetic acceleration to escape Earth's gravity, followed by ion propulsion. Third is an implementation of the sunshade as a cloud of many spacecraft, autonomously stabilized by modulating solar radiation pressure. These meter-sized "flyers" would be assembled completely before launch, avoiding any need for construction or unfolding in space. They would weigh a gram each, be launched in stacks of 800,000, and remain for a projected lifetime of 50 years within a 100,000-km-long cloud. The concept builds on existing technologies. It seems feasible that it could be developed and deployed in approximately 25 years at a cost of a few trillion dollars, <0.5% of world gross domestic product (GDP) over that time.

Feasibility of cooling the Earth with a cloud of small spacecraft near the inner Lagrange point (L1)

PubMed Central

Angel, Roger

2006-01-01

If it were to become apparent that dangerous changes in global climate were inevitable, despite greenhouse gas controls, active methods to cool the Earth on an emergency basis might be desirable. The concept considered here is to block 1.8% of the solar flux with a space sunshade orbited near the inner Lagrange point (L1), in-line between the Earth and sun. Following the work of J. Early [Early, JT (1989) J Br Interplanet Soc 42:567–569], transparent material would be used to deflect the sunlight, rather than to absorb it, to minimize the shift in balance out from L1 caused by radiation pressure. Three advances aimed at practical implementation are presented. First is an optical design for a very thin refractive screen with low reflectivity, leading to a total sunshade mass of ≈20 million tons. Second is a concept aimed at reducing transportation cost to $50/kg by using electromagnetic acceleration to escape Earth's gravity, followed by ion propulsion. Third is an implementation of the sunshade as a cloud of many spacecraft, autonomously stabilized by modulating solar radiation pressure. These meter-sized “flyers” would be assembled completely before launch, avoiding any need for construction or unfolding in space. They would weigh a gram each, be launched in stacks of 800,000, and remain for a projected lifetime of 50 years within a 100,000-km-long cloud. The concept builds on existing technologies. It seems feasible that it could be developed and deployed in ≈25 years at a cost of a few trillion dollars, <0.5% of world gross domestic product (GDP) over that time. PMID:17085589

The Human Mind As General Problem Solver, Is Observed To Find ``Best'' Solutions, That Correspond To Highest Mental Coherence: Will Discuss ``sing Glass Type Theory'' of Princeton Physicist J J Hopfield, Points To How Best Use Our Own Human Mind!!

NASA Astrophysics Data System (ADS)

Gurr, Henry

2014-03-01

Princeton Physicist J. J. Hopfield's Mathematical Model of the Mammalian Brain, (Similar To Ising Glass Model of a crystal of magnetic spin particles) says our Brain-Work for Memory, Perception, Language, Thinking, etc, (Even the AHA-EUREKA-Flash Of Insight Type Problem Solving), is achieved by our massively inter-connected CNS Neurons ... working together ... MINIMIZING an analog of physical energy ... thus yielding Optimal Solutions: These ``best'' answers, correspond to highest mental coherence, for most facets organism response, beit mental (eg: perception, memory, ideas, thinking, etc) or physical-muscular-actions (eg speaking, tool using, trail following, etc). Our brain is this way, because living creature, MUST be evolved, so they will find & use the best actions, for survival!!! Our human heritage, is to instantly compute near optimal future plans, (mental & physical-muscular), and be able to accomplish plans reliably & efficiently. If you know of book or articles in these topic areas, please email to HenryG--USCA.edu How to work well, with your own ``self'', called mind-body, will follow!! Conjectures: Who is the ``I'' that appears to make decisions? Am ``I'' the master of my domain? Is there an ``I'' or am ``I'' merely an illusion of reality.

Flexible Kernel Memory

PubMed Central

Nowicki, Dimitri; Siegelmann, Hava

2010-01-01

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces. PMID:20552013

Machine parts recognition using a trinary associative memory

NASA Technical Reports Server (NTRS)

Awwal, Abdul Ahad S.; Karim, Mohammad A.; Liu, Hua-Kuang

1989-01-01

The convergence mechanism of vectors in Hopfield's neural network in relation to recognition of partially known patterns is studied in terms of both inner products and Hamming distance. It has been shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, inner product weighting coefficients play a more dominant role in certain data representations for determining the convergence mechanism. A trinary neuron representation for associative memory is found to be more effective for associative recall. Applications of the trinary associative memory to reconstruct machine part images that are partially missing are demonstrated by means of computer simulation as examples of the usefulness of this approach.

Machine Parts Recognition Using A Trinary Associative Memory

NASA Astrophysics Data System (ADS)

Awwal, Abdul A. S.; Karim, Mohammad A.; Liu, Hua-Kuang

1989-05-01

The convergence mechanism of vectors in Hopfield's neural network in relation to recognition of partially known patterns is studied in terms of both inner products and Hamming distance. It has been shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, inner product weighting coefficients play a more dominant role in certain data representations for determining the convergence mechanism. A trinary neuron representation for associative memory is found to be more effective for associative recall. Applications of the trinary associative memory to reconstruct machine part images that are partially missing are demonstrated by means of computer simulation as examples of the usefulness of this approach.

Fabry-Perot confocal resonator optical associative memory

NASA Astrophysics Data System (ADS)

Burns, Thomas J.; Rogers, Steven K.; Vogel, George A.

1993-03-01

A unique optical associative memory architecture is presented that combines the optical processing environment of a Fabry-Perot confocal resonator with the dynamic storage and recall properties of volume holograms. The confocal resonator reduces the size and complexity of previous associative memory architectures by folding a large number of discrete optical components into an integrated, compact optical processing environment. Experimental results demonstrate the system is capable of recalling a complete object from memory when presented with partial information about the object. A Fourier optics model of the system's operation shows it implements a spatially continuous version of a discrete, binary Hopfield neural network associative memory.

A wave shaping approach of ferrite inductors exhibiting hysteresis using orthogonal field bias

NASA Astrophysics Data System (ADS)

Adly, A. A.; Abd-El-Hafiz, S. K.; Mahgoub, A. O.

2018-05-01

Advances in power electronic systems have considerably contributed to a wide spectrum of applications. In most power electronic circuits, inductors play crucial functions. Utilization of ferrite cores becomes a must when large inductances are required. Nevertheless, this results in an additional complexity due to their hysteresis nature. Recently, an efficient approach for modeling vector hysteresis using tri-node Hopfield neural networks (HNNs) has been introduced. This paper presents a wave shaping approach using hollow cylindrical ferrite core inductors having axial and toroidal windings. The approach investigates the possibility of tuning the inductor permeability to minimize circuit harmonics. Details of the approach are given in the paper.

A phenomenological memristor model for short-term/long-term memory

NASA Astrophysics Data System (ADS)

Chen, Ling; Li, Chuandong; Huang, Tingwen; Ahmad, Hafiz Gulfam; Chen, Yiran

2014-08-01

Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett-Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network.

An Interactive Simulation Program for Exploring Computational Models of Auto-Associative Memory.

PubMed

Fink, Christian G

2017-01-01

While neuroscience students typically learn about activity-dependent plasticity early in their education, they often struggle to conceptually connect modification at the synaptic scale with network-level neuronal dynamics, not to mention with their own everyday experience of recalling a memory. We have developed an interactive simulation program (based on the Hopfield model of auto-associative memory) that enables the user to visualize the connections generated by any pattern of neural activity, as well as to simulate the network dynamics resulting from such connectivity. An accompanying set of student exercises introduces the concepts of pattern completion, pattern separation, and sparse versus distributed neural representations. Results from a conceptual assessment administered before and after students worked through these exercises indicate that the simulation program is a useful pedagogical tool for illustrating fundamental concepts of computational models of memory.

Vibration control of uncertain multiple launch rocket system using radial basis function neural network

NASA Astrophysics Data System (ADS)

Li, Bo; Rui, Xiaoting

2018-01-01

Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

Extreme ultraviolet spectra of Venusian airglow observed by EXCEED

NASA Astrophysics Data System (ADS)

Nara, Yusuke; Yoshikawa, Ichiro; Yoshioka, Kazuo; Murakami, Go; Kimura, Tomoki; Yamazaki, Atsushi; Tsuchiya, Fuminori; Kuwabara, Masaki; Iwagami, Naomoto

2018-06-01

Extreme ultraviolet (EUV) spectra of Venus in the wavelength range 520 - 1480 Å with 3 - 4 Å resolutions were obtained in March 2014 by an EUV imaging spectrometer EXCEED (Extreme Ultraviolet Spectroscope for Exospheric Dynamics) on the HISAKI spacecraft. Due to its high sensitivity and long exposure time, many new emission lines and bands were identified. Already known emissions such as the O II 834 Å, O I 989 Å, H ILy - β 1026 Å, and the C I 1277 Å lines (Broadfoot et al., 1974; Bertaux et al., 1980; Feldman et al., 2000) are also detected in the EXCEED spectrum. In addition, N2 band systems such as the Lyman-Birge-Hopfield (a 1Πg - X 1Σg+) (2, 0), (2, 1), (3, 1), (3, 2) and (5, 3) bands, the Birge-Hopfield (b1Πu - X 1 Σg+) (1, 3) band, and the Carroll-Yoshino (c 4‧ 1 Σu+ - X 1Σg+) (0, 0) and (0, 1) bands together are identified for the first time in the Venusian airglow. We also identified the CO Hopfield-Birge (B 1Σ+ - X 1Σ+) (1, 0) band in addition to the already known (0, 0) band, and the CO Hopfield-Birge (C 1Σ+ - X 1Σ+) (0, 1), (0, 2) bands in addition to the already known (0, 0) band (Feldman et al., 2000; Gérard et al., 2011).

The Augmented Lagrange Multipliers Method for Matrix Completion from Corrupted Samplings with Application to Mixed Gaussian-Impulse Noise Removal

PubMed Central

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu

2014-01-01

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the -norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image. PMID:25248103

Modeling the Global Coronal Field with Simulated Synoptic Magnetograms from Earth and the Lagrange Points L3, L4, and L5

NASA Astrophysics Data System (ADS)

Petrie, Gordon; Pevtsov, Alexei; Schwarz, Andrew; DeRosa, Marc

2018-06-01

The solar photospheric magnetic flux distribution is key to structuring the global solar corona and heliosphere. Regular full-disk photospheric magnetogram data are therefore essential to our ability to model and forecast heliospheric phenomena such as space weather. However, our spatio-temporal coverage of the photospheric field is currently limited by our single vantage point at/near Earth. In particular, the polar fields play a leading role in structuring the large-scale corona and heliosphere, but each pole is unobservable for {>} 6 months per year. Here we model the possible effect of full-disk magnetogram data from the Lagrange points L4 and L5, each extending longitude coverage by 60°. Adding data also from the more distant point L3 extends the longitudinal coverage much further. The additional vantage points also improve the visibility of the globally influential polar fields. Using a flux-transport model for the solar photospheric field, we model full-disk observations from Earth/L1, L3, L4, and L5 over a solar cycle, construct synoptic maps using a novel weighting scheme adapted for merging magnetogram data from multiple viewpoints, and compute potential-field models for the global coronal field. Each additional viewpoint brings the maps and models into closer agreement with the reference field from the flux-transport simulation, with particular improvement at polar latitudes, the main source of the fast solar wind.

Modeling the Sedimentation of Red Blood Cells in Flow under Strong External Magnetic Body Force using a Lattice Boltzmann Fictitious Domain Method

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

Shi, Xing; Lin, Guang

To model the sedimentation of the red blood cell (RBC) in a square duct and a circular pipe, the recently developed technique derived from the lattice Boltzmann method and the distributed Lagrange multiplier/fictitious domain method (LBM-DLM/FD) is extended to employ the mesoscopic network model for simulations of the sedimentation of the RBC in flow. The flow is simulated by the lattice Boltzmann method with a strong magnetic body force, while the network model is used for modeling RBC deformation. The fluid-RBC interactions are enforced by the Lagrange multiplier. The sedimentation of the RBC in a square duct and a circularmore » pipe is simulated, revealing the capacity of the current method for modeling the sedimentation of RBC in various flows. Numerical results illustrate that that the terminal setting velocity increases with the increment of the exerted body force. The deformation of the RBC has significant effect on the terminal setting velocity due to the change of the frontal area. The larger the exerted force is, the smaller the frontal area and the larger deformation of the RBC are.« less

Statistical Neurodynamics.

NASA Astrophysics Data System (ADS)

Paine, Gregory Harold

1982-03-01

The primary objective of the thesis is to explore the dynamical properties of small nerve networks by means of the methods of statistical mechanics. To this end, a general formalism is developed and applied to elementary groupings of model neurons which are driven by either constant (steady state) or nonconstant (nonsteady state) forces. Neuronal models described by a system of coupled, nonlinear, first-order, ordinary differential equations are considered. A linearized form of the neuronal equations is studied in detail. A Lagrange function corresponding to the linear neural network is constructed which, through a Legendre transformation, provides a constant of motion. By invoking the Maximum-Entropy Principle with the single integral of motion as a constraint, a probability distribution function for the network in a steady state can be obtained. The formalism is implemented for some simple networks driven by a constant force; accordingly, the analysis focuses on a study of fluctuations about the steady state. In particular, a network composed of N noninteracting neurons, termed Free Thinkers, is considered in detail, with a view to interpretation and numerical estimation of the Lagrange multiplier corresponding to the constant of motion. As an archetypical example of a net of interacting neurons, the classical neural oscillator, consisting of two mutually inhibitory neurons, is investigated. It is further shown that in the case of a network driven by a nonconstant force, the Maximum-Entropy Principle can be applied to determine a probability distribution functional describing the network in a nonsteady state. The above examples are reconsidered with nonconstant driving forces which produce small deviations from the steady state. Numerical studies are performed on simplified models of two physical systems: the starfish central nervous system and the mammalian olfactory bulb. Discussions are given as to how statistical neurodynamics can be used to gain a better

Method for nonlinear optimization for gas tagging and other systems

DOEpatents

Chen, Ting; Gross, Kenny C.; Wegerich, Stephan

1998-01-01

A method and system for providing nuclear fuel rods with a configuration of isotopic gas tags. The method includes selecting a true location of a first gas tag node, selecting initial locations for the remaining n-1 nodes using target gas tag compositions, generating a set of random gene pools with L nodes, applying a Hopfield network for computing on energy, or cost, for each of the L gene pools and using selected constraints to establish minimum energy states to identify optimal gas tag nodes with each energy compared to a convergence threshold and then upon identifying the gas tag node continuing this procedure until establishing the next gas tag node until all remaining n nodes have been established.

How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules

PubMed Central

Zhang, Kechen

2017-01-01

A neural network with symmetric reciprocal connections always admits a Lyapunov function, whose minima correspond to the memory states stored in the network. Networks with suitable asymmetric connections can store and retrieve a sequence of memory patterns, but the dynamics of these networks cannot be characterized as readily as that of the symmetric networks due to the lack of established general methods. Here, a reduction method is developed for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories, as in a Hopfield network. The method projects the original activity pattern of the network to a low-dimensional space such that sequential memory retrievals in the original network correspond to periodic oscillations in the reduced system. The reduced system is self-contained and provides quantitative information about the stability and speed of sequential memory retrievals in the original network. The time evolution of the overlaps between the network state and the stored memory patterns can also be determined from extended reduced systems. The reduction procedure can be summarized by a few reduction rules, which are applied to several network models, including coupled networks and networks with time-delayed connections, and the analytical solutions of the reduced systems are confirmed by numerical simulations of the original networks. Finally, a local learning rule that provides an approximation to the connection weights involving the pseudoinverse is also presented. PMID:24877729

North American CO2 fluxes for 2007-2015 from NOAA's CarbonTracker-Lagrange Regional Inverse Modeling Framework

NASA Astrophysics Data System (ADS)

Andrews, A. E.; Hu, L.; Thoning, K. W.; Nehrkorn, T.; Mountain, M. E.; Jacobson, A. R.; Michalak, A.; Dlugokencky, E. J.; Sweeney, C.; Worthy, D. E. J.; Miller, J. B.; Fischer, M. L.; Biraud, S.; van der Velde, I. R.; Basu, S.; Tans, P. P.

2017-12-01

CarbonTracker-Lagrange (CT-L) is a new high-resolution regional inverse modeling system for improved estimation of North American CO2 fluxes. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We performed a suite of synthetic-data experiments to evaluate a variety of inversion configurations, including (1) solving for scaling factors to an a priori flux versus additive corrections, (2) solving for fluxes at 3-hrly resolution versus at coarser temporal resolution, (3) solving for fluxes at 1o × 1o resolution versus at large eco-regional scales. Our framework explicitly and objectively solves for the optimal solution with a full error covariance matrix with maximum likelihood estimation, thereby enabling rigorous uncertainty estimates for the derived fluxes. In the synthetic-data inversions, we find that solving for weekly scaling factors of a priori Net Ecosystem Exchange (NEE) at 1o × 1o resolution with optimization of diurnal cycles of CO2 fluxes yields faithful retrieval of the specified "true" fluxes as those solved at 3-hrly resolution. In contrast, a scheme that does not allow for optimization of diurnal cycles of CO2 fluxes suffered from larger aggregation errors. We then applied the optimal inversion setup to estimate North American fluxes for 2007-2015 using real atmospheric CO2 observations, multiple prior estimates of NEE, and multiple boundary values estimated from the NOAA's global Eulerian CarbonTracker (CarbonTracker) and from an empirical approach. Our derived North American land CO2 fluxes show larger seasonal amplitude than those estimated from the CarbonTracker, removing seasonal biases in the CarbonTracker's simulated CO2 mole fractions. Independent evaluations using in-situ CO2 eddy covariance flux measurements and independent aircraft profiles also suggest an improved estimation on North

Synoptic ozone, cloud reflectivity, and erythemal irradiance from sunrise to sunset for the whole earth as viewed by the DSCOVR spacecraft from the earth-sun Lagrange 1 orbit

NASA Astrophysics Data System (ADS)

Herman, Jay; Huang, Liang; McPeters, Richard; Ziemke, Jerry; Cede, Alexander; Blank, Karin

2018-01-01

EPIC (Earth Polychromatic Imaging Camera) on board the DSCOVR (Deep Space Climate Observatory) spacecraft is the first earth science instrument located near the earth-sun gravitational plus centrifugal force balance point, Lagrange 1. EPIC measures earth-reflected radiances in 10 wavelength channels ranging from 317.5 to 779.5 nm. Of these channels, four are in the UV range 317.5, 325, 340, and 388 nm, which are used to retrieve O3, 388 nm scene reflectivity (LER: Lambert equivalent reflectivity), SO2, and aerosol properties. These new synoptic quantities are retrieved for the entire sunlit globe from sunrise to sunset multiple times per day as the earth rotates in EPIC's field of view. Retrieved ozone amounts agree with ground-based measurements and satellite data to within 3 %. The ozone amounts and LER are combined to derive the erythemal irradiance for the earth's entire sunlit surface at a nadir resolution of 18 × 18 km2 using a computationally efficient approximation to a radiative transfer calculation of irradiance. The results show very high summertime values of the UV index (UVI) in the Andes and Himalayas (greater than 18), and high values of UVI near the Equator at equinox.

Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

NASA Astrophysics Data System (ADS)

Xi, Yangui; Yu, Yongguang; Zhang, Shuo; Hai, Xudong

2018-01-01

Not Available Project supported by the National Natural Science Foundation of China (Grant Nos. 11371049 and 61772063) and the Fundamental Research Funds for the Central Universities, China (Grant No. 2016JBM070).

From neurons to epidemics: How trophic coherence affects spreading processes.

PubMed

Klaise, Janis; Johnson, Samuel

2016-06-01

Trophic coherence, a measure of the extent to which the nodes of a directed network are organised in levels, has recently been shown to be closely related to many structural and dynamical aspects of complex systems, including graph eigenspectra, the prevalence or absence of feedback cycles, and linear stability. Furthermore, non-trivial trophic structures have been observed in networks of neurons, species, genes, metabolites, cellular signalling, concatenated words, P2P users, and world trade. Here, we consider two simple yet apparently quite different dynamical models-one a susceptible-infected-susceptible epidemic model adapted to include complex contagion and the other an Amari-Hopfield neural network-and show that in both cases the related spreading processes are modulated in similar ways by the trophic coherence of the underlying networks. To do this, we propose a network assembly model which can generate structures with tunable trophic coherence, limiting in either perfectly stratified networks or random graphs. We find that trophic coherence can exert a qualitative change in spreading behaviour, determining whether a pulse of activity will percolate through the entire network or remain confined to a subset of nodes, and whether such activity will quickly die out or endure indefinitely. These results could be important for our understanding of phenomena such as epidemics, rumours, shocks to ecosystems, neuronal avalanches, and many other spreading processes.

Phase transitions in restricted Boltzmann machines with generic priors

NASA Astrophysics Data System (ADS)

Barra, Adriano; Genovese, Giuseppe; Sollich, Peter; Tantari, Daniele

2017-10-01

We study generalized restricted Boltzmann machines with generic priors for units and weights, interpolating between Boolean and Gaussian variables. We present a complete analysis of the replica symmetric phase diagram of these systems, which can be regarded as generalized Hopfield models. We underline the role of the retrieval phase for both inference and learning processes and we show that retrieval is robust for a large class of weight and unit priors, beyond the standard Hopfield scenario. Furthermore, we show how the paramagnetic phase boundary is directly related to the optimal size of the training set necessary for good generalization in a teacher-student scenario of unsupervised learning.

Studies of superresolution range-Doppler imaging

NASA Astrophysics Data System (ADS)

Zhu, Zhaoda; Ye, Zhenru; Wu, Xiaoqing; Yin, Jun; She, Zhishun

1993-02-01

This paper presents three superresolution imaging methods, including the linear prediction data extrapolation DFT (LPDEDFT), the dynamic optimization linear least squares (DOLLS), and the Hopfield neural network nonlinear least squares (HNNNLS). Live data of a metalized scale model B-52 aircraft, mounted on a rotating platform in a microwave anechoic chamber, have in this way been processed, as has a flying Boeing-727 aircraft. The imaging results indicate that, compared to the conventional Fourier method, either higher resolution for the same effective bandwidth of transmitted signals and total rotation angle in imaging, or equal-quality images from smaller bandwidth and total rotation, angle may be obtained by these superresolution approaches. Moreover, these methods are compared in respect of their resolution capability and computational complexity.

Method for nonlinear optimization for gas tagging and other systems

DOEpatents

Chen, T.; Gross, K.C.; Wegerich, S.

1998-01-06

A method and system are disclosed for providing nuclear fuel rods with a configuration of isotopic gas tags. The method includes selecting a true location of a first gas tag node, selecting initial locations for the remaining n-1 nodes using target gas tag compositions, generating a set of random gene pools with L nodes, applying a Hopfield network for computing on energy, or cost, for each of the L gene pools and using selected constraints to establish minimum energy states to identify optimal gas tag nodes with each energy compared to a convergence threshold and then upon identifying the gas tag node continuing this procedure until establishing the next gas tag node until all remaining n nodes have been established. 6 figs.

From neural-based object recognition toward microelectronic eyes

NASA Technical Reports Server (NTRS)

Sheu, Bing J.; Bang, Sa Hyun

1994-01-01

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Structured approaches to large-scale systems: Variational integrators for interconnected Lagrange-Dirac systems and structured model reduction on Lie groups

NASA Astrophysics Data System (ADS)

Parks, Helen Frances

This dissertation presents two projects related to the structured integration of large-scale mechanical systems. Structured integration uses the considerable differential geometric structure inherent in mechanical motion to inform the design of numerical integration schemes. This process improves the qualitative properties of simulations and becomes especially valuable as a measure of accuracy over long time simulations in which traditional Gronwall accuracy estimates lose their meaning. Often, structured integration schemes replicate continuous symmetries and their associated conservation laws at the discrete level. Such is the case for variational integrators, which discretely replicate the process of deriving equations of motion from variational principles. This results in the conservation of momenta associated to symmetries in the discrete system and conservation of a symplectic form when applicable. In the case of Lagrange-Dirac systems, variational integrators preserve a discrete analogue of the Dirac structure preserved in the continuous flow. In the first project of this thesis, we extend Dirac variational integrators to accommodate interconnected systems. We hope this work will find use in the fields of control, where a controlled system can be thought of as a "plant" system joined to its controller, and in the approach of very large systems, where modular modeling may prove easier than monolithically modeling the entire system. The second project of the thesis considers a different approach to large systems. Given a detailed model of the full system, can we reduce it to a more computationally efficient model without losing essential geometric structures in the system? Asked without the reference to structure, this is the essential question of the field of model reduction. The answer there has been a resounding yes, with Principal Orthogonal Decomposition (POD) with snapshots rising as one of the most successful methods. Our project builds on previous work

From neurons to epidemics: How trophic coherence affects spreading processes

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2016-06-01

Trophic coherence, a measure of the extent to which the nodes of a directed network are organised in levels, has recently been shown to be closely related to many structural and dynamical aspects of complex systems, including graph eigenspectra, the prevalence or absence of feedback cycles, and linear stability. Furthermore, non-trivial trophic structures have been observed in networks of neurons, species, genes, metabolites, cellular signalling, concatenated words, P2P users, and world trade. Here, we consider two simple yet apparently quite different dynamical models—one a susceptible-infected-susceptible epidemic model adapted to include complex contagion and the other an Amari-Hopfield neural network—and show that in both cases the related spreading processes are modulated in similar ways by the trophic coherence of the underlying networks. To do this, we propose a network assembly model which can generate structures with tunable trophic coherence, limiting in either perfectly stratified networks or random graphs. We find that trophic coherence can exert a qualitative change in spreading behaviour, determining whether a pulse of activity will percolate through the entire network or remain confined to a subset of nodes, and whether such activity will quickly die out or endure indefinitely. These results could be important for our understanding of phenomena such as epidemics, rumours, shocks to ecosystems, neuronal avalanches, and many other spreading processes.

Complex network problems in physics, computer science and biology

NASA Astrophysics Data System (ADS)

Cojocaru, Radu Ionut

lattice at zero temperature and then we apply this formalism to the K-SAT problem defined in Chapter 1. The phase transition which physicists study often corresponds to a change in the computational complexity of the corresponding computer science problem. Chapter 3 presents phase transitions which are specific to the problems discussed in Chapter 1 and also known results for the K-SAT problem. We discuss the replica method and experimental evidences of replica symmetry breaking. The physics approach to hard problems is based on replica methods which are difficult to understand. In Chapter 4 we develop novel methods for studying hard problems using methods similar to the message passing techniques that were discussed in Chapter 2. Although we concentrated on the symmetric case, cavity methods show promise for generalizing our methods to the un-symmetric case. As has been highlighted by John Hopfield, several key features of biological systems are not shared by physical systems. Although living entities follow the laws of physics and chemistry, the fact that organisms adapt and reproduce introduces an essential ingredient that is missing in the physical sciences. In order to extract information from networks many algorithm have been developed. In Chapter 5 we apply polynomial algorithms like minimum spanning tree in order to study and construct gene regulatory networks from experimental data. As future work we propose the use of algorithms like min-cut/max-flow and Dijkstra for understanding key properties of these networks.

Quantum-holographic and classical Hopfield-like associative nnets: implications for modeling two cognitive modes of consciousness

NASA Astrophysics Data System (ADS)

Rakovic, D.; Dugic, M.

2005-05-01

Quantum bases of consciousness are considered with psychosomatic implications of three front lines of psychosomatic medicine (hesychastic spirituality, holistic Eastern medicine, and symptomatic Western medicine), as well as cognitive implications of two modes of individual consciousness (quantum-coherent transitional and altered states, and classically reduced normal states) alongside with conditions of transformations of one mode into another (considering consciousness quantum-coherence/classical-decoherence acupuncture system/nervous system interaction, direct and reverse, with and without threshold limits, respectively) - by using theoretical methods of associative neural networks and quantum neural holography combined with quantum decoherence theory.

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

Sobral, G. A. Jr.; Vieira, V. M.; Lyra, M. L.

Extending a model due to Derrida, Gardner, and Zippelius, we have studied the recognition ability of an extreme and asymmetrically diluted version of the Hopfield model for associative memory by including the effect of a stimulus in the dynamics of the system. We obtain exact results for the dynamic evolution of the average network superposition. The stimulus field was considered as proportional to the overlapping of the state of the system with a particular stimulated pattern. Two situations were analyzed, namely, the external stimulus acting on the initialization pattern (parallel stimulus) and the external stimulus acting on a pattern orthogonalmore » to the initialization one (orthogonal stimulus). In both cases, we obtained the complete phase diagram in the parameter space composed of the stimulus field, thermal noise, and network capacity. Our results show that the system improves its recognition ability for parallel stimulus. For orthogonal stimulus two recognition phases emerge with the system locking at the initialization or stimulated pattern. We confront our analytical results with numerical simulations for the noiseless case T=0.« less

A network of networks.

PubMed

Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian

2017-04-10

Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

PubMed

Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

2016-04-01

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Social networks in drinking behaviors among Japanese: support network, drinking network, and intervening network].

PubMed

Yoshihara, Chika; Shimizu, Shinji

2005-10-01

The national representative sample was analyzed to examine the relationship between respondents' drinking practice and the social network which was constructed of three different types of network: support network, drinking network, and intervening network. Non-parametric statistical analysis was conducted with chi square method and ANOVA analysis, due to the risk of small samples in some basic tabulation cells. The main results are as follows: (1) In the support network of workplace associates, moderate drinkers enjoyed much more sociable support care than both nondrinkers and hard drinkers, which might suggest a similar effect as the French paradox. Meanwhile in the familial and kinship network, the more intervening care support was provided, the harder respondents' drinking practice. (2) The drinking network among Japanese people for both sexes is likely to be convergent upon certain types of network categories and not decentralized in various categories. This might reflect of the drinking culture of Japan, which permits people to drink everyday as a practice, especially male drinkers. Subsequently, solitary drinking is not optional for female drinkers. (3) Intervening network analysis showed that the harder the respondents' drinking practices, the more frequently their drinking behaviors were checked in almost all the categories of network. A rather complicated gender double-standard was found in the network of hard drinkers with their friends, particularly for female drinkers. Medical professionals played a similar intervening role for men as family and kinship networks but to a less degree than friends for females. The social network is considerably associated with respondents' drinking, providing both sociability for moderate drinkers and intervention for hard drinkers, depending on network categories. To minimize the risk of hard drinking and advance self-healthy drinking there should be more research development on drinking practice and the social network.

1. EXTERIOR VIEW OF 209 WARE STREET LOOKING SOUTH. THIS ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. EXTERIOR VIEW OF 209 WARE STREET LOOKING SOUTH. THIS STRUCTURE WAS ONE OF APPROXIMATELY SEVENTEEN DUPLEXES BUILT AS THE ORIGINAL WORKER HOUSING FOR THE LaGRANGE COTTON MILLS, LATER KNOWN AS CALUMET MILL. LaGRANGE MILLS (1888-89) WAS THE FIRST COTTON MILL IN LaGRANGE. NOTE THE GABLE-ON-HIP ROOF FORM AND TWO IDENTICAL STRUCTURES VISIBLE TO THE LEFT. - 209 Ware Street (House), 209 Ware Street, La Grange, Troup County, GA

Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.

Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and

Vulnerability of network of networks

NASA Astrophysics Data System (ADS)

Havlin, S.; Kenett, D. Y.; Bashan, A.; Gao, J.; Stanley, H. E.

2014-10-01

Our dependence on networks - be they infrastructure, economic, social or others - leaves us prone to crises caused by the vulnerabilities of these networks. There is a great need to develop new methods to protect infrastructure networks and prevent cascade of failures (especially in cases of coupled networks). Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How, and at which cost can one restructure the network such that it will become more robust against malicious attacks? The gradual increase in attacks on the networks society depends on - Internet, mobile phone, transportation, air travel, banking, etc. - emphasize the need to develop new strategies to protect and defend these crucial networks of communication and infrastructure networks. One example is the threat of liquid explosives a few years ago, which completely shut down air travel for days, and has created extreme changes in regulations. Such threats and dangers warrant the need for new tools and strategies to defend critical infrastructure. In this paper we review recent advances in the theoretical understanding of the vulnerabilities of interdependent networks with and without spatial embedding, attack strategies and their affect on such networks of networks as well as recently developed strategies to optimize and repair failures caused by such attacks.

Skylab

NASA Image and Video Library

1972-06-02

Princeton, New Jersey high school student, Alison Hopfield, is greeted by astronauts Russell L. Schweickart (left) and Owen K. Garriott (center) during a tour of the Marshall Space Flight Center (MSFC). Hopfield was among 25 winners of a contest in which some 3,500 high school students proposed experiments for the following year’s Skylab mission. The nationwide scientific competition was sponsored by the National Science Teachers Association and the National Aeronautics and Space Administration (NASA). The winning students, along with their parents and sponsor teachers, visited MSFC where they met with scientists and engineers, participated in design reviews for their experiments, and toured MSFC facilities. Of the 25 students, 6 did not see their experiments conducted on Skylab because the experiments were not compatible with Skylab hardware and timelines. Of the 19 remaining, 11 experiments required the manufacture of additional equipment.

1. STREETSCAPE VIEW OF 208 VINE STREET (FIRST HOUSE ON ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. STREETSCAPE VIEW OF 208 VINE STREET (FIRST HOUSE ON RIGHT) LOOKING WEST. THIS STRUCTURE WAS ONE OF APPROXIMATELY SEVENTEEN DUPLEXES BUILT AS THE ORIGINAL WORKER HOUSING FOR THE LaGRANGE COTTON MILLS, LATER KNOWN AS CALUMET MILL. LaGRANGE MILLS (1888-89) WAS THE FIRST COTTON MILL IN LaGRANGE. NOTE THE GABLE-ON-HIP ROOF FORM AND IDENTICAL STRUCTURES FACING EACH OTHER ALONG BOTH SIDES OF THE NARROW STREET. - 208 Vine Street (House), 208 Vine Street, La Grange, Troup County, GA

Semantic Networks and Social Networks

ERIC Educational Resources Information Center

Downes, Stephen

2005-01-01

Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

PubMed

Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

Modeling the Citation Network by Network Cosmology

PubMed Central

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well. PMID:25807397

Modeling the citation network by network cosmology.

PubMed

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

Interconnecting astronomical networks: evolving from single networks to meta-networks

NASA Astrophysics Data System (ADS)

White, R. R.; Allan, A.; Evans, S.; Vestrand, W. T.; Wren, J.; Wozniak, P.

2006-06-01

Over the past four years we have seen continued advancement in network technology and how those technologies are beginning to enable astronomical science. Even though some sociological aspects are hindering full cooperation between most observatories and telescopes outside of their academic or institutional connections, an unprecedented step during the summer of 2005 was taken towards creating a world-wide interconnection of astronomical assets. The Telescope Alert Operations Network System (TALONS), a centralized server/client bi-directional network developed and operated by Los Alamos National Laboratory, integrated one of its network nodes with a node from the eScience Telescopes for Astronomical Research (eSTAR), a peer-to-peer agent based network developed and operated by The University of Exeter. Each network can act independently, providing support for their direct clients, and by interconnection provide local clients with access to; outside telescope systems, software tools unavailable locally, and the ability to utilize assets far more efficiently, thereby enabling science on a world-wide scale. In this paper we will look at the evolution of these independent networks into the worlds first heterogeneous telescope network and where this may take astronomy in the future. We will also examine those key elements necessary to providing universal communication between diverse astronomical networks.

Topology of the European Network of Earth Observation Networks and the need for an European Network of Networks

NASA Astrophysics Data System (ADS)

Masó, Joan; Serral, Ivette; McCallum, Ian; Blonda, Palma; Plag, Hans-Peter

2016-04-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations" is an H2020 Coordination and Support Action with the primary goal of linking existing Earth Observation networks with science and technology (S&T) communities, the industry sector, the Group on Earth Observations (GEO), and Copernicus. The project will end in February 2017. ConnectinGEO will initiate a European Network of Earth Observation Networks (ENEON) that will encompass space-based, airborne and in-situ observations networks. ENEON will be composed of project partners representing thematic observation networks along with the GEOSS Science and Technology Stakeholder Network, GEO Communities of Practices, Copernicus services, Sentinel missions and in-situ support data representatives, representatives of the European space-based, airborne and in-situ observations networks. This communication presents the complex panorama of Earth Observations Networks in Europe. The list of networks is classified by discipline, variables, geospatial scope, etc. We also capture the membership and relations with other networks and umbrella organizations like GEO. The result is a complex interrelation between networks that can not be clearly expressed in a flat list. Technically the networks can be represented as nodes with relations between them as lines connecting the nodes in a graph. We have chosen RDF as a language and an AllegroGraph 3.3 triple store that is visualized in several ways using for example Gruff 5.7. Our final aim is to identify gaps in the EO Networks and justify the need for a more structured coordination between them.

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Cascading Failures and Recovery in Networks of Networks

NASA Astrophysics Data System (ADS)

Havlin, Shlomo

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

Access Scheme for Controlling Mobile Agents and its Application to Share Medical Information.

PubMed

Liao, Yu-Ting; Chen, Tzer-Shyong; Chen, Tzer-Long; Chung, Yu-Fang; Chen, Yu- Xin; Hwang, Jen-Hung; Wang, Huihui; Wei, Wei

2016-05-01

This study is showing the advantage of mobile agents to conquer heterogeneous system environments and contribute to a virtual integrated sharing system. Mobile agents will collect medical information from each medical institution as a method to achieve the medical purpose of data sharing. Besides, this research also provides an access control and key management mechanism by adopting Public key cryptography and Lagrange interpolation. The safety analysis of the system is based on a network attacker's perspective. The achievement of this study tries to improve the medical quality, prevent wasting medical resources and make medical resources access to appropriate configuration.

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.

Why Network? Theoretical Perspectives on Networking

ERIC Educational Resources Information Center

Muijs, Daniel; West, Mel; Ainscow, Mel

2010-01-01

In recent years, networking and collaboration have become increasingly popular in education. However, there is at present a lack of attention to the theoretical basis of networking, which could illuminate when and when not to network and under what conditions networks are likely to be successful. In this paper, we will attempt to sketch the…

Data center networks and network architecture

NASA Astrophysics Data System (ADS)

Esaki, Hiroshi

2014-02-01

This paper discusses and proposes the architectural framework, which is for data center networks. The data center networks require new technical challenges, and it would be good opportunity to change the functions, which are not need in current and future networks. Based on the observation and consideration on data center networks, this paper proposes; (i) Broadcast-free layer 2 network (i.e., emulation of broadcast at the end-node), (ii) Full-mesh point-to-point pipes, and (iii) IRIDES (Invitation Routing aDvertisement for path Engineering System).

Hacking Social Networks: Examining the Viability of Using Computer Network Attack Against Social Networks

DTIC Science & Technology

2007-03-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited. HACKING SOCIAL NETWORKS : EXAMINING THE...VIABILITY OF USING COMPUTER NETWORK ATTACK AGAINST SOCIAL NETWORKS by Russell G. Schuhart II March 2007 Thesis Advisor: David Tucker Second Reader...Master’s Thesis 4. TITLE AND SUBTITLE: Hacking Social Networks : Examining the Viability of Using Computer Network Attack Against Social Networks 6. AUTHOR

78 FR 43821 - Final Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-22

............ +902 Unincorporated Areas of LaGrange County. Big Long Lake Entire shoreline......... +957 Unincorporated Areas of LaGrange County. Big Turkey Lake Entire shoreline within +932 Unincorporated Areas of... Vertical Datum. + North American Vertical Datum. Depth in feet above ground. [caret] Mean Sea Level...

Exploring network operations for data and information networks

NASA Astrophysics Data System (ADS)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Dirac structures in vakonomic mechanics

NASA Astrophysics Data System (ADS)

Jiménez, Fernando; Yoshimura, Hiroaki

2015-08-01

In this paper, we explore dynamics of the nonholonomic system called vakonomic mechanics in the context of Lagrange-Dirac dynamical systems using a Dirac structure and its associated Hamilton-Pontryagin variational principle. We first show the link between vakonomic mechanics and nonholonomic mechanics from the viewpoints of Dirac structures as well as Lagrangian submanifolds. Namely, we clarify that Lagrangian submanifold theory cannot represent nonholonomic mechanics properly, but vakonomic mechanics instead. Second, in order to represent vakonomic mechanics, we employ the space TQ ×V∗, where a vakonomic Lagrangian is defined from a given Lagrangian (possibly degenerate) subject to nonholonomic constraints. Then, we show how implicit vakonomic Euler-Lagrange equations can be formulated by the Hamilton-Pontryagin variational principle for the vakonomic Lagrangian on the extended Pontryagin bundle (TQ ⊕T∗ Q) ×V∗. Associated with this variational principle, we establish a Dirac structure on (TQ ⊕T∗ Q) ×V∗ in order to define an intrinsic vakonomic Lagrange-Dirac system. Furthermore, we also establish another construction for the vakonomic Lagrange-Dirac system using a Dirac structure on T∗ Q ×V∗, where we introduce a vakonomic Dirac differential. Finally, we illustrate our theory of vakonomic Lagrange-Dirac systems by some examples such as the vakonomic skate and the vertical rolling coin.

Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1996-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Unipolar terminal-attractor based neural associative memory with adaptive threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1993-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Post optimization paradigm in maximum 3-satisfiability logic programming

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

Maximum 3-Satisfiability (MAX-3SAT) is a counterpart of the Boolean satisfiability problem that can be treated as a constraint optimization problem. It deals with a conundrum of searching the maximum number of satisfied clauses in a particular 3-SAT formula. This paper presents the implementation of enhanced Hopfield network in hastening the Maximum 3-Satisfiability (MAX-3SAT) logic programming. Four post optimization techniques are investigated, including the Elliot symmetric activation function, Gaussian activation function, Wavelet activation function and Hyperbolic tangent activation function. The performances of these post optimization techniques in accelerating MAX-3SAT logic programming will be discussed in terms of the ratio of maximum satisfied clauses, Hamming distance and the computation time. Dev-C++ was used as the platform for training, testing and validating our proposed techniques. The results depict the Hyperbolic tangent activation function and Elliot symmetric activation function can be used in doing MAX-3SAT logic programming.

A Series of Computational Neuroscience Labs Increases Comfort with MATLAB.

PubMed

Nichols, David F

2015-01-01

Computational simulations allow for a low-cost, reliable means to demonstrate complex and often times inaccessible concepts to undergraduates. However, students without prior computer programming training may find working with code-based simulations to be intimidating and distracting. A series of computational neuroscience labs involving the Hodgkin-Huxley equations, an Integrate-and-Fire model, and a Hopfield Memory network were used in an undergraduate neuroscience laboratory component of an introductory level course. Using short focused surveys before and after each lab, student comfort levels were shown to increase drastically from a majority of students being uncomfortable or with neutral feelings about working in the MATLAB environment to a vast majority of students being comfortable working in the environment. Though change was reported within each lab, a series of labs was necessary in order to establish a lasting high level of comfort. Comfort working with code is important as a first step in acquiring computational skills that are required to address many questions within neuroscience.

A Series of Computational Neuroscience Labs Increases Comfort with MATLAB

PubMed Central

Nichols, David F.

2015-01-01

Computational simulations allow for a low-cost, reliable means to demonstrate complex and often times inaccessible concepts to undergraduates. However, students without prior computer programming training may find working with code-based simulations to be intimidating and distracting. A series of computational neuroscience labs involving the Hodgkin-Huxley equations, an Integrate-and-Fire model, and a Hopfield Memory network were used in an undergraduate neuroscience laboratory component of an introductory level course. Using short focused surveys before and after each lab, student comfort levels were shown to increase drastically from a majority of students being uncomfortable or with neutral feelings about working in the MATLAB environment to a vast majority of students being comfortable working in the environment. Though change was reported within each lab, a series of labs was necessary in order to establish a lasting high level of comfort. Comfort working with code is important as a first step in acquiring computational skills that are required to address many questions within neuroscience. PMID:26557798

A quantum probability perspective on borderline vagueness.

PubMed

Blutner, Reinhard; Pothos, Emmanuel M; Bruza, Peter

2013-10-01

The term "vagueness" describes a property of natural concepts, which normally have fuzzy boundaries, admit borderline cases, and are susceptible to Zeno's sorites paradox. We will discuss the psychology of vagueness, especially experiments investigating the judgment of borderline cases and contradictions. In the theoretical part, we will propose a probabilistic model that describes the quantitative characteristics of the experimental finding and extends Alxatib's and Pelletier's () theoretical analysis. The model is based on a Hopfield network for predicting truth values. Powerful as this classical perspective is, we show that it falls short of providing an adequate coverage of the relevant empirical results. In the final part, we will argue that a substantial modification of the analysis put forward by Alxatib and Pelletier and its probabilistic pendant is needed. The proposed modification replaces the standard notion of probabilities by quantum probabilities. The crucial phenomenon of borderline contradictions can be explained then as a quantum interference phenomenon. © 2013 Cognitive Science Society, Inc.

Analysis of zenith tropospheric delay in tropical latitudes

NASA Astrophysics Data System (ADS)

Zablotskyj, Fedir; Zablotska, Alexandra

2010-05-01

The paper studies some peculiarities of the nature of zenith tropospheric delay in tropical latitudes. There are shown the values of dry and wet components of zenith tropospheric delay obtained by an integration of the radiosonde data at 9 stations: Guam, Seyshelles, Singapore, Pago Pago, Hilo, Koror, San Cristobal, San Juan and Belem. There were made 350 atmospheric models for the period from 11th to 20th of January, April, July and October 2008 at 0h and 12h UT (Universal Time). The quantities of the dry dd(aer) and wet dw(aer) components of zenith tropospheric delay were determined by means of the integration for each atmospheric model. Then the quantities of the dry dd(SA), dd(HO) and wet dw(SA), dw(HO) components of zenith tropospheric delay (Saastamoinen and Hopfield analytical models) were calculated by the surface values of the pressure P0, temperature t0, relative air humidity U0 on the height H0 and by the geographic latitude φ. It must be point out the following from the analysis of the averaged quantities and differences δdd(SA), δdd(HO), δdw(SA), δdw(HO) between the correspondent components of zenith tropospheric delay obtained by the radiosonde data and by the analytical models: zenith tropospheric delay obtained by the radiosonde data amounts to considerably larger value in the equatorial zone, especially, at the expense of the wet component, in contrast to high and middle latitudes. Thus, the dry component of zenith tropospheric delay is equal at the average 2290 mm and the wet component is 290 mm; by the results of the analysis of Saastamoinen and Hopfield models the dry component differences δdd(SA) and δdd(HO) are negative in all cases and average -20 mm. It is not typical neither for high latitudes nor for middle ones; the differences between the values of the wet components obtained from radiosonde data and of Saastamoinen and Hopfield models are positive in general. Therewith the δdw(HO) values are larger than the correspondent �

Dependable Networks as a Paradigm for Network Innovation

NASA Astrophysics Data System (ADS)

Miki, Tetsuya

In past, dependable networks meant minimizing network outages or the impact of the outages. However, over the decade, major network services have shifted from telephone and data transmission to Internet and to mobile communication, where higher layer services with a variety of contents are provided. Reviewing these backgrounds of network development, the importance of the dependability of higher layer network services are pointed out. Then, the main aspects to realize the dependability are given for lower, middle and higher layer network services. In addition, some particular issues for dependable networks are described.

Analytical Dynamics and Nonrigid Spacecraft Simulation

NASA Technical Reports Server (NTRS)

Likins, P. W.

1974-01-01

Application to the simulation of idealized spacecraft are considered both for multiple-rigid-body models and for models consisting of combination of rigid bodies and elastic bodies, with the elastic bodies being defined either as continua, as finite-element systems, or as a collection of given modal data. Several specific examples are developed in detail by alternative methods of analytical mechanics, and results are compared to a Newton-Euler formulation. The following methods are developed from d'Alembert's principle in vector form: (1) Lagrange's form of d'Alembert's principle for independent generalized coordinates; (2) Lagrange's form of d'Alembert's principle for simply constrained systems; (3) Kane's quasi-coordinate formulation of D'Alembert's principle; (4) Lagrange's equations for independent generalized coordinates; (5) Lagrange's equations for simply constrained systems; (6) Lagrangian quasi-coordinate equations (or the Boltzmann-Hamel equations); (7) Hamilton's equations for simply constrained systems; and (8) Hamilton's equations for independent generalized coordinates.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

Network marketing on a small-world network

NASA Astrophysics Data System (ADS)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

Weighted projected networks: mapping hypergraphs to networks.

PubMed

López, Eduardo

2013-05-01

Many natural, technological, and social systems incorporate multiway interactions, yet are characterized and measured on the basis of weighted pairwise interactions. In this article, I propose a family of models in which pairwise interactions originate from multiway interactions, by starting from ensembles of hypergraphs and applying projections that generate ensembles of weighted projected networks. I calculate analytically the statistical properties of weighted projected networks, and suggest ways these could be used beyond theoretical studies. Weighted projected networks typically exhibit weight disorder along links even for very simple generating hypergraph ensembles. Also, as the size of a hypergraph changes, a signature of multiway interaction emerges on the link weights of weighted projected networks that distinguishes them from fundamentally weighted pairwise networks. This signature could be used to search for hidden multiway interactions in weighted network data. I find the percolation threshold and size of the largest component for hypergraphs of arbitrary uniform rank, translate the results into projected networks, and show that the transition is second order. This general approach to network formation has the potential to shed new light on our understanding of weighted networks.

Evaluation of the Consistency among In Situ and Remote Sensing Measurements of CO2 over North America using the CarbonTracker-Lagrange Regional Inverse Modeling Framework

NASA Astrophysics Data System (ADS)

Andrews, A. E.; Trudeau, M.; Hu, L.; Thoning, K. W.; Shiga, Y. P.; Michalak, A. M.; Benmergui, J. S.; Mountain, M. E.; Nehrkorn, T.; O'Dell, C.; Jacobson, A. R.; Miller, J.; Sweeney, C.; Chen, H.; Ploeger, F.; Tans, P. P.

2017-12-01

CarbonTracker-Lagrange (CT-L) is a regional inverse modeling system for estimating CO2 fluxes with rigorous uncertainty quantification. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We have computed a library of footprints corresponding to in situ and remote sensing measurements of CO2 over North America for 2007-2015. GOSAT and OCO-2 XCO2 retrievals are simulated using a suite of CT-L terrestrial ecosystem flux estimates that have been optimized with respect to in situ atmospheric CO2 measurements along with fossil fuel fluxes from emissions inventories. A vertical profile of STILT-WRF footprints was constructed corresponding to each simulated satellite retrieval, and CO2 profiles are generated by convolving the footprints with fluxes and attaching initial values advected from the domain boundaries. The stratospheric contribution to XCO2 has been estimated using 4-dimensional CO2 fields from the NOAA CarbonTracker model (version CT2016) and from the Chemical Lagrangian Model of the Stratosphere (CLaMS), after scaling the model fields to match data from the NOAA AirCore surface-to-stratosphere air sampling system. Tropospheric lateral boundary conditions are from CT2016 and from an empirical boundary value product derived from aircraft and marine boundary layer data. The averaging kernel and a priori CO2 profile are taken into account for direct comparisons with retrievals. We have focused on North America due to the relatively dense in situ measurements available with the aim of developing strategies for combined assimilation of in situ and remote sensing data. We will consider the extent to which interannual variability in terrestrial fluxes is manifest in the real and simulated satellite retrievals, and we will investigate possible systematic biases in the satellite retrievals and in the model.

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph

Computer Networks and Networking: A Primer.

ERIC Educational Resources Information Center

Collins, Mauri P.

1993-01-01

Provides a basic introduction to computer networks and networking terminology. Topics addressed include modems; the Internet; TCP/IP (Transmission Control Protocol/Internet Protocol); transmission lines; Internet Protocol numbers; network traffic; Fidonet; file transfer protocol (FTP); TELNET; electronic mail; discussion groups; LISTSERV; USENET;…

Network structure exploration in networks with node attributes

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Minimal Increase Network Coding for Dynamic Networks.

PubMed

Zhang, Guoyin; Fan, Xu; Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.

Minimal Increase Network Coding for Dynamic Networks

PubMed Central

Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. PMID:26867211

Undermining and Strengthening Social Networks through Network Modification

PubMed Central

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-01-01

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention. PMID:27703198

Undermining and Strengthening Social Networks through Network Modification.

PubMed

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-10-05

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.

Undermining and Strengthening Social Networks through Network Modification

NASA Astrophysics Data System (ADS)

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-10-01

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.

Spatial networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

Research in network management techniques for tactical data communications networks

NASA Astrophysics Data System (ADS)

Boorstyn, R.; Kershenbaum, A.; Maglaris, B.; Sarachik, P.

1982-09-01

This is the final technical report for work performed on network management techniques for tactical data networks. It includes all technical papers that have been published during the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques, and local area networks.

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Network Hardware Virtualization for Application Provisioning in Core Networks

DOE PAGES

Gumaste, Ashwin; Das, Tamal; Khandwala, Kandarp; ...

2017-02-03

We present that service providers and vendors are moving toward a network virtualized core, whereby multiple applications would be treated on their own merit in programmable hardware. Such a network would have the advantage of being customized for user requirements and allow provisioning of next generation services that are built specifically to meet user needs. In this article, we articulate the impact of network virtualization on networks that provide customized services and how a provider's business can grow with network virtualization. We outline a decision map that allows mapping of applications with technology that is supported in network-virtualization - orientedmore » equipment. Analogies to the world of virtual machines and generic virtualization show that hardware supporting network virtualization will facilitate new customer needs while optimizing the provider network from the cost and performance perspectives. A key conclusion of the article is that growth would yield sizable revenue when providers plan ahead in terms of supporting network-virtualization-oriented technology in their networks. To be precise, providers have to incorporate into their growth plans network elements capable of new service deployments while protecting network neutrality. Finally, a simulation study validates our NV-induced model.« less

Network Hardware Virtualization for Application Provisioning in Core Networks

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

Gumaste, Ashwin; Das, Tamal; Khandwala, Kandarp

We present that service providers and vendors are moving toward a network virtualized core, whereby multiple applications would be treated on their own merit in programmable hardware. Such a network would have the advantage of being customized for user requirements and allow provisioning of next generation services that are built specifically to meet user needs. In this article, we articulate the impact of network virtualization on networks that provide customized services and how a provider's business can grow with network virtualization. We outline a decision map that allows mapping of applications with technology that is supported in network-virtualization - orientedmore » equipment. Analogies to the world of virtual machines and generic virtualization show that hardware supporting network virtualization will facilitate new customer needs while optimizing the provider network from the cost and performance perspectives. A key conclusion of the article is that growth would yield sizable revenue when providers plan ahead in terms of supporting network-virtualization-oriented technology in their networks. To be precise, providers have to incorporate into their growth plans network elements capable of new service deployments while protecting network neutrality. Finally, a simulation study validates our NV-induced model.« less

Configurable hardware integrate and fire neurons for sparse approximation.

PubMed

Shapero, Samuel; Rozell, Christopher; Hasler, Paul

2013-09-01

Sparse approximation is an important optimization problem in signal and image processing applications. A Hopfield-Network-like system of integrate and fire (IF) neurons is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete L1 sparse approximation problem. A scalable system architecture is described, including IF neurons with a nonlinear firing function, and current-based synapses to provide linear computation. A network of 18 neurons with 12 inputs is implemented on the RASP 2.9v chip, a Field Programmable Analog Array (FPAA) with directly programmable floating gate elements. Said system uses over 1400 floating gates, the largest system programmed on a FPAA to date. The circuit successfully reproduced the outputs of a digital optimization program, converging to within 4.8% RMS, and an objective cost only 1.7% higher on average. The active circuit consumed 559 μA of current at 2.4 V and converges on solutions in 25 μs, with measurement of the converged spike rate taking an additional 1 ms. Extrapolating the scaling trends to a N=1000 node system, the spiking LCA compares favorably with state-of-the-art digital solutions, and analog solutions using a non-spiking approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Networks.

ERIC Educational Resources Information Center

Maughan, George R.; Petitto, Karen R.; McLaughlin, Don

2001-01-01

Describes the connectivity features and options of modern campus communication and information system networks, including signal transmission (wire-based and wireless), signal switching, convergence of networks, and network assessment variables, to enable campus leaders to make sound future-oriented decisions. (EV)

Network Management of the SPLICE Computer Network.

DTIC Science & Technology

1982-12-01

Approved for public release; distri4ition unlimited. Network lanagenent Df the SPLICE Computer Network by Zriig E. Opal captaini United St~tes larine... structure of the network must leni itself t3 change and reconfiguration, one author [Ref. 2: p.21] recommended that a global bus topology be adopted for...statistics, trace statistics, snapshot statistiZs, artifi - cial traffic generators, auulat on, a network measurement center which includes control, collction

Robustness of network of networks under targeted attack.

PubMed

Dong, Gaogao; Gao, Jianxi; Du, Ruijin; Tian, Lixin; Stanley, H Eugene; Havlin, Shlomo

2013-05-01

The robustness of a network of networks (NON) under random attack has been studied recently [Gao et al., Phys. Rev. Lett. 107, 195701 (2011)]. Understanding how robust a NON is to targeted attacks is a major challenge when designing resilient infrastructures. We address here the question how the robustness of a NON is affected by targeted attack on high- or low-degree nodes. We introduce a targeted attack probability function that is dependent upon node degree and study the robustness of two types of NON under targeted attack: (i) a tree of n fully interdependent Erdős-Rényi or scale-free networks and (ii) a starlike network of n partially interdependent Erdős-Rényi networks. For any tree of n fully interdependent Erdős-Rényi networks and scale-free networks under targeted attack, we find that the network becomes significantly more vulnerable when nodes of higher degree have higher probability to fail. When the probability that a node will fail is proportional to its degree, for a NON composed of Erdős-Rényi networks we find analytical solutions for the mutual giant component P(∞) as a function of p, where 1-p is the initial fraction of failed nodes in each network. We also find analytical solutions for the critical fraction p(c), which causes the fragmentation of the n interdependent networks, and for the minimum average degree k[over ¯](min) below which the NON will collapse even if only a single node fails. For a starlike NON of n partially interdependent Erdős-Rényi networks under targeted attack, we find the critical coupling strength q(c) for different n. When q>q(c), the attacked system undergoes an abrupt first order type transition. When q≤q(c), the system displays a smooth second order percolation transition. We also evaluate how the central network becomes more vulnerable as the number of networks with the same coupling strength q increases. The limit of q=0 represents no dependency, and the results are consistent with the classical

Networks within networks: The neuronal control of breathing

PubMed Central

Garcia, Alfredo J.; Zanella, Sebastien; Koch, Henner; Doi, Atsushi; Ramirez, Jan-Marino

2013-01-01

Breathing emerges through complex network interactions involving neurons distributed throughout the nervous system. The respiratory rhythm generating network is composed of micro networks functioning within larger networks to generate distinct rhythms and patterns that characterize breathing. The pre-Bötzinger complex, a rhythm generating network located within the ventrolateral medulla assumes a core function without which respiratory rhythm generation and breathing cease altogether. It contains subnetworks with distinct synaptic and intrinsic membrane properties that give rise to different types of respiratory rhythmic activities including eupneic, sigh, and gasping activities. While critical aspects of these rhythmic activities are preserved when isolated in in vitro preparations, the pre-Bötzinger complex functions in the behaving animal as part of a larger network that receives important inputs from areas such as the pons and parafacial nucleus. The respiratory network is also an integrator of modulatory and sensory inputs that imbue the network with the important ability to adapt to changes in the behavioral, metabolic, and developmental conditions of the organism. This review summarizes our current understanding of these interactions and relates the emerging concepts to insights gained in other rhythm generating networks. PMID:21333801

Network Monitor and Control of Disruption-Tolerant Networks

NASA Technical Reports Server (NTRS)

Torgerson, J. Leigh

2014-01-01

For nearly a decade, NASA and many researchers in the international community have been developing Internet-like protocols that allow for automated network operations in networks where the individual links between nodes are only sporadically connected. A family of Disruption-Tolerant Networking (DTN) protocols has been developed, and many are reaching CCSDS Blue Book status. A NASA version of DTN known as the Interplanetary Overlay Network (ION) has been flight-tested on the EPOXI spacecraft and ION is currently being tested on the International Space Station. Experience has shown that in order for a DTN service-provider to set up a large scale multi-node network, a number of network monitor and control technologies need to be fielded as well as the basic DTN protocols. The NASA DTN program is developing a standardized means of querying a DTN node to ascertain its operational status, known as the DTN Management Protocol (DTNMP), and the program has developed some prototypes of DTNMP software. While DTNMP is a necessary component, it is not sufficient to accomplish Network Monitor and Control of a DTN network. JPL is developing a suite of tools that provide for network visualization, performance monitoring and ION node control software. This suite of network monitor and control tools complements the GSFC and APL-developed DTN MP software, and the combined package can form the basis for flight operations using DTN.

Analysis of reliability for multi-ring interconnection of RPR networks

NASA Astrophysics Data System (ADS)

Liu, Jia; Jin, Depeng; Zeng, Lieguang; Li, Yong

2008-11-01

In this paper, the reliability and MTTF (Mean Time to Failure) for multi-ring RPR (Resilient Packet Ring) are calculated on the conditions of single-link failures, double-link failures and no failure, respectively. The parameters such as the total number of stations N, the number of the sub-rings R, and the distribution of Ni which represents the number of the stations in the i-th sub-ring (1<=i<=R) are contained in the formulas. The relationship between the value of the reliability/MTTF and the parameters N, R and Ni is analyzed. The result shows that reliability/MTTF of the RPR multi-rings is increasing while the variance of Ni is decreasing. It is also proved that the value of the reliability/MTTF is maximum when Ni=Nj ( i ≠j and 1<=i, j<=R) by using Lagrange multipliers method, i.e. the condition of the optimal reliability of multi-ring RPR is satisfied when var(Ni) =0.

A Person Fit Test for IRT Models for Polytomous Items

ERIC Educational Resources Information Center

Glas, C. A. W.; Dagohoy, Anna Villa T.

2007-01-01

A person fit test based on the Lagrange multiplier test is presented for three item response theory models for polytomous items: the generalized partial credit model, the sequential model, and the graded response model. The test can also be used in the framework of multidimensional ability parameters. It is shown that the Lagrange multiplier…

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and

Network Sampling and Classification:An Investigation of Network Model Representations

PubMed Central

Airoldi, Edoardo M.; Bai, Xue; Carley, Kathleen M.

2011-01-01

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed. PMID:21666773

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.

Network Solutions.

ERIC Educational Resources Information Center

Vietzke, Robert; And Others

1996-01-01

This special section explains the latest developments in networking technologies, profiles school districts benefiting from successful implementations, and reviews new products for building networks. Highlights include ATM (asynchronous transfer mode), cable modems, networking switches, Internet screening software, file servers, network management…

Energy-aware virtual network embedding in flexi-grid networks.

PubMed

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Bounded state variables and the calculus of variations

NASA Technical Reports Server (NTRS)

Hanafy, L. M.

1972-01-01

An optimal control problem with bounded state variables is transformed into a Lagrange problem by means of differentiable mappings which take some Euclidean space onto the control and state regions. Whereas all such mappings lead to a Lagrange problem, it is shown that only those which are defined as acceptable pairs of transformations are suitable in the sense that solutions to the transformed Lagrange problem will lead to solutions to the original bounded state problem and vice versa. In particular, an acceptable pair of transformations is exhibited for the case when the control and state regions are right parallelepipeds. Finally, a description of the necessary conditions for the bounded state problem which were obtained by this method is given.

Comparison of Numerical Modeling Methods for Soil Vibration Cutting

NASA Astrophysics Data System (ADS)

Jiang, Jiandong; Zhang, Enguang

2018-01-01

In this paper, we studied the appropriate numerical simulation method for vibration soil cutting. Three numerical simulation methods, commonly used for uniform speed soil cutting, Lagrange, ALE and DEM, are analyzed. Three models of vibration soil cutting simulation model are established by using ls-dyna.The applicability of the three methods to this problem is analyzed in combination with the model mechanism and simulation results. Both the Lagrange method and the DEM method can show the force oscillation of the tool and the large deformation of the soil in the vibration cutting. Lagrange method shows better effect of soil debris breaking. Because of the poor stability of ALE method, it is not suitable to use soil vibration cutting problem.

Deploying temporary networks for upscaling of sparse network stations

NASA Astrophysics Data System (ADS)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

2016-10-01

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

Network configuration management : paving the way to network agility.

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

Maestas, Joseph H.

2007-08-01

Sandia networks consist of nearly nine hundred routers and switches and nearly one million lines of command code, and each line ideally contributes to the capabilities of the network to convey information from one location to another. Sandia's Cyber Infrastructure Development and Deployment organizations recognize that it is therefore essential to standardize network configurations and enforce conformance to industry best business practices and documented internal configuration standards to provide a network that is agile, adaptable, and highly available. This is especially important in times of constrained budgets as members of the workforce are called upon to improve efficiency, effectiveness, andmore » customer focus. Best business practices recommend using the standardized configurations in the enforcement process so that when root cause analysis results in recommended configuration changes, subsequent configuration auditing will improve compliance to the standard. Ultimately, this minimizes mean time to repair, maintains the network security posture, improves network availability, and enables efficient transition to new technologies. Network standardization brings improved network agility, which in turn enables enterprise agility, because the network touches all facets of corporate business. Improved network agility improves the business enterprise as a whole.« less

Comparison analysis on vulnerability of metro networks based on complex network

NASA Astrophysics Data System (ADS)

Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

2018-04-01

This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.

Network architecture in a converged optical + IP network

NASA Astrophysics Data System (ADS)

Wakim, Walid; Zottmann, Harald

2012-01-01

As demands on Provider Networks continue to grow at exponential rates, providers are forced to evaluate how to continue to grow the network while increasing service velocity, enhancing resiliency while decreasing the total cost of ownership (TCO). The bandwidth growth that networks are experiencing is in the form packet based multimedia services such as video, video conferencing, gaming, etc... mixed with Over the Top (OTT) content providers such as Netflix, and the customer's expectations that best effort is not enough you end up with a situation that forces the provider to analyze how to gain more out of the network with less cost. In this paper we will discuss changes in the network that are driving us to a tighter integration between packet and optical layers and how to improve on today's multi - layer inefficiencies to drive down network TCO and provide for a fully integrated and dynamic network that will decrease time to revenue.

Temporal networks

NASA Astrophysics Data System (ADS)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered

From network structure to network reorganization: implications for adult neurogenesis

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Network morphospace

PubMed Central

Avena-Koenigsberger, Andrea; Goñi, Joaquín; Solé, Ricard; Sporns, Olaf

2015-01-01

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems. PMID:25540237

Process-in-Network: A Comprehensive Network Processing Approach

PubMed Central

Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

2012-01-01

A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network. PMID:22969390

Correlated network of networks enhances robustness against catastrophic failures.

PubMed

Min, Byungjoon; Zheng, Muhua

2018-01-01

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule.

Trauma-Exposed Latina Immigrants' Networks: A Social Network Analysis Approach.

PubMed

Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A; Fernandez, Nicole C; Cabling, Mark; Kaltman, Stacey

2016-11-01

Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. In 2011-2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted.

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

2016-12-01

Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

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

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

PubMed

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

Psychology and social networks: a dynamic network theory perspective.

PubMed

Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

2014-04-01

Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Animal transportation networks

PubMed Central

Perna, Andrea; Latty, Tanya

2014-01-01

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research. PMID:25165598

Correlated network of networks enhances robustness against catastrophic failures

PubMed Central

Zheng, Muhua

2018-01-01

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule. PMID:29668730

Low-rank network decomposition reveals structural characteristics of small-world networks

NASA Astrophysics Data System (ADS)

Barranca, Victor J.; Zhou, Douglas; Cai, David

2015-12-01

Small-world networks occur naturally throughout biological, technological, and social systems. With their prevalence, it is particularly important to prudently identify small-world networks and further characterize their unique connection structure with respect to network function. In this work we develop a formalism for classifying networks and identifying small-world structure using a decomposition of network connectivity matrices into low-rank and sparse components, corresponding to connections within clusters of highly connected nodes and sparse interconnections between clusters, respectively. We show that the network decomposition is independent of node indexing and define associated bounded measures of connectivity structure, which provide insight into the clustering and regularity of network connections. While many existing network characterizations rely on constructing benchmark networks for comparison or fail to describe the structural properties of relatively densely connected networks, our classification relies only on the intrinsic network structure and is quite robust with respect to changes in connection density, producing stable results across network realizations. Using this framework, we analyze several real-world networks and reveal new structural properties, which are often indiscernible by previously established characterizations of network connectivity.

Vulnerability of complex networks

NASA Astrophysics Data System (ADS)

Mishkovski, Igor; Biey, Mario; Kocarev, Ljupco

2011-01-01

We consider normalized average edge betweenness of a network as a metric of network vulnerability. We suggest that normalized average edge betweenness together with is relative difference when certain number of nodes and/or edges are removed from the network is a measure of network vulnerability, called vulnerability index. Vulnerability index is calculated for four synthetic networks: Erdős-Rényi (ER) random networks, Barabási-Albert (BA) model of scale-free networks, Watts-Strogatz (WS) model of small-world networks, and geometric random networks. Real-world networks for which vulnerability index is calculated include: two human brain networks, three urban networks, one collaboration network, and two power grid networks. We find that WS model of small-world networks and biological networks (human brain networks) are the most robust networks among all networks studied in the paper.

Trauma-Exposed Latina Immigrants’ Networks: A Social Network Analysis Approach

PubMed Central

Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A.; Fernandez, Nicole C.; Cabling, Mark; Kaltman, Stacey

2015-01-01

Objective Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. Methods In 2011–2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Results Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Conclusions Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted. PMID:28078194

Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

PubMed

Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

2014-04-21

Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.

Modelling dendritic ecological networks in space: An integrated network perspective

Treesearch

Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

2013-01-01

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

A Network of Networks Perspective on Global Trade.

PubMed

Maluck, Julian; Donner, Reik V

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990-2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector's role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network's substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed to

Managing Network Partitions in Structured P2P Networks

NASA Astrophysics Data System (ADS)

Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to scale, tolerate failures, and self-manage. Any long-lived Internet-scale distributed system is destined to face network partitions. Consequently, the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems. This makes it a crucial requirement for building any structured peer-to-peer systems to be resilient to network partitions. Although the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems, it has hardly been studied in the context of structured peer-to-peer systems. Structured overlays have mainly been studied under churn (frequent joins/failures), which as a side effect solves the problem of network partitions, as it is similar to massive node failures. Yet, the crucial aspect of network mergers has been ignored. In fact, it has been claimed that ring-based structured overlay networks, which constitute the majority of the structured overlays, are intrinsically ill-suited for merging rings. In this chapter, we motivate the problem of network partitions and mergers in structured overlays. We discuss how a structured overlay can automatically detect a network partition and merger. We present an algorithm for merging multiple similar ring-based overlays when the underlying network merges. We examine the solution in dynamic conditions, showing how our solution is resilient to churn during the merger, something widely believed to be difficult or impossible. We evaluate the algorithm for various scenarios and show that even when falsely detecting a merger, the algorithm quickly terminates and does not clutter the network with many messages. The algorithm is flexible as the tradeoff between message complexity and time complexity can be adjusted by a parameter.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

PubMed Central

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-01-01

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

PubMed

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Network Upgrade for the SLC: PEP II Network

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

Crane, M.; Call, M.; Clark, S.

2011-09-09

The PEP-II control system required a new network to support the system functions. This network, called CTLnet, is an FDDI/Ethernet based network using only TCP/IP protocols. An upgrade of the SLC Control System micro communications to use TCP/IP and SLCNET would allow all PEP-II control system nodes to use TCP/IP. CTLnet is private and separate from the SLAC public network. Access to nodes and control system functions is provided by multi-homed application servers with connections to both the private CTLnet and the SLAC public network. Monitoring and diagnostics are provided using a dedicated system. Future plans and current status informationmore » is included.« less

User Access Management Based on Network Pricing for Social Network Applications

PubMed Central

Ma, Xingmin; Gu, Qing

2018-01-01

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. PMID:29495252

A Collaboration Network Model Of Cytokine-Protein Network

NASA Astrophysics Data System (ADS)

Zou, Sheng-Rong; Zhou, Ta; Peng, Yu-Jing; Guo, Zhong-Wei; Gu, Chang-Gui; He, Da-Ren

2008-03-01

Complex networks provide us a new view for investigation of immune systems. We collect data through STRING database and present a network description with cooperation network model. The cytokine-protein network model we consider is constituted by two kinds of nodes, one is immune cytokine types which can be regarded as collaboration acts, the other one is protein type which can be regarded as collaboration actors. From act degree distribution that can be well described by typical SPL (shifted power law) functions [1], we find that HRAS, TNFRSF13C, S100A8, S100A1, MAPK8, S100A7, LIF, CCL4, CXCL13 are highly collaborated with other proteins. It reveals that these mediators are important in cytokine-protein network to regulate immune activity. Dyad in the collaboration networks can be defined as two proteins and they appear in one cytokine collaboration relationship. The dyad act degree distribution can also be well described by typical SPL functions. [1] Assortativity and act degree distribution of some collaboration networks, Hui Chang, Bei-Bei Su, Yue-Ping Zhou, Daren He, Physica A, 383 (2007) 687-702

Network traffic anomaly prediction using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

2017-03-01

As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Airborne Network Optimization with Dynamic Network Update

DTIC Science & Technology

2015-03-26

Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...Member Dr. Barry E. Mullins Member AFIT-ENG-MS-15-M-030 Abstract Modern networks employ congestion and routing management algorithms that can perform...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The

Aberrant within- and between-network connectivity of the mirror neuron system network and the mentalizing network in first episode psychosis.

PubMed

Choe, Eugenie; Lee, Tae Young; Kim, Minah; Hur, Ji-Won; Yoon, Youngwoo Bryan; Cho, Kang-Ik K; Kwon, Jun Soo

2018-03-26

It has been suggested that the mentalizing network and the mirror neuron system network support important social cognitive processes that are impaired in schizophrenia. However, the integrity and interaction of these two networks have not been sufficiently studied, and their effects on social cognition in schizophrenia remain unclear. Our study included 26 first-episode psychosis (FEP) patients and 26 healthy controls. We utilized resting-state functional connectivity to examine the a priori-defined mirror neuron system network and the mentalizing network and to assess the within- and between-network connectivities of the networks in FEP patients. We also assessed the correlation between resting-state functional connectivity measures and theory of mind performance. FEP patients showed altered within-network connectivity of the mirror neuron system network, and aberrant between-network connectivity between the mirror neuron system network and the mentalizing network. The within-network connectivity of the mirror neuron system network was noticeably correlated with theory of mind task performance in FEP patients. The integrity and interaction of the mirror neuron system network and the mentalizing network may be altered during the early stages of psychosis. Additionally, this study suggests that alterations in the integrity of the mirror neuron system network are highly related to deficient theory of mind in schizophrenia, and this problem would be present from the early stage of psychosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Energy-aware virtual network embedding in flexi-grid optical networks

NASA Astrophysics Data System (ADS)

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng; Chen, Bin

2018-01-01

Virtual network embedding (VNE) problem is to map multiple heterogeneous virtual networks (VN) on a shared substrate network, which mitigate the ossification of the substrate network. Meanwhile, energy efficiency has been widely considered in the network design. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the power increment of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low energy consumption. Numerical results show the functionality of the heuristic algorithm in a 24-node network.

A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

NASA Astrophysics Data System (ADS)

Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

Research of ad hoc network based on SINCGARS network

NASA Astrophysics Data System (ADS)

Nie, Hao; Cai, Xiaoxia; Chen, Hong; Chen, Jian; Weng, Pengfei

2016-03-01

In today's world, science and technology make a spurt of progress, so society has entered the era of information technology, network. Only the comprehensive use of electronic warfare and network warfare means can we maximize their access to information and maintain the information superiority. Combined with the specific combat mission and operational requirements, the research design and construction in accordance with the actual military which are Suitable for the future of information technology needs of the tactical Adhoc network, tactical internet, will greatly improve the operational efficiency of the command of the army. Through the study of the network of the U.S. military SINCGARS network, it can explore the routing protocol and mobile model, to provide a reference for the research of our army network.

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.

Network survivability performance

NASA Astrophysics Data System (ADS)

1993-11-01

This technical report has been developed to address the survivability of telecommunications networks including services. It responds to the need for a common understanding of, and assessment techniques for network survivability, availability, integrity, and reliability. It provides a basis for designing and operating telecommunications networks to user expectations for network survivability and a foundation for continuing industry activities in the subject area. This report focuses on the survivability of both public and private networks and covers a wide range of users. Two frameworks are established for quantifying and categorizing service outages, and for classifying network survivability techniques and measures. The performance of the network survivability techniques is considered; however, recommended objectives are not established for network survivability performance.

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

PubMed

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

Distributed sensor networks: a cellular nonlinear network perspective.

PubMed

Haenggi, Martin

2003-12-01

Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.

Impact of reduced scale free network on wireless sensor network

NASA Astrophysics Data System (ADS)

Keshri, Neha; Gupta, Anurag; Mishra, Bimal Kumar

2016-12-01

In heterogeneous wireless sensor network (WSN) each data-packet traverses through multiple hops over restricted communication range before it reaches the sink. The amount of energy required to transmit a data-packet is directly proportional to the number of hops. To balance the energy costs across the entire network and to enhance the robustness in order to improve the lifetime of WSN becomes a key issue of researchers. Due to high dimensionality of an epidemic model of WSN over a general scale free network, it is quite difficult to have close study of network dynamics. To overcome this complexity, we simplify a general scale free network by partitioning all of its motes into two classes: higher-degree motes and lower-degree motes, and equating the degrees of all higher-degree motes with lower-degree motes, yielding a reduced scale free network. We develop an epidemic model of WSN based on reduced scale free network. The existence of unique positive equilibrium is determined with some restrictions. Stability of the system is proved. Furthermore, simulation results show improvements made in this paper have made the entire network have a better robustness to the network failure and the balanced energy costs. This reduced model based on scale free network theory proves more applicable to the research of WSN.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

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

A Network of Networks Perspective on Global Trade

PubMed Central

Maluck, Julian; Donner, Reik V.

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990–2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector’s role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network’s substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed

SCM: A method to improve network service layout efficiency with network evolution

PubMed Central

Zhao, Qi; Zhang, Chuanhao

2017-01-01

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299

SCM: A method to improve network service layout efficiency with network evolution.

PubMed

Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng

2017-01-01

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.

Multilayer Brain Networks

NASA Astrophysics Data System (ADS)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

Resilience of networks to environmental stress: From regular to random networks

NASA Astrophysics Data System (ADS)

Eom, Young-Ho

2018-04-01

Despite the huge interest in network resilience to stress, most of the studies have concentrated on internal stress damaging network structure (e.g., node removals). Here we study how networks respond to environmental stress deteriorating their external conditions. We show that, when regular networks gradually disintegrate as environmental stress increases, disordered networks can suddenly collapse at critical stress with hysteresis and vulnerability to perturbations. We demonstrate that this difference results from a trade-off between node resilience and network resilience to environmental stress. The nodes in the disordered networks can suppress their collapses due to the small-world topology of the networks but eventually collapse all together in return. Our findings indicate that some real networks can be highly resilient against environmental stress to a threshold yet extremely vulnerable to the stress above the threshold because of their small-world topology.

Development and Evaluation of a Hydrostatic Dynamical Core Using the Spectral Element/Discontinuous Galerkin Methods

DTIC Science & Technology

2014-04-01

The CG and DG horizontal discretization employs high-order nodal basis functions associated with Lagrange polynomials based on Gauss-Lobatto- Legendre ...and DG horizontal discretization employs high-order nodal basis functions 29 associated with Lagrange polynomials based on Gauss-Lobatto- Legendre ...Inside 235 each element we build ( 1)N + Gauss-Lobatto- Legendre (GLL) quadrature points, where N 236 indicate the polynomial order of the basis

An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters

NASA Astrophysics Data System (ADS)

Sheng, L.; Qian, S. Q.; Ye, Y. Q.; Wu, Y. H.

2017-09-01

In this article, an improved immune algorithm (IIA), based on the fundamental principles of the biological immune system, is proposed for optimizing the pulse width modulation (PWM) control sequence of a single-phase full-bridge inverter. The IIA takes advantage of the receptor editing and adaptive mutation mechanisms of the immune system to develop two operations that enhance the population diversity and convergence of the proposed algorithm. To verify the effectiveness and examine the performance of the IIA, 17 cases are considered, including fixed and disturbed resistances. Simulation results show that the IIA is able to obtain an effective PWM control sequence. Furthermore, when compared with existing immune algorithms (IAs), genetic algorithms (GAs), a non-traditional GA, simplified simulated annealing, and a generalized Hopfield neural network method, the IIA can achieve small total harmonic distortion (THD) and large magnitude. Meanwhile, a non-parametric test indicates that the IIA is significantly better than most comparison algorithms. Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/0305215X.2016.1250894.

Non-autonomous equations with unpredictable solutions

NASA Astrophysics Data System (ADS)

Akhmet, Marat; Fen, Mehmet Onur

2018-06-01

To make research of chaos more amenable to investigating differential and discrete equations, we introduce the concepts of an unpredictable function and sequence. The topology of uniform convergence on compact sets is applied to define unpredictable functions [1,2]. The unpredictable sequence is defined as a specific unpredictable function on the set of integers. The definitions are convenient to be verified as solutions of differential and discrete equations. The topology is metrizable and easy for applications with integral operators. To demonstrate the effectiveness of the approach, the existence and uniqueness of the unpredictable solution for a delay differential equation are proved as well as for quasilinear discrete systems. As a corollary of the theorem, a similar assertion for a quasilinear ordinary differential equation is formulated. The results are demonstrated numerically, and an application to Hopfield neural networks is provided. In particular, Poincaré chaos near periodic orbits is observed. The completed research contributes to the theory of chaos as well as to the theory of differential and discrete equations, considering unpredictable solutions.

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

NASA Astrophysics Data System (ADS)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

Technological Networks

NASA Astrophysics Data System (ADS)

Mitra, Bivas

The study of networks in the form of mathematical graph theory is one of the fundamental pillars of discrete mathematics. However, recent years have witnessed a substantial new movement in network research. The focus of the research is shifting away from the analysis of small graphs and the properties of individual vertices or edges to consideration of statistical properties of large scale networks. This new approach has been driven largely by the availability of technological networks like the Internet [12], World Wide Web network [2], etc. that allow us to gather and analyze data on a scale far larger than previously possible. At the same time, technological networks have evolved as a socio-technological system, as the concepts of social systems that are based on self-organization theory have become unified in technological networks [13]. In today’s society, we have a simple and universal access to great amounts of information and services. These information services are based upon the infrastructure of the Internet and the World Wide Web. The Internet is the system composed of ‘computers’ connected by cables or some other form of physical connections. Over this physical network, it is possible to exchange e-mails, transfer files, etc. On the other hand, the World Wide Web (commonly shortened to the Web) is a system of interlinked hypertext documents accessed via the Internet where nodes represent web pages and links represent hyperlinks between the pages. Peer-to-peer (P2P) networks [26] also have recently become a popular medium through which huge amounts of data can be shared. P2P file sharing systems, where files are searched and downloaded among peers without the help of central servers, have emerged as a major component of Internet traffic. An important advantage in P2P networks is that all clients provide resources, including bandwidth, storage space, and computing power. In this chapter, we discuss these technological networks in detail. The review

Adaptive dynamical networks

NASA Astrophysics Data System (ADS)

Maslennikov, O. V.; Nekorkin, V. I.

2017-10-01

Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.

A Network Coding Based Routing Protocol for Underwater Sensor Networks

PubMed Central

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime. PMID:22666045

A network coding based routing protocol for underwater sensor networks.

PubMed

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.

Directed network modules

NASA Astrophysics Data System (ADS)

Palla, Gergely; Farkas, Illés J.; Pollner, Péter; Derényi, Imre; Vicsek, Tamás

2007-06-01

A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Rényi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs.

Network connectivity value.

PubMed

Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

2017-04-21

In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant. Copyright © 2017 Elsevier Ltd. All rights reserved.

Default mode network, motor network, dorsal and ventral basal ganglia networks in the rat brain: comparison to human networks using resting state-fMRI.

PubMed

Sierakowiak, Adam; Monnot, Cyril; Aski, Sahar Nikkhou; Uppman, Martin; Li, Tie-Qiang; Damberg, Peter; Brené, Stefan

2015-01-01

Rodent models are developed to enhance understanding of the underlying biology of different brain disorders. However, before interpreting findings from animal models in a translational aspect to understand human disease, a fundamental step is to first have knowledge of similarities and differences of the biological systems studied. In this study, we analyzed and verified four known networks termed: default mode network, motor network, dorsal basal ganglia network, and ventral basal ganglia network using resting state functional MRI (rsfMRI) in humans and rats. Our work supports the notion that humans and rats have common robust resting state brain networks and that rsfMRI can be used as a translational tool when validating animal models of brain disorders. In the future, rsfMRI may be used, in addition to short-term interventions, to characterize longitudinal effects on functional brain networks after long-term intervention in humans and rats.

Default Mode Network, Motor Network, Dorsal and Ventral Basal Ganglia Networks in the Rat Brain: Comparison to Human Networks Using Resting State-fMRI

PubMed Central

Sierakowiak, Adam; Monnot, Cyril; Aski, Sahar Nikkhou; Uppman, Martin; Li, Tie-Qiang; Damberg, Peter; Brené, Stefan

2015-01-01

Rodent models are developed to enhance understanding of the underlying biology of different brain disorders. However, before interpreting findings from animal models in a translational aspect to understand human disease, a fundamental step is to first have knowledge of similarities and differences of the biological systems studied. In this study, we analyzed and verified four known networks termed: default mode network, motor network, dorsal basal ganglia network, and ventral basal ganglia network using resting state functional MRI (rsfMRI) in humans and rats. Our work supports the notion that humans and rats have common robust resting state brain networks and that rsfMRI can be used as a translational tool when validating animal models of brain disorders. In the future, rsfMRI may be used, in addition to short-term interventions, to characterize longitudinal effects on functional brain networks after long-term intervention in humans and rats. PMID:25789862

Lifting SU(2) spin networks to projected spin networks

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

Dupuis, Maiete; Livine, Etera R.

2010-09-15

Projected spin network states are the canonical basis of quantum states of geometry for the recent EPRL-FK spinfoam models for quantum gravity introduced by Engle-Pereira-Rovelli-Livine and Freidel-Krasnov. They are functionals of both the Lorentz connection and the time-normal field. We analyze in detail the map from these projected spin networks to the standard SU(2) spin networks of loop quantum gravity. We show that this map is not one to one and that the corresponding ambiguity is parameterized by the Immirzi parameter. We conclude with a comparison of the scalar products between projected spin networks and SU(2) spin network states.

Nested Neural Networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1992-01-01

Report presents analysis of nested neural networks, consisting of interconnected subnetworks. Analysis based on simplified mathematical models more appropriate for artificial electronic neural networks, partly applicable to biological neural networks. Nested structure allows for retrieval of individual subpatterns. Requires fewer wires and connection devices than fully connected networks, and allows for local reconstruction of damaged subnetworks without rewiring entire network.

A research on the application of software defined networking in satellite network architecture

NASA Astrophysics Data System (ADS)

Song, Huan; Chen, Jinqiang; Cao, Suzhi; Cui, Dandan; Li, Tong; Su, Yuxing

2017-10-01

Software defined network is a new type of network architecture, which decouples control plane and data plane of traditional network, has the feature of flexible configurations and is a direction of the next generation terrestrial Internet development. Satellite network is an important part of the space-ground integrated information network, while the traditional satellite network has the disadvantages of difficult network topology maintenance and slow configuration. The application of SDN technology in satellite network can solve these problems that traditional satellite network faces. At present, the research on the application of SDN technology in satellite network is still in the stage of preliminary study. In this paper, we start with introducing the SDN technology and satellite network architecture. Then we mainly introduce software defined satellite network architecture, as well as the comparison of different software defined satellite network architecture and satellite network virtualization. Finally, the present research status and development trend of SDN technology in satellite network are analyzed.

An iterative network partition algorithm for accurate identification of dense network modules

PubMed Central

Sun, Siqi; Dong, Xinran; Fu, Yao; Tian, Weidong

2012-01-01

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks. PMID:22121225

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

Local area networking: Ames centerwide network

NASA Technical Reports Server (NTRS)

Price, Edwin

1988-01-01

A computer network can benefit the user by making his/her work quicker and easier. A computer network is made up of seven different layers with the lowest being the hardware, the top being the user, and the middle being the software. These layers are discussed.

General invertible transformation and physical degrees of freedom

NASA Astrophysics Data System (ADS)

Takahashi, Kazufumi; Motohashi, Hayato; Suyama, Teruaki; Kobayashi, Tsutomu

2017-04-01

An invertible field transformation is such that the old field variables correspond one-to-one to the new variables. As such, one may think that two systems that are related by an invertible transformation are physically equivalent. However, if the transformation depends on field derivatives, the equivalence between the two systems is nontrivial due to the appearance of higher derivative terms in the equations of motion. To address this problem, we prove the following theorem on the relation between an invertible transformation and Euler-Lagrange equations: If the field transformation is invertible, then any solution of the original set of Euler-Lagrange equations is mapped to a solution of the new set of Euler-Lagrange equations, and vice versa. We also present applications of the theorem to scalar-tensor theories.

High fidelity wireless network evaluation for heterogeneous cognitive radio networks

NASA Astrophysics Data System (ADS)

Ding, Lei; Sagduyu, Yalin; Yackoski, Justin; Azimi-Sadjadi, Babak; Li, Jason; Levy, Renato; Melodia, Tammaso

2012-06-01

We present a high fidelity cognitive radio (CR) network emulation platform for wireless system tests, measure- ments, and validation. This versatile platform provides the configurable functionalities to control and repeat realistic physical channel effects in integrated space, air, and ground networks. We combine the advantages of scalable simulation environment with reliable hardware performance for high fidelity and repeatable evaluation of heterogeneous CR networks. This approach extends CR design only at device (software-defined-radio) or lower-level protocol (dynamic spectrum access) level to end-to-end cognitive networking, and facilitates low-cost deployment, development, and experimentation of new wireless network protocols and applications on frequency- agile programmable radios. Going beyond the channel emulator paradigm for point-to-point communications, we can support simultaneous transmissions by network-level emulation that allows realistic physical-layer inter- actions between diverse user classes, including secondary users, primary users, and adversarial jammers in CR networks. In particular, we can replay field tests in a lab environment with real radios perceiving and learning the dynamic environment thereby adapting for end-to-end goals over distributed spectrum coordination channels that replace the common control channel as a single point of failure. CR networks offer several dimensions of tunable actions including channel, power, rate, and route selection. The proposed network evaluation platform is fully programmable and can reliably evaluate the necessary cross-layer design solutions with configurable op- timization space by leveraging the hardware experiments to represent the realistic effects of physical channel, topology, mobility, and jamming on spectrum agility, situational awareness, and network resiliency. We also provide the flexibility to scale up the test environment by introducing virtual radios and establishing seamless signal

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Differential network as an indicator of osteoporosis with network entropy.

PubMed

Ma, Lili; Du, Hongmei; Chen, Guangdong

2018-07-01

Osteoporosis is a common skeletal disorder characterized by a decrease in bone mass and density. The peak bone mass (PBM) is a significant determinant of osteoporosis. To gain insights into the indicating effect of PBM to osteoporosis, this study focused on characterizing the PBM networks and identifying key genes. One biological data set with 12 monocyte low PBM samples and 11 high PBM samples was derived to construct protein-protein interaction networks (PPINs). Based on clique-merging, module-identification algorithm was used to identify modules from PPINs. The systematic calculation and comparison were performed to test whether the network entropy can discriminate the low PBM network from high PBM network. We constructed 32 destination networks with 66 modules divided from monocyte low and high PBM networks. Among them, network 11 was the only significantly differential one (P<0.05) with 8 nodes and 28 edges. All genes belonged to precursors of osteoclasts, which were related to calcium transport as well as blood monocytes. In conclusion, based on the entropy in PBM PPINs, the differential network appears to be a novel therapeutic indicator for osteoporosis during the bone monocyte progression; these findings are helpful in disclosing the pathogenetic mechanisms of osteoporosis.

Understanding resilience in industrial symbiosis networks: insights from network analysis.

PubMed

Chopra, Shauhrat S; Khanna, Vikas

2014-08-01

Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validating Large Scale Networks Using Temporary Local Scale Networks

USDA-ARS?s Scientific Manuscript database

The USDA NRCS Soil Climate Analysis Network and NOAA Climate Reference Networks are nationwide meteorological and land surface data networks with soil moisture measurements in the top layers of soil. There is considerable interest in scaling these point measurements to larger scales for validating ...

Heterogeneous Spacecraft Networks

NASA Technical Reports Server (NTRS)

Nakamura, Yosuke (Inventor); Faber, Nicolas T. (Inventor); Frost, Chad R. (Inventor); Alena, Richard L. (Inventor)

2018-01-01

The present invention provides a heterogeneous spacecraft network including a network management architecture to facilitate communication between a plurality of operations centers and a plurality of data user communities. The network management architecture includes a plurality of network nodes in communication with the plurality of operations centers. The present invention also provides a method of communication for a heterogeneous spacecraft network. The method includes: transmitting data from a first space segment to a first ground segment; transmitting the data from the first ground segment to a network management architecture; transmitting data from a second space segment to a second ground segment, the second space and ground segments having incompatible communication systems with the first space and ground segments; transmitting the data from the second ground station to the network management architecture; and, transmitting data from the network management architecture to a plurality of data user communities.

Migration of optical core network to next generation networks - Carrier Grade Ethernet Optical Transport Network

NASA Astrophysics Data System (ADS)

Glamočanin, D.

2017-05-01

In order to maintain the continuity of the telecom operators’ network construction, while monitoring development needs, increasing customers’ demands and application of technological improvements, it is necessary to migrate optical transport core network to the next generation networks - Carrier Grade Ethernet Optical Transport Network (OTN CE). The primary objective of OTN CE is to realize an environment that is based solely on the switching in the optical domain, i.e. the realization of transparent optical networks and optical switching to the second layer of ISO / OSI model. The realization of such a network provides opportunities for further development of existing, but also technologically more demanding, new services. It is also a prerequisite to provide higher scalability, reliability, security and quality of QoS service, as well as prerequisites for the establishment of SLA (Service Level Agreement) for existing services, especially traffic in real time. This study aims to clarify the proposed model, which has the potential to be eventually adjusted in accordance with new scientific knowledge in this field as well as market requirements.

Resource constrained design of artificial neural networks using comparator neural network

NASA Technical Reports Server (NTRS)

Wah, Benjamin W.; Karnik, Tanay S.

1992-01-01

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Ecological network analysis for a virtual water network.

PubMed

Fang, Delin; Chen, Bin

2015-06-02

The notions of virtual water flows provide important indicators to manifest the water consumption and allocation between different sectors via product transactions. However, the configuration of virtual water network (VWN) still needs further investigation to identify the water interdependency among different sectors as well as the network efficiency and stability in a socio-economic system. Ecological network analysis is chosen as a useful tool to examine the structure and function of VWN and the interactions among its sectors. A balance analysis of efficiency and redundancy is also conducted to describe the robustness (RVWN) of VWN. Then, network control analysis and network utility analysis are performed to investigate the dominant sectors and pathways for virtual water circulation and the mutual relationships between pairwise sectors. A case study of the Heihe River Basin in China shows that the balance between efficiency and redundancy is situated on the left side of the robustness curve with less efficiency and higher redundancy. The forestation, herding and fishing sectors and industrial sectors are found to be the main controllers. The network tends to be more mutualistic and synergic, though some competitive relationships that weaken the virtual water circulation still exist.

Network motif frequency vectors reveal evolving metabolic network organisation.

PubMed

Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

2015-01-01

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

Integrated Networks.

ERIC Educational Resources Information Center

Robinovitz, Stewart

1987-01-01

A strategy for integrated data and voice networks implemented at the University of Michigan is described. These networks often use multi-technologies, multi-vendors, and multi-transmission media that will be fused into a single integrated network. Transmission media include twisted-pair wire, coaxial cable, fiber optics, and microwave. (Author/MLW)

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-06-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan; Jersey Inst Ansari, New; Jersey Inst, New

2005-04-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-05-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or

Editorial [Special issue on software defined networks and infrastructures, network function virtualisation, autonomous systems and network management

DOE PAGES

Biswas, Amitava; Liu, Chen; Monga, Inder; ...

2016-01-01

For last few years, there has been a tremendous growth in data traffic due to high adoption rate of mobile devices and cloud computing. Internet of things (IoT) will stimulate even further growth. This is increasing scale and complexity of telecom/internet service provider (SP) and enterprise data centre (DC) compute and network infrastructures. As a result, managing these large network-compute converged infrastructures is becoming complex and cumbersome. To cope up, network and DC operators are trying to automate network and system operations, administrations and management (OAM) functions. OAM includes all non-functional mechanisms which keep the network running.

Bio-Inspired Networking — Self-Organizing Networked Embedded Systems

NASA Astrophysics Data System (ADS)

Dressler, Falko

The turn to nature has brought us many unforeseen great concepts and solutions. This course seems to hold on for many research domains. In this article, we study the applicability of biological mechanisms and techniques in the domain of communications. In particular, we study the behavior and the challenges in networked embedded systems that are meant to self-organize in large groups of nodes. Application examples include wireless sensor networks and sensor/actuator networks. Based on a review of the needs and requirements in such networks, we study selected bio-inspired networking approaches that claim to outperform other methods in specific domains. We study mechanisms in swarm intelligence, the artificial immune system, and approaches based on investigations on the cellular signaling pathways. As a major conclusion, we derive that bio-inspired networking techniques do have advantages compared to engineering methods. Nevertheless, selection and employment must be done carefully to achieve the desired performance gains.

Research in Network Management Techniques for Tactical Data Communications Network.

DTIC Science & Technology

1982-09-01

the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques...contro!lers are designed to perform their limited tasks optimally. For the dynamic routing problem considered here, the local controllers are node...feedback to finding in optimum stead-o-state routing (static strategies) under non - control which can be easily implemented in real time. congested

Class network routing

DOEpatents

Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2009-09-08

Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

Properties of healthcare teaming networks as a function of network construction algorithms.

PubMed

Zand, Martin S; Trayhan, Melissa; Farooq, Samir A; Fucile, Christopher; Ghoshal, Gourab; White, Robert J; Quill, Caroline M; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy

2017-01-01

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and

Properties of healthcare teaming networks as a function of network construction algorithms

PubMed Central

Trayhan, Melissa; Farooq, Samir A.; Fucile, Christopher; Ghoshal, Gourab; White, Robert J.; Quill, Caroline M.; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy

2017-01-01

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106–108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

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

Correlations in star networks: from Bell inequalities to network inequalities

NASA Astrophysics Data System (ADS)

Tavakoli, Armin; Olivier Renou, Marc; Gisin, Nicolas; Brunner, Nicolas

2017-07-01

The problem of characterizing classical and quantum correlations in networks is considered. Contrary to the usual Bell scenario, where distant observers share a physical system emitted by one common source, a network features several independent sources, each distributing a physical system to a subset of observers. In the quantum setting, the observers can perform joint measurements on initially independent systems, which may lead to strong correlations across the whole network. In this work, we introduce a technique to systematically map a Bell inequality to a family of Bell-type inequalities bounding classical correlations on networks in a star-configuration. Also, we show that whenever a given Bell inequality can be violated by some entangled state ρ, then all the corresponding network inequalities can be violated by considering many copies of ρ distributed in the star network. The relevance of these ideas is illustrated by applying our method to a specific multi-setting Bell inequality. We derive the corresponding network inequalities, and study their quantum violations.

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

NASA Astrophysics Data System (ADS)

Zhang, Liang; Lu, Na

2017-12-01

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

Shared protection based virtual network mapping in space division multiplexing optical networks

NASA Astrophysics Data System (ADS)

Zhang, Huibin; Wang, Wei; Zhao, Yongli; Zhang, Jie

2018-05-01

Space Division Multiplexing (SDM) has been introduced to improve the capacity of optical networks. In SDM optical networks, there are multiple cores/modes in each fiber link, and spectrum resources are multiplexed in both frequency and core/modes dimensions. Enabled by network virtualization technology, one SDM optical network substrate can be shared by several virtual networks operators. Similar with point-to-point connection services, virtual networks (VN) also need certain survivability to guard against network failures. Based on customers' heterogeneous requirements on the survivability of their virtual networks, this paper studies the shared protection based VN mapping problem and proposes a Minimum Free Frequency Slots (MFFS) mapping algorithm to improve spectrum efficiency. Simulation results show that the proposed algorithm can optimize SDM optical networks significantly in terms of blocking probability and spectrum utilization.

A Network Access Control Framework for 6LoWPAN Networks

PubMed Central

Oliveira, Luís M. L.; Rodrigues, Joel J. P. C.; de Sousa, Amaro F.; Lloret, Jaime

2013-01-01

Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes. PMID:23334610

Networking in 1993.

ERIC Educational Resources Information Center

Clement, John; Abrahams, Janice

1994-01-01

Describes the growth and evolution of educational networking including the growth in the number of users; networking tools such as Gopher; Internet information resources; problems; evaluations of network use in education; the evolution of educational communities on the Internet; integrating networks into the process of educational change; and the…

Interorganizational relationships within state tobacco control networks: a social network analysis.

PubMed

Krauss, Melissa; Mueller, Nancy; Luke, Douglas

2004-10-01

State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

A Network Primer: Full-Fledged Educational Networks.

ERIC Educational Resources Information Center

Lehrer, Ariella

1988-01-01

Discusses some of the factors included in choosing appropriate computer networks for the classroom. Describes such networks as those produced by Apple Computer, Corvus Systems, Velan, Berkeley Softworks, Tandy, LAN-TECH, Unisys, and International Business Machines (IBM). (TW)

Network planning study of the metro-optical-network-oriented 3G application

NASA Astrophysics Data System (ADS)

Gong, Qian; Xu, Rong; Lin, Jin Tong

2005-02-01

To compare with the 2G mobile communication, 3G technologies can supply the perfect service scope and performance. 3G is the trend of the mobile communication. So now to build the transmission network, it is needed to consider how the transmission network to support the 3G applications. For the 3G network architecture, it include the 2 part: Utran access network and core network. So the metro optical network should consider how to build the network to adapt the 3G applications. Include the metro core and access layer. In the metro core, we should consider the network should evolved towards the Mesh architecture with ASON function to realize the fast protection and restoration, quick end-to-end service provision, and high capacity cross-connect matrix etc. In the access layer, the network should have the ability to access the 3G services such as ATM interface with IMA function. In addition, the traffic grooming should be provided to improve the bandwidth utility. In this paper, first we present the MCC network situation, the network planning model will be introduced. Then we present the topology architecture, node capacity and traffic forecast. At last, based on our analysis, we will give a total solution to MCC to build their metro optical network toward to the mesh network with the consideration of 3G services.

Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy.

PubMed

Widjaja, E; Zamyadi, M; Raybaud, C; Snead, O C; Smith, M L

2013-12-01

Epilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function. Fifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups. The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal

Optical network democratization.

PubMed

Nejabati, Reza; Peng, Shuping; Simeonidou, Dimitra

2016-03-06

The current Internet infrastructure is not able to support independent evolution and innovation at physical and network layer functionalities, protocols and services, while at same time supporting the increasing bandwidth demands of evolving and heterogeneous applications. This paper addresses this problem by proposing a completely democratized optical network infrastructure. It introduces the novel concepts of the optical white box and bare metal optical switch as key technology enablers for democratizing optical networks. These are programmable optical switches whose hardware is loosely connected internally and is completely separated from their control software. To alleviate their complexity, a multi-dimensional abstraction mechanism using software-defined network technology is proposed. It creates a universal model of the proposed switches without exposing their technological details. It also enables a conventional network programmer to develop network applications for control of the optical network without specific technical knowledge of the physical layer. Furthermore, a novel optical network virtualization mechanism is proposed, enabling the composition and operation of multiple coexisting and application-specific virtual optical networks sharing the same physical infrastructure. Finally, the optical white box and the abstraction mechanism are experimentally evaluated, while the virtualization mechanism is evaluated with simulation. © 2016 The Author(s).

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Networking Ethics: A Survey of Bioethics Networks Across the U.S.

PubMed

Fausett, Jennifer Kleiner; Gilmore-Szott, Eleanor; Hester, D Micah

2016-06-01

Ethics networks have emerged over the last few decades as a mechanism for individuals and institutions over various regions, cities and states to converge on healthcare-related ethical issues. However, little is known about the development and nature of such networks. In an effort to fill the gap in the knowledge about such networks, a survey was conducted that evaluated the organizational structure, missions and functions, as well as the outcomes/products of ethics networks across the country. Eighteen established bioethics networks were identified via consensus of three search processes and were approached for participation. The participants completed a survey developed for the purposes of this study and distributed via SurveyMonkey. Responses were obtained from 10 of the 18 identified and approached networks regarding topic areas of: Network Composition and Catchment Areas; Network Funding and Expenses; Personnel; Services; and Missions and Accomplishments. Bioethics networks are designed primarily to bring ethics education and support to professionals and hospitals. They do so over specifically defined areas-states, regions, or communities-and each is concerned about how to stay financially healthy. At the same time, the networks work off different organizational models, either as stand-alone organizations or as entities within existing organizational structures.

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

NASA Astrophysics Data System (ADS)

Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos

2016-04-01

Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and