Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network.

PubMed

Savitha, R; Suresh, S; Sundararajan, N

2012-08-01

This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-valued neural network called "Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN)". McFCRN has two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Relaxation Network (FCRN) with a fully complex-valued Gaussian like activation function (sech) in the hidden layer and an exponential activation function in the output layer forms the cognitive component. The meta-cognitive component contains a self-regulatory learning mechanism which controls the learning ability of FCRN by deciding what-to-learn, when-to-learn and how-to-learn from a sequence of training data. The input parameters of cognitive components are chosen randomly and the output parameters are estimated by minimizing a logarithmic error function. The problem of explicit minimization of magnitude and phase errors in the logarithmic error function is converted to system of linear equations and output parameters of FCRN are computed analytically. McFCRN starts with zero hidden neuron and builds the number of neurons required to approximate the target function. The meta-cognitive component selects the best learning strategy for FCRN to acquire the knowledge from training data and also adapts the learning strategies to implement best human learning components. Performance studies on a function approximation and real-valued classification problems show that proposed McFCRN performs better than the existing results reported in the literature. Copyright © 2012 Elsevier Ltd. All rights reserved.

Numerical Nonlinear Robust Control with Applications to Humanoid Robots

DTIC Science & Technology

2015-07-01

automatically. While optimization and optimal control theory have been widely applied in humanoid robot control, it is not without drawbacks . A blind... drawback of Galerkin-based approaches is the need to successively produce discrete forms, which is difficult to implement in practice. Related...universal function approx- imation ability, these approaches are not without drawbacks . In practice, while a single hidden layer neural network can

Detection and electrical characterization of hidden layers using time-domain analysis of terahertz reflections

NASA Astrophysics Data System (ADS)

Geltner, I.; Hashimshony, D.; Zigler, A.

2002-07-01

We use a time-domain analysis method to characterize the outer layer of a multilayer structure regardless of the inner ones, thus simplifying the characterization of all the layers. We combine this method with THz reflection spectroscopy to detect nondestructively a hidden aluminum oxide layer under opaque paint and to measure its conductivity and high-frequency dielectric constant in the THz range.

Bounds on the number of hidden neurons in three-layer binary neural networks.

PubMed

Zhang, Zhaozhi; Ma, Xiaomin; Yang, Yixian

2003-09-01

This paper investigates an important problem concerning the complexity of three-layer binary neural networks (BNNs) with one hidden layer. The neuron in the studied BNNs employs a hard limiter activation function with only integer weights and an integer threshold. The studies are focused on implementations of arbitrary Boolean functions which map from [0, 1]n into [0, 1]. A deterministic algorithm called set covering algorithm (SCA) is proposed for the construction of a three-layer BNN to implement an arbitrary Boolean function. The SCA is based on a unit sphere covering (USC) of the Hamming space (HS) which is chosen in advance. It is proved that for the implementation of an arbitrary Boolean function of n-variables (n > or = 3) by using SCA, [3L/2] hidden neurons are necessary and sufficient, where L is the number of unit spheres contained in the chosen USC of the n-dimensional HS. It is shown that by using SCA, the number of hidden neurons required is much less than that by using a two-parallel hyperplane method. In order to indicate the potential ability of three-layer BNNs, a lower bound on the required number of hidden neurons which is derived by using the method of estimating the Vapnik-Chervonenkis (VC) dimension is also given.

Computational models of location-invariant orthographic processing

NASA Astrophysics Data System (ADS)

Dandurand, Frédéric; Hannagan, Thomas; Grainger, Jonathan

2013-03-01

We trained three topologies of backpropagation neural networks to discriminate 2000 words (lexical representations) presented at different positions of a horizontal letter array. The first topology (zero-deck) contains no hidden layer, the second (one-deck) has a single hidden layer, and for the last topology (two-deck), the task is divided in two subtasks implemented as two stacked neural networks, with explicit word-centred letters as intermediate representations. All topologies successfully simulated two key benchmark phenomena observed in skilled human reading: transposed-letter priming and relative-position priming. However, the two-deck topology most accurately simulated the ability to discriminate words from nonwords, while containing the fewest connection weights. We analysed the internal representations after training. Zero-deck networks implement a letter-based scheme with a position bias to differentiate anagrams. One-deck networks implement a holographic overlap coding in which representations are essentially letter-based and words are linear combinations of letters. Two-deck networks also implement holographic-coding.

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

PubMed

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to further investigate the disease mechanisms underlying each of these clusters. In summary, we show that a deep learning model can be trained to represent biologically and clinically meaningful abstractions of cancer gene expression data. Understanding what additional relationships these hidden layer abstractions have with the cancer cellular signaling system could have a significant impact on the understanding and treatment of cancer.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Multilayer neural networks with extensively many hidden units.

PubMed

Rosen-Zvi, M; Engel, A; Kanter, I

2001-08-13

The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions, whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter, the storage capacity is found to scale with the logarithm of the number of implementable Boolean functions. The generalization behavior is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones.

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

PubMed

Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

2016-12-01

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural Network Burst Pressure Prediction in Graphite/Epoxy Pressure Vessels from Acoustic Emission Amplitude Data

NASA Technical Reports Server (NTRS)

Hill, Eric v. K.; Walker, James L., II; Rowell, Ginger H.

1995-01-01

Acoustic emission (AE) data were taken during hydroproof for three sets of ASTM standard 5.75 inch diameter filament wound graphite/epoxy bottles. All three sets of bottles had the same design and were wound from the same graphite fiber; the only difference was in the epoxies used. Two of the epoxies had similar mechanical properties, and because the acoustic properties of materials are a function of their stiffnesses, it was thought that the AE data from the two sets might also be similar; however, this was not the case. Therefore, the three resin types were categorized using dummy variables, which allowed the prediction of burst pressures all three sets of bottles using a single neural network. Three bottles from each set were used to train the network. The resin category, the AE amplitude distribution data taken up to 25 % of the expected burst pressure, and the actual burst pressures were used as inputs. Architecturally, the network consisted of a forty-three neuron input layer (a single categorical variable defining the resin type plus forty-two continuous variables for the AE amplitude frequencies), a fifteen neuron hidden layer for mapping, and a single output neuron for burst pressure prediction. The network trained on all three bottle sets was able to predict burst pressures in the remaining bottles with a worst case error of + 6.59%, slightly greater than the desired goal of + 5%. This larger than desired error was due to poor resolution in the amplitude data for the third bottle set. When the third set of bottles was eliminated from consideration, only four hidden layer neurons were necessary to generate a worst case prediction error of - 3.43%, well within the desired goal.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

2015-01-01

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems. PMID:26262687

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Modular Neural Networks for Speech Recognition.

DTIC Science & Technology

1996-08-01

automatic speech rccogni- tion, understanding and translation since the early 1950’ s . Although researchers have demonstrated impressive results with...nodes. It serves only as a data source for the following hidden layer( s ). Finally, the networks output is computed by neurons in the output layer. The...following update rule for weights in the hidden layer: w (,,•+I) ("’) E/V S (W W k- = wj, -- 7 - / v It is easy to generalize the backpropagation

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

Techniques of noninvasive optical tomographic imaging

NASA Astrophysics Data System (ADS)

Rosen, Joseph; Abookasis, David; Gokhler, Mark

2006-01-01

Recently invented methods of optical tomographic imaging through scattering and absorbing media are presented. In one method, the three-dimensional structure of an object hidden between two biological tissues is recovered from many noisy speckle pictures obtained on the output of a multi-channeled optical imaging system. Objects are recovered from many speckled images observed by a digital camera through two stereoscopic microlens arrays. Each microlens in each array generates a speckle image of the object buried between the layers. In the computer each image is Fourier transformed jointly with an image of the speckled point-like source captured under the same conditions. A set of the squared magnitudes of the Fourier-transformed pictures is accumulated to form a single average picture. This final picture is again Fourier transformed, resulting in the three-dimensional reconstruction of the hidden object. In the other method, the effect of spatial longitudinal coherence is used for imaging through an absorbing layer with different thickness, or different index of refraction, along the layer. The technique is based on synthesis of multiple peak spatial degree of coherence. This degree of coherence enables us to scan simultaneously different sample points on different altitudes, and thus decreases the acquisition time. The same multi peak degree of coherence is also used for imaging through the absorbing layer. Our entire experiments are performed with a quasi-monochromatic light source. Therefore problems of dispersion and inhomogeneous absorption are avoided.

Optimized hardware framework of MLP with random hidden layers for classification applications

NASA Astrophysics Data System (ADS)

Zyarah, Abdullah M.; Ramesh, Abhishek; Merkel, Cory; Kudithipudi, Dhireesha

2016-05-01

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

Optimizing hidden layer node number of BP network to estimate fetal weight

NASA Astrophysics Data System (ADS)

Su, Juan; Zou, Yuanwen; Lin, Jiangli; Wang, Tianfu; Li, Deyu; Xie, Tao

2007-12-01

The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Autonomous Navigation Apparatus With Neural Network for a Mobile Vehicle

NASA Technical Reports Server (NTRS)

Quraishi, Naveed (Inventor)

1996-01-01

An autonomous navigation system for a mobile vehicle arranged to move within an environment includes a plurality of sensors arranged on the vehicle and at least one neural network including an input layer coupled to the sensors, a hidden layer coupled to the input layer, and an output layer coupled to the hidden layer. The neural network produces output signals representing respective positions of the vehicle, such as the X coordinate, the Y coordinate, and the angular orientation of the vehicle. A plurality of patch locations within the environment are used to train the neural networks to produce the correct outputs in response to the distances sensed.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

PubMed

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Hidden Interface Driven Exchange Coupling in Oxide Heterostructures

DOE PAGES

Chen, Aiping; Wang, Qiang; Fitzsimmons, Michael R.; ...

2017-05-02

In a variety of emergent phenomena have been enabled by interface engineering in complex oxides. The existence of an intrinsic interfacial layer has often been found at oxide heterointerfaces. But, the role of such an interlayerin controlling functionalities is not fully explored. Here, we report the control of the exchange bias (EB) in single-phase manganite thin films with nominallyuniform chemical composition across the interfaces. The sign of EB depends on the magnitude of the cooling field. A pinned layer, confirmed by polarized neutron reflectometry, provides the source of unidirectional anisotropy. The origin of the exchange bias coupling is discussed inmore » terms of magnetic interactions between the interfacial ferromagnetically reduced layer and the bulk ferromagnetic region. The sign of EB is related to the frustration of antiferromagnetic coupling between the ferromagnetic region and the pinned layer. These results shed new light on using oxide interfaces to design functional spintronic devices.« less

A neural network based computational model to predict the output power of different types of photovoltaic cells.

PubMed

Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

2017-01-01

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

2016-05-01

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Deep generative learning of location-invariant visual word recognition.

PubMed

Di Bono, Maria Grazia; Zorzi, Marco

2013-01-01

It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words-which was the model's learning objective-is largely based on letter-level information.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Controlling the metal-to-insulator relaxation of the metastable hidden quantum state in 1T-TaS2.

PubMed

Vaskivskyi, Igor; Gospodaric, Jan; Brazovskii, Serguei; Svetin, Damjan; Sutar, Petra; Goreshnik, Evgeny; Mihailovic, Ian A; Mertelj, Tomaz; Mihailovic, Dragan

2015-07-01

Controllable switching between metastable macroscopic quantum states under nonequilibrium conditions induced either by light or with an external electric field is rapidly becoming of great fundamental interest. We investigate the relaxation properties of a "hidden" (H) charge density wave (CDW) state in thin single crystals of the layered dichalcogenide 1T-TaS2, which can be reached by either a single 35-fs optical laser pulse or an ~30-ps electrical pulse. From measurements of the temperature dependence of the resistivity under different excitation conditions, we find that the metallic H state relaxes to the insulating Mott ground state through a sequence of intermediate metastable states via discrete jumps over a "Devil's staircase." In between the discrete steps, an underlying glassy relaxation process is observed, which arises because of reciprocal-space commensurability frustration between the CDW and the underlying lattice. We show that the metastable state relaxation rate may be externally stabilized by substrate strain, thus opening the way to the design of nonvolatile ultrafast high-temperature memory devices based on switching between CDW states with large intrinsic differences in electrical resistance.

Diffuse Reflectance Spectroscopy of Hidden Objects. Part II: Recovery of a Target Spectrum.

PubMed

Pomerantsev, Alexey L; Rodionova, Oxana Ye; Skvortsov, Alexej N

2017-08-01

In this study, we consider the reconstruction of a diffuse reflectance near-infrared spectrum of an object (target spectrum) in case the object is covered by an interfering absorbing and scattering layer. Recovery is performed using a new empirical method, which was developed in our previous study. We focus on a system, which consists of several layers of polyethylene (PE) film and underlayer objects with different spectral features. The spectral contribution of the interfering layer is modeled by a three-component two-parameter multivariate curve resolution (MCR) model, which was built and calibrated using spectrally flat objects. We show that this model is applicable to real objects with non-uniform spectra. Ultimately, the target spectrum can be reconstructed from a single spectrum of the covered target. With calculation methods, we are able to recover quite accurately the spectrum of a target even when the object is covered by 0.7 mm of PE.

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

Design of double fuzzy clustering-driven context neural networks.

PubMed

Kim, Eun-Hu; Oh, Sung-Kwun; Pedrycz, Witold

2018-08-01

In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear function and the outputs of the hidden layer. The weights are updated using the least square estimation (LSE)-based method. (iii) At the output layer, the outputs of context layer are decoded to produce the corresponding numeric output. At this time, the weighted average is used and the weights are also adjusted with the use of the LSE scheme. From the viewpoint of performance improvement, the proposed design methodologies are discussed and experimented with the aid of benchmark machine learning datasets. Through the experiments, it is shown that the generalization abilities of the proposed DFCCNNs are better than those of the conventional FCNNs reported in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

A simple method to derive bounds on the size and to train multilayer neural networks

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Antsaklis, Panos J.

1991-01-01

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely, the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to no.1 is greater than p - 1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving no.1 + 1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.

A shared synapse architecture for efficient FPGA implementation of autoencoders.

PubMed

Suzuki, Akihiro; Morie, Takashi; Tamukoh, Hakaru

2018-01-01

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers' units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks.

Novel maximum-margin training algorithms for supervised neural networks.

PubMed

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by MICI, MMGDX, and Levenberg-Marquard (LM), respectively. The resulting neural network was named assembled neural network (ASNN). Benchmark data sets of real-world problems have been used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and balanced error rate.

Cascade Error Projection: A Learning Algorithm for Hardware Implementation

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher

1996-01-01

In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters. Furthermore, CEP learning algorithm is operated only on one layer, whereas the other set of weights can be calculated deterministically. In association with the dynamical stepsize change concept to convert the weight update from infinite space into a finite space, the relation between the current stepsize and the previous energy level is also given and the estimation procedure for optimal stepsize is used for validation of our proposed technique. The weight values of zero are used for starting the learning for every layer, and a single hidden unit is applied instead of using a pool of candidate hidden units similar to cascade correlation scheme. Therefore, simplicity in hardware implementation is also obtained. Furthermore, this analysis allows us to select from other methods (such as the conjugate gradient descent or the Newton's second order) one of which will be a good candidate for the learning technique. The choice of learning technique depends on the constraints of the problem (e.g., speed, performance, and hardware implementation); one technique may be more suitable than others. Moreover, for a discrete weight space, the theoretical analysis presents the capability of learning with limited weight quantization. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

PubMed

Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

2013-01-01

There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P < 0.05) and no significant difference between Cox and the neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer). Probabilities of survival were calculated using three neural network models with 3, 5, and 7 nodes in the hidden layer, and it has been observed that none of the predictions was significantly different from results with the Kaplan-Meier method and they appeared more comparable towards the last months (fifth year). However, we observed better accuracy using the neural network with 5 nodes in the hidden layer. Using the Cox proportional hazards and a neural network with 3 nodes in the hidden layer, we found enhanced accuracy with the neural network model. Neural networks can provide more accurate predictions for survival probabilities compared to the Cox proportional hazards mode, especially now that advances in computer sciences have eliminated limitations associated with complex computations. It is not recommended in order to adding too many hidden layer nodes because sample size related effects can reduce the accuracy. We recommend increasing the number of nodes to a point that increased accuracy continues (decrease in mean standard error), however increasing nodes should cease when a change in this trend is observed.

Multilayer perceptron architecture optimization using parallel computing techniques.

PubMed

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

Multilayer perceptron architecture optimization using parallel computing techniques

PubMed Central

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time. PMID:29236744

Neural node network and model, and method of teaching same

DOEpatents

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

Neural node network and model, and method of teaching same

DOEpatents

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Power of the Poincaré group: elucidating the hidden symmetries in focal conic domains.

PubMed

Alexander, Gareth P; Chen, Bryan Gin-Ge; Matsumoto, Elisabetta A; Kamien, Randall D

2010-06-25

Focal conic domains are typically the "smoking gun" by which smectic liquid crystalline phases are identified. The geometry of the equally spaced smectic layers is highly generic but, at the same time, difficult to work with. In this Letter we develop an approach to the study of focal sets in smectics which exploits a hidden Poincaré symmetry revealed only by viewing the smectic layers as projections from one-higher dimension. We use this perspective to shed light upon several classic focal conic textures, including the concentric cyclides of Dupin, polygonal textures, and tilt-grain boundaries.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

PubMed Central

Jie, Shao

2014-01-01

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

Method Accelerates Training Of Some Neural Networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O.

1992-01-01

Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

NuSTAR Seeks Hidden Black Holes

NASA Image and Video Library

2015-07-06

Top: An illustration of NASA's Nuclear Spectroscopic Telescope Array, or NuSTAR, in orbit. The unique school bus-long mast allows NuSTAR to focus high energy X-rays. Lower-left: A color image from NASA's Hubble Space Telescope of one of the nine galaxies targeted by NuSTAR in search of hidden black holes. Bottom-right: An artist's illustration of a supermassive black hole, actively feasting on its surroundings. The central black hole is hidden from direct view by a thick layer of encircling gas and dust. http://photojournal.jpl.nasa.gov/catalog/PIA19348

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Imaging of endodontic biofilms by combined microscopy (FISH/cLSM - SEM).

PubMed

Schaudinn, C; Carr, G; Gorur, A; Jaramillo, D; Costerton, J W; Webster, P

2009-08-01

Scanning electron microscopy is a useful imaging approach for the visualization of bacterial biofilms in their natural environments including their medical and dental habitats, because it allows for the exploration of large surfaces with excellent resolution of topographic features. Most biofilms in nature, however, are embedded in a thick layer of extracellular matrix that prevents a clear identification of individual bacteria by scanning electron microscopy. The use of confocal laser scanning microscopy on the other hand in combination with fluorescence in situ hybridization enables the visualization of matrix embedded bacteria in multi-layered biofilms. In our study, fluorescence in situ hybridization/confocal laser scanning microscopy and scanning electron microscopy were applied to visualize bacterial biofilm in endodontic root canals. The resulting fluorescence in situ hybridization /confocal laser scanning microscopy and scanning electron microscopy and pictures were subsequently combined into one single image to provide high-resolution information on the location of hidden bacteria. The combined use of scanning electron microscopy and fluorescence in situ hybridization / confocal laser scanning microscopy has the potential to overcome the limits of each single technique.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Alternative sensor system and MLP neural network for vehicle pedal activity estimation.

PubMed

Wefky, Ahmed M; Espinosa, Felipe; Jiménez, José A; Santiso, Enrique; Rodríguez, José M; Fernández, Alfredo J

2010-01-01

It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch) reflects the driver's behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration) that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

PubMed Central

Wefky, Ahmed M.; Espinosa, Felipe; Jiménez, José A.; Santiso, Enrique; Rodríguez, José M.; Fernández, Alfredo J.

2010-01-01

It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch) reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration) that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer. PMID:22319326

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Fast and robust group-wise eQTL mapping using sparse graphical models.

PubMed

Cheng, Wei; Shi, Yu; Zhang, Xiang; Wang, Wei

2015-01-16

Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways. We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods. Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

2016-06-01

While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

Application of shift-and-add algorithms for imaging objects within biological media

NASA Astrophysics Data System (ADS)

Aizert, Avishai; Moshe, Tomer; Abookasis, David

2017-01-01

The Shift-and-Add (SAA) technique is a simple mathematical operation developed to reconstruct, at high spatial resolution, atmospherically degraded solar images obtained from stellar speckle interferometry systems. This method shifts and assembles individual degraded short-exposure images into a single average image with significantly improved contrast and detail. Since the inhomogeneous refractive indices of biological tissue causes light scattering similar to that induced by optical turbulence in the atmospheric layers, we assume that SAA methods can be successfully implemented to reconstruct the image of an object within a scattering biological medium. To test this hypothesis, five SAA algorithms were evaluated for reconstructing images acquired from multiple viewpoints. After successfully retrieving the hidden object's shape, quantitative image quality metrics were derived, enabling comparison of imaging error across a spectrum of layer thicknesses, demonstrating the relative efficacy of each SAA algorithm for biological imaging.

New ARCH: Future Generation Internet Architecture

DTIC Science & Technology

2004-08-01

a vocabulary to talk about a system . This provides a framework ( a “reference model ...layered model Modularity and abstraction are central tenets of Computer Science thinking. Modularity breaks a system into parts, normally to permit...this complexity is hidden. Abstraction suggests a structure for the system . A popular and simple structure is a layered model : lower layer

Parsimonious extreme learning machine using recursive orthogonal least squares.

PubMed

Wang, Ning; Er, Meng Joo; Han, Min

2014-10-01

Novel constructive and destructive parsimonious extreme learning machines (CP- and DP-ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure and excellent generalization of multiinput-multioutput single hidden-layer feedforward networks (SLFNs) are obtained. The proposed ELMs are developed by innovative decomposition of the recursive orthogonal least squares procedure into sequential partial orthogonalization (SPO). The salient features of the proposed approaches are as follows: 1) Initial hidden nodes are randomly generated by the ELM methodology and recursively orthogonalized into an upper triangular matrix with dramatic reduction in matrix size; 2) the constructive SPO in the CP-ELM focuses on the partial matrix with the subcolumn of the selected regressor including nonzeros as the first column while the destructive SPO in the DP-ELM operates on the partial matrix including elements determined by the removed regressor; 3) termination criteria for CP- and DP-ELM are simplified by the additional residual error reduction method; and 4) the output weights of the SLFN need not be solved in the model selection procedure and is derived from the final upper triangular equation by backward substitution. Both single- and multi-output real-world regression data sets are used to verify the effectiveness and superiority of the CP- and DP-ELM in terms of parsimonious architecture and generalization accuracy. Innovative applications to nonlinear time-series modeling demonstrate superior identification results.

Hidden One-Dimensional Electronic Structure of η-Mo_4O_11

NASA Astrophysics Data System (ADS)

Gweon, G.-H.; Mo, S.-K.; Allen, J. W.; Höchst, H.; Sarrao, J. L.; Fisk, Z.

2002-03-01

η-Mo_4O_11 is a layered metal that undergoes two charge density wave (CDW) transitions at 109 K and 30 K, and is unique in showing a bulk quantum Hall effect. Research so far indicates that this material has a ``hidden one-dimensional'' (hidden-1d) Fermi surface (FS) in the normal state (T > 109 K), whose nesting property drives the 109 K CDW formation. Here, we directly confirm this picture by angle resolved photoemission spectroscopy (ARPES). We also observe a gap opening associated with the 109 K transition. Most interesting, this material shows the same ARPES line shape anomalies that suggest electron fractionalization in other hidden-1d materials like NaMo_6O_17 and KMo_6O_17. Studies of the 30 K transition are in progress.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Experimental test of state-independent quantum contextuality of an indivisible quantum system

NASA Astrophysics Data System (ADS)

Li, Meng; Huang, Yun-Feng; Cao, Dong-Yang; Zhang, Chao; Zhang, Yong-Sheng; Liu, Bi-Heng; Li, Chuan-Feng; Guo, Guang-Can

2014-05-01

Since the quantum mechanics was born, quantum mechanics was argued among scientists because the differences between quantum mechanics and the classical physics. Because of this, some people give hidden variable theory. One of the hidden variable theory is non-contextual hidden variable theory, and KS inequalities are famous in non-contextual hidden variable theory. But the original KS inequalities have 117 directions to measure, so it is almost impossible to test the KS inequalities in experiment. However bout two years ago, Sixia Yu and C.H. Oh point out that for a single qutrit, we only need to measure 13 directions, then we can test the KS inequalities. This makes it possible to test the KS inequalities in experiment. We use the polarization and the path of single photon to construct a qutrit, and we use the half-wave plates, the beam displacers and polar beam splitters to prepare the quantum state and finish the measurement. And the result prove that quantum mechanics is right and non-contextual hidden variable theory is wrong.

Eastern Sahara Geology from Orbital Radar: Potential Analog to Mars

NASA Technical Reports Server (NTRS)

Farr, T. G.; Paillou, P.; Heggy, E.

2004-01-01

Much of the surface of Mars has been intensely reworked by aeolian processes and key evidence about the history of the Martian environment seems to be hidden beneath a widespread layer of debris (paleo lakes and rivers, faults, impact craters). In the same way, the recent geological and hydrological history of the eastern Sahara is still mainly hidden under large regions of wind-blown sand which represent a possible terrestrial analog to Mars. The subsurface geology there is generally invisible to optical remote sensing techniques, but radar images obtained from the Shuttle Imaging Radar (SIR) missions were able to penetrate the superficial sand layer to reveal parts of paleohydrological networks in southern Egypt.

The Power of Poincaré: Elucidating the Hidden Symmetries in Focal Conic Domains

NASA Astrophysics Data System (ADS)

Matsumoto, Elisabetta A.; Alexander, Gareth P.; Chen, Bryan Gin-Ge; Kamien, Randall D.

2011-03-01

Focal conic domains are typically the ``smoking gun'' by which smectic liquid crystalline phases are identified. The geometry of the equally spaced smectic layers is highly generic but, at the same time, difficult to work with. We develop an approach to the study of focal sets in smectics which exploits a hidden Poincaré symmetry revealed only by viewing the smectic layers as projections from one-higher dimension. We use this perspective to shed light upon the concentric cyclides of Dupin and several classic focal conic textures which exhibit a more widespread level of geometric organization as in Friedel's law of corresponding cones, the networks and trellises expounded by Bouligand, or Apollonian packings.

A new learning algorithm for a fully connected neuro-fuzzy inference system.

PubMed

Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

2014-10-01

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

A shared synapse architecture for efficient FPGA implementation of autoencoders

PubMed Central

Morie, Takashi; Tamukoh, Hakaru

2018-01-01

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers’ units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks. PMID:29543909

High-frequency guided ultrasonic waves for hidden defect detection in multi-layered aircraft structures.

PubMed

Masserey, Bernard; Raemy, Christian; Fromme, Paul

2014-09-01

Aerospace structures often contain multi-layered metallic components where hidden defects such as fatigue cracks and localized disbonds can develop, necessitating non-destructive testing. Employing standard wedge transducers, high frequency guided ultrasonic waves that penetrate through the complete thickness were generated in a model structure consisting of two adhesively bonded aluminium plates. Interference occurs between the wave modes during propagation along the structure, resulting in a frequency dependent variation of the energy through the thickness with distance. The wave propagation along the specimen was measured experimentally using a laser interferometer. Good agreement with theoretical predictions and two-dimensional finite element simulations was found. Significant propagation distance with a strong, non-dispersive main wave pulse was achieved. The interaction of the high frequency guided ultrasonic waves with small notches in the aluminium layer facing the sealant and on the bottom surface of the multilayer structure was investigated. Standard pulse-echo measurements were conducted to verify the detection sensitivity and the influence of the stand-off distance predicted from the finite element simulations. The results demonstrated the potential of high frequency guided waves for hidden defect detection at critical and difficult to access locations in aerospace structures from a stand-off distance. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

PubMed

Tu, Junchao; Zhang, Liyan

2018-01-12

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.

PubMed

Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L 2 -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

PubMed Central

Abuassba, Adnan O. M.; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets. PMID:28546808

Noise-free accurate count of microbial colonies by time-lapse shadow image analysis.

PubMed

Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hisakage; Saito, Mikako; Matsuoka, Hideaki

2012-12-01

Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance. Copyright © 2012 Elsevier B.V. All rights reserved.

Hidden multiparticle excitation in a weakly interacting Bose-Einstein condensate

NASA Astrophysics Data System (ADS)

Watabe, Shohei

2018-03-01

We investigate multiparticle excitation effect on a collective density excitation as well as a single-particle excitation in a weakly interacting Bose-Einstein condensate (BEC). We find that although the weakly interacting BEC offers weak multiparticle excitation spectrum at low temperatures, this multiparticle excitation effect may not remain hidden, but emerges as bimodality in the density response function through the single-particle excitation. Identification of spectra in the BEC between the single-particle excitation and the density excitation is also assessed at nonzero temperatures, which has been known to be unique nature in the BEC at absolute zero temperature.

A Hidden Markov Model for Single Particle Tracks Quantifies Dynamic Interactions between LFA-1 and the Actin Cytoskeleton

PubMed Central

Das, Raibatak; Cairo, Christopher W.; Coombs, Daniel

2009-01-01

The extraction of hidden information from complex trajectories is a continuing problem in single-particle and single-molecule experiments. Particle trajectories are the result of multiple phenomena, and new methods for revealing changes in molecular processes are needed. We have developed a practical technique that is capable of identifying multiple states of diffusion within experimental trajectories. We model single particle tracks for a membrane-associated protein interacting with a homogeneously distributed binding partner and show that, with certain simplifying assumptions, particle trajectories can be regarded as the outcome of a two-state hidden Markov model. Using simulated trajectories, we demonstrate that this model can be used to identify the key biophysical parameters for such a system, namely the diffusion coefficients of the underlying states, and the rates of transition between them. We use a stochastic optimization scheme to compute maximum likelihood estimates of these parameters. We have applied this analysis to single-particle trajectories of the integrin receptor lymphocyte function-associated antigen-1 (LFA-1) on live T cells. Our analysis reveals that the diffusion of LFA-1 is indeed approximately two-state, and is characterized by large changes in cytoskeletal interactions upon cellular activation. PMID:19893741

Reservoir computing on the hypersphere

NASA Astrophysics Data System (ADS)

Andrecut, M.

Reservoir Computing (RC) refers to a Recurrent Neural Network (RNNs) framework, frequently used for sequence learning and time series prediction. The RC system consists of a random fixed-weight RNN (the input-hidden reservoir layer) and a classifier (the hidden-output readout layer). Here, we focus on the sequence learning problem, and we explore a different approach to RC. More specifically, we remove the nonlinear neural activation function, and we consider an orthogonal reservoir acting on normalized states on the unit hypersphere. Surprisingly, our numerical results show that the system’s memory capacity exceeds the dimensionality of the reservoir, which is the upper bound for the typical RC approach based on Echo State Networks (ESNs). We also show how the proposed system can be applied to symmetric cryptography problems, and we include a numerical implementation.

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-04-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-01-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

A Near Zero Refractive Index Metamaterial for Electromagnetic Invisibility Cloaking Operation

PubMed Central

Islam, Sikder Sunbeam; Faruque, Mohammad Rashed Iqbal; Islam, Mohammad Tariqul

2015-01-01

The paper reveals the design of a unit cell of a metamaterial that shows more than 2 GHz wideband near zero refractive index (NZRI) property in the C-band region of microwave spectra. The two arms of the unit cell were splitted in such a way that forms a near-pi-shape structure on epoxy resin fiber (FR-4) substrate material. The reflection and transmission characteristics of the unit cell were achieved by utilizing finite integration technique based simulation software. Measured results were presented, which complied well with simulated results. The unit cell was then applied to build a single layer rectangular-shaped cloak that operates in the C-band region where a metal cylinder was perfectly hidden electromagnetically by reducing the scattering width below zero. Moreover, the unit cell shows NZRI property there. The experimental result for the cloak operation was presented in terms of S-parameters as well. In addition, the same metamaterial shell was also adopted for designing an eye-shaped and triangular-shaped cloak structure to cloak the same object, and cloaking operation is achieved in the C-band, as well with slightly better cloaking performance. The novel design, NZRI property, and single layer C-band cloaking operation has made the design a promising one in the electromagnetic paradigm. PMID:28793472

A Near Zero Refractive Index Metamaterial for Electromagnetic Invisibility Cloaking Operation.

PubMed

Islam, Sikder Sunbeam; Faruque, Mohammad Rashed Iqbal; Islam, Mohammad Tariqul

2015-07-29

The paper reveals the design of a unit cell of a metamaterial that shows more than 2 GHz wideband near zero refractive index (NZRI) property in the C-band region of microwave spectra. The two arms of the unit cell were splitted in such a way that forms a near-pi-shape structure on epoxy resin fiber (FR-4) substrate material. The reflection and transmission characteristics of the unit cell were achieved by utilizing finite integration technique based simulation software. Measured results were presented, which complied well with simulated results. The unit cell was then applied to build a single layer rectangular-shaped cloak that operates in the C-band region where a metal cylinder was perfectly hidden electromagnetically by reducing the scattering width below zero. Moreover, the unit cell shows NZRI property there. The experimental result for the cloak operation was presented in terms of S-parameters as well. In addition, the same metamaterial shell was also adopted for designing an eye-shaped and triangular-shaped cloak structure to cloak the same object, and cloaking operation is achieved in the C-band, as well with slightly better cloaking performance. The novel design, NZRI property, and single layer C-band cloaking operation has made the design a promising one in the electromagnetic paradigm.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

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

PubMed Central

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

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided. PMID:28871217

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

NASA Astrophysics Data System (ADS)

Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj

2012-05-01

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

Character Recognition Using Genetically Trained Neural Networks

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

Diniz, C.; Stantz, K.M.; Trahan, M.W.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfidmore » recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of noise significantly degrades character recognition efficiency, some of which can be overcome by adding noise during training and optimizing the form of the network's activation fimction.« less

Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

PubMed

Poernomo, Alvin; Kang, Dae-Ki

2018-08-01

Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been thoroughly explored. Moreover, training a Dropout model will significantly increase the training time as it takes longer time to converge than a non-Dropout model with the same architecture. To deal with these issues, we address Biased Dropout and Crossmap Dropout, two novel approaches of Dropout extension based on the behavior of hidden units in CNN model. Biased Dropout divides the hidden units in a certain layer into two groups based on their magnitude and applies different Dropout rate to each group appropriately. Hidden units with higher activation value, which give more contributions to the network final performance, will be retained by a lower Dropout rate, while units with lower activation value will be exposed to a higher Dropout rate to compensate the previous part. The second approach is Crossmap Dropout, which is an extension of the regular Dropout in convolution layer. Each feature map in a convolution layer has a strong correlation between each other, particularly in every identical pixel location in each feature map. Crossmap Dropout tries to maintain this important correlation yet at the same time break the correlation between each adjacent pixel with respect to all feature maps by applying the same Dropout mask to all feature maps, so that all pixels or units in equivalent positions in each feature map will be either dropped or active during training. Our experiment with various benchmark datasets shows that our approaches provide better generalization than the regular Dropout. Moreover, our Biased Dropout takes faster time to converge during training phase, suggesting that assigning noise appropriately in hidden units can lead to an effective regularization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Communication cost of simulating Bell correlations.

PubMed

Toner, B F; Bacon, D

2003-10-31

What classical resources are required to simulate quantum correlations? For the simplest and most important case of local projective measurements on an entangled Bell pair state, we show that exact simulation is possible using local hidden variables augmented by just one bit of classical communication. Certain quantum teleportation experiments, which teleport a single qubit, therefore admit a local hidden variables model.

Radio for hidden-photon dark matter detection

DOE PAGES

Chaudhuri, Saptarshi; Graham, Peter W.; Irwin, Kent; ...

2015-10-08

We propose a resonant electromagnetic detector to search for hidden-photon dark matter over an extensive range of masses. Hidden-photon dark matter can be described as a weakly coupled “hidden electric field,” oscillating at a frequency fixed by the mass, and able to penetrate any shielding. At low frequencies (compared to the inverse size of the shielding), we find that the observable effect of the hidden photon inside any shielding is a real, oscillating magnetic field. We outline experimental setups designed to search for hidden-photon dark matter, using a tunable, resonant LC circuit designed to couple to this magnetic field. Ourmore » “straw man” setups take into consideration resonator design, readout architecture and noise estimates. At high frequencies, there is an upper limit to the useful size of a single resonator set by 1/ν. However, many resonators may be multiplexed within a hidden-photon coherence length to increase the sensitivity in this regime. Hidden-photon dark matter has an enormous range of possible frequencies, but current experiments search only over a few narrow pieces of that range. As a result, we find the potential sensitivity of our proposal is many orders of magnitude beyond current limits over an extensive range of frequencies, from 100 Hz up to 700 GHz and potentially higher.« less

Neural networks with local receptive fields and superlinear VC dimension.

PubMed

Schmitt, Michael

2002-04-01

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

A comparative analysis of support vector machines and extreme learning machines.

PubMed

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

Surface treatment using metal foil liner

NASA Technical Reports Server (NTRS)

Garvey, Ray

1989-01-01

A metal foil liner can be used to seal large area surfaces. Characteristics of the two-layer foil liner are discussed. Micrographs for foil-to-foil, foil-to-composite, visible seams, and hidden seams are examined.

Violation of Leggett-type inequalities in the spin-orbit degrees of freedom of a single photon

NASA Astrophysics Data System (ADS)

Cardano, Filippo; Karimi, Ebrahim; Marrucci, Lorenzo; de Lisio, Corrado; Santamato, Enrico

2013-09-01

We report the experimental violation of Leggett-type inequalities for a hybrid entangled state of spin and orbital angular momentum of a single photon. These inequalities give a physical criterion to verify the possible validity of a class of hidden-variable theories, originally named “crypto nonlocal,” that are not excluded by the violation of Bell-type inequalities. In our case, the tested theories assume the existence of hidden variables associated with independent degrees of freedom of the same particle, while admitting the possibility of an influence between the two measurements, i.e., the so-called contextuality of observables. We observe a violation of the Leggett inequalities for a range of experimental inputs, with a maximum violation of seven standard deviations, thus ruling out this class of hidden-variable models with a high level of confidence.

A possible loophole in the theorem of Bell.

PubMed

Hess, K; Philipp, W

2001-12-04

The celebrated inequalities of Bell are based on the assumption that local hidden parameters exist. When combined with conflicting experimental results, these inequalities appear to prove that local hidden parameters cannot exist. This contradiction suggests to many that only instantaneous action at a distance can explain the Einstein, Podolsky, and Rosen type of experiments. We show that, in addition to the assumption that hidden parameters exist, Bell tacitly makes a variety of other assumptions that contribute to his being able to obtain the desired contradiction. For instance, Bell assumes that the hidden parameters do not depend on time and are governed by a single probability measure independent of the analyzer settings. We argue that the exclusion of time has neither a physical nor a mathematical basis but is based on Bell's translation of the concept of Einstein locality into the language of probability theory. Our additional set of local hidden variables includes time-like correlated parameters and a generalized probability density. We prove that our extended space of local hidden variables does not permit Bell-type proofs to go forward.

High pressure air compressor valve fault diagnosis using feedforward neural networks

NASA Astrophysics Data System (ADS)

James Li, C.; Yu, Xueli

1995-09-01

Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor's automatic condition monitoring and fault diagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in fault diagnosis. The trained neural networks provide very accurate diagnosis for suction and discharge valve defects.

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

NASA Astrophysics Data System (ADS)

Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

2017-01-01

In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

Active semi-supervised learning method with hybrid deep belief networks.

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

[Application of an artificial neural network in the design of sustained-release dosage forms].

PubMed

Wei, X H; Wu, J J; Liang, W Q

2001-09-01

To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton

NASA Astrophysics Data System (ADS)

Silaban, Herlan; Zarlis, Muhammad; Sawaluddin

2017-12-01

Back-propagation is one of the learning algorithms on artificial neural networks that have been widely used to solve various problems, such as pattern recognition, prediction and classification. The Back-propagation architecture will affect the outcome of learning processed. BFGS Quasi-Newton is one of the functions that can be used to change the weight of back-propagation. This research tested some back-propagation architectures using classical back-propagation and back-propagation with BFGS. There are 7 architectures that have been tested on glass dataset with various numbers of neurons, 6 architectures with 1 hidden layer and 1 architecture with 2 hidden layers. BP with BFGS improves the convergence of the learning process. The average improvement convergence is 98.34%. BP with BFGS is more optimal on architectures with smaller number of neurons with decreased epoch number is 94.37% with the increase of accuracy about 0.5%.

Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks.

PubMed

Koprowski, Robert; Lanza, Michele; Irregolare, Carlo

2016-11-15

Efficacy and high availability of surgery techniques for refractive defect correction increase the number of patients who undergo to this type of surgery. Regardless of that, with increasing age, more and more patients must undergo cataract surgery. Accurate evaluation of corneal power is an extremely important element affecting the precision of intraocular lens (IOL) power calculation and errors in this procedure could affect quality of life of patients and satisfaction with the service provided. The available device able to measure corneal power have been tested to be not reliable after myopic refractive surgery. Artificial neural networks with error backpropagation and one hidden layer were proposed for corneal power prediction. The article analysed the features acquired from the Pentacam HR tomograph, which was necessary to measure the corneal power. Additionally, several billion iterations of artificial neural networks were conducted for several hundred simulations of different network configurations and different features derived from the Pentacam HR. The analysis was performed on a PC with Intel ® Xeon ® X5680 3.33 GHz CPU in Matlab ® Version 7.11.0.584 (R2010b) with Signal Processing Toolbox Version 7.1 (R2010b), Neural Network Toolbox 7.0 (R2010b) and Statistics Toolbox (R2010b). A total corneal power prediction error was obtained for 172 patients (113 patients forming the training set and 59 patients in the test set) with an average age of 32 ± 9.4 years, including 67% of men. The error was at an average level of 0.16 ± 0.14 diopters and its maximum value did not exceed 0.75 dioptres. The Pentacam parameters (measurement results) providing the above result are tangential anterial/posterior. The corneal net power and equivalent k-reading power. The analysis time for a single patient (a single eye) did not exceed 0.1 s, whereas the time of network training was about 3 s for 1000 iterations (the number of neurons in the hidden layer was 400).

Fragmentation of copper current collectors in Li-ion batteries during spherical indentation

NASA Astrophysics Data System (ADS)

Wang, Hsin; Watkins, Thomas R.; Simunovic, Srdjan; Bingham, Philip R.; Allu, Srikanth; Turner, John A.

2017-10-01

Large, areal, brittle fracture of copper current collector foils has been observed by 3D x-ray computed tomography (XCT) of a spherically indented Li-ion cell. This fracture is hidden and non-catastrophic to a degree because the graphite layers deform plastically, and hold the materials together so that the cracks in the foils cannot be seen under optical and electron microscopy. The cracking of copper foils could not be immediately confirmed when the cell is opened for post-mortem examination. However, 3D XCT on the indented cell reveals ;mud cracks; within the copper layer and an X-ray radiograph on a single foil of the Cu anode shows clearly that the copper foil has broken into multiple pieces. This failure mode of anodes in Li-ion cell has very important implications on the behavior of Li-ion cells under mechanical abuse conditions. The fragmentation of current collectors in the anode must be taken into consideration for the electrochemical responses which may lead to capacity loss and affect thermal runaway behavior of the cells.

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

PubMed

Winkler, David A; Le, Tu C

2017-01-01

Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Criteria for Choosing the Best Neural Network: Part 1

DTIC Science & Technology

1991-07-24

Touretzky, pp. 177-185. San Mateo: Morgan Kaufmann. Harp, S.A., Samad , T., and Guha, A . (1990). Designing application-specific neural networks using genetic...determining a parsimonious neural network for use in prediction/generalization based on a given fixed learning sample. Both the classification and...statistical settings, algorithms for selecting the number of hidden layer nodes in a three layer, feedforward neural network are presented. The selection

The PRISM (Pliocene Palaeoclimate) reconstruction: Time for a paradigm shift

USGS Publications Warehouse

Dowsett, Harry J.; Robinson, Marci M.; Stoll, Danielle K.; Foley, Kevin M.; Johnson, Andrew L. A.; Williams, Mark; Riesselman, Christina

2013-01-01

Global palaeoclimate reconstructions have been invaluable to our understanding of the causes and effects of climate change, but single-temperature representations of the oceanic mixed layer for data–model comparisons are outdated, and the time for a paradigm shift in marine palaeoclimate reconstruction is overdue. The new paradigm in marine palaeoclimate reconstruction stems the loss of valuable climate information and instead presents a holistic and nuanced interpretation of multi-dimensional oceanographic processes and responses. A wealth of environmental information is hidden within the US Geological Survey's Pliocene Research,Interpretation and Synoptic Mapping (PRISM) marine palaeoclimate reconstruction, and we introduce here a plan to incorporate all valuable climate data into the next generation of PRISM products. Beyond the global approach and focus, we plan to incorporate regional climate dynamics with emphasis on processes, integrating multiple environmental proxies wherever available in order to better characterize the mixed layer, and developing a finer time slice within the Mid-Piacenzian Age of the Pliocene, complemented by underused proxies that offer snapshots into environmental conditions. The result will be a proxy-rich, temporally nested, process-oriented approach in a digital format - a relational database with geographic information system capabilities comprising a three-dimensional grid representing the surface layer, with a plethora of data in each cell.

Inference for dynamics of continuous variables: the extended Plefka expansion with hidden nodes

NASA Astrophysics Data System (ADS)

Bravi, B.; Sollich, P.

2017-06-01

We consider the problem of a subnetwork of observed nodes embedded into a larger bulk of unknown (i.e. hidden) nodes, where the aim is to infer these hidden states given information about the subnetwork dynamics. The biochemical networks underlying many cellular and metabolic processes are important realizations of such a scenario as typically one is interested in reconstructing the time evolution of unobserved chemical concentrations starting from the experimentally more accessible ones. We present an application to this problem of a novel dynamical mean field approximation, the extended Plefka expansion, which is based on a path integral description of the stochastic dynamics. As a paradigmatic model we study the stochastic linear dynamics of continuous degrees of freedom interacting via random Gaussian couplings. The resulting joint distribution is known to be Gaussian and this allows us to fully characterize the posterior statistics of the hidden nodes. In particular the equal-time hidden-to-hidden variance—conditioned on observations—gives the expected error at each node when the hidden time courses are predicted based on the observations. We assess the accuracy of the extended Plefka expansion in predicting these single node variances as well as error correlations over time, focussing on the role of the system size and the number of observed nodes.

Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang

2018-04-01

Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

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

2015-12-01

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

Superfluid Densities in Superconducting/Ferromagnetic (Nb/NiV/Nb) Heterostructures

NASA Astrophysics Data System (ADS)

Hinton, Michael; Peters, Brian; Hauser, Adam; Meyer, Julia; Yang, Fengyuan; Lemberger, Thomas

2011-03-01

Superfluid density measurements allow us to probe the superconducting structure of thin films below Tc with remarkable detail. They yield information not only of the inherent robustness of the superconducting state, but also about the homogeneity of the sample and possible ``hidden'' transitions at temperatures lower than the initial Tc . For this reason multiple transitions in superconducting heterostructures are revealed to us. We use superfluid density measurements on Nb/ Ni 0.95 V0.05 /Nb trilayers to study the interplay between two superconducting films separated by the destructive proximity effects of a ferromagnet. We show there are trilayers with strong coupling, which produces a single transition, that become decoupled to the point of separation into two transitions as the ferromagnetic layer thickness increases. We discuss the difficulties in observing the second transition in σ1 , while obvious in λ-2 .

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy.

PubMed

Zhang, Yudong; Nayak, Deepak Ranjan; Yang, Ming; Yuan, Ti-Fei; Liu, Bin; Lu, Huimin; Wang, Shuihua

2017-01-01

Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier. The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively. Our developed system is promising and effective in detecting hearing loss. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Microwave spectroscopic observation of multiple phase transitions in the bilayer electron solid in wide quantum wells

NASA Astrophysics Data System (ADS)

Hatke, Anthony; Engel, Lloyd; Liu, Yang; Shayegan, Mansour; Pfeiffer, Loren; West, Ken; Baldwin, Kirk

2015-03-01

The termination of the low Landau filling factor (ν) fractional quantum Hall series for a single layer two dimensional system results in the formation of a pinned Wigner solid for ν < 1 / 5. In a wide quantum well the system can support a bilayer state in which interlayer and intralayer interactions become comparable, which is measured in traditional transport as an insulating state for ν < 1 / 2. We perform microwave spectroscopic studies of this bilayer state and observe that this insulator exhibits a resonance, a signature of a solid phase. Additionally, we find that as we increase the density of the well at fixed ν this bilayer solid exhibits multiple sharp reductions in the resonance amplitude vs ν. This behavior is characteristic of multiple phase transitions, which remain hidden from dc transport measurements.

Enhanced axion-photon coupling in GUT with hidden photon

NASA Astrophysics Data System (ADS)

Daido, Ryuji; Takahashi, Fuminobu; Yokozaki, Norimi

2018-05-01

We show that the axion coupling to photons can be enhanced in simple models with a single Peccei-Quinn field, if the gauge coupling unification is realized by a large kinetic mixing χ = O (0.1) between hypercharge and unbroken hidden U(1)H. The key observation is that the U(1)H gauge coupling should be rather strong to induce such large kinetic mixing, leading to enhanced contributions of hidden matter fields to the electromagnetic anomaly. We find that the axion-photon coupling is enhanced by about a factor of 10-100 with respect to the GUT-axion models with E / N = 8 / 3.

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

Bell's theorem and the problem of decidability between the views of Einstein and Bohr.

PubMed

Hess, K; Philipp, W

2001-12-04

Einstein, Podolsky, and Rosen (EPR) have designed a gedanken experiment that suggested a theory that was more complete than quantum mechanics. The EPR design was later realized in various forms, with experimental results close to the quantum mechanical prediction. The experimental results by themselves have no bearing on the EPR claim that quantum mechanics must be incomplete nor on the existence of hidden parameters. However, the well known inequalities of Bell are based on the assumption that local hidden parameters exist and, when combined with conflicting experimental results, do appear to prove that local hidden parameters cannot exist. This fact leaves only instantaneous actions at a distance (called "spooky" by Einstein) to explain the experiments. The Bell inequalities are based on a mathematical model of the EPR experiments. They have no experimental confirmation, because they contradict the results of all EPR experiments. In addition to the assumption that hidden parameters exist, Bell tacitly makes a variety of other assumptions; for instance, he assumes that the hidden parameters are governed by a single probability measure independent of the analyzer settings. We argue that the mathematical model of Bell excludes a large set of local hidden variables and a large variety of probability densities. Our set of local hidden variables includes time-like correlated parameters and a generalized probability density. We prove that our extended space of local hidden variables does permit derivation of the quantum result and is consistent with all known experiments.

A hybrid wavelet transform based short-term wind speed forecasting approach.

PubMed

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

PubMed Central

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy. PMID:25136699

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

PubMed

García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

2006-05-01

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Two fast and accurate heuristic RBF learning rules for data classification.

PubMed

Rouhani, Modjtaba; Javan, Dawood S

2016-03-01

This paper presents new Radial Basis Function (RBF) learning methods for classification problems. The proposed methods use some heuristics to determine the spreads, the centers and the number of hidden neurons of network in such a way that the higher efficiency is achieved by fewer numbers of neurons, while the learning algorithm remains fast and simple. To retain network size limited, neurons are added to network recursively until termination condition is met. Each neuron covers some of train data. The termination condition is to cover all training data or to reach the maximum number of neurons. In each step, the center and spread of the new neuron are selected based on maximization of its coverage. Maximization of coverage of the neurons leads to a network with fewer neurons and indeed lower VC dimension and better generalization property. Using power exponential distribution function as the activation function of hidden neurons, and in the light of new learning approaches, it is proved that all data became linearly separable in the space of hidden layer outputs which implies that there exist linear output layer weights with zero training error. The proposed methods are applied to some well-known datasets and the simulation results, compared with SVM and some other leading RBF learning methods, show their satisfactory and comparable performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks

NASA Astrophysics Data System (ADS)

Yasami, Yasser; Safaei, Farshad

2018-02-01

The traditional complex network theory is particularly focused on network models in which all network constituents are dealt with equivalently, while fail to consider the supplementary information related to the dynamic properties of the network interactions. This is a main constraint leading to incorrect descriptions of some real-world phenomena or incomplete capturing the details of certain real-life problems. To cope with the problem, this paper addresses the multilayer aspects of dynamic complex networks by analyzing the properties of intrinsically multilayered co-authorship networks, DBLP and Astro Physics, and presenting a novel multilayer model of dynamic complex networks. The model examines the layers evolution (layers birth/death process and lifetime) throughout the network evolution. Particularly, this paper models the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model considering feature cascade, and thereby formulates the link generation process for intra-layer and inter-layer links. Although adjacency matrixes are useful to describe the traditional single-layer networks, such a representation is not sufficient to describe and analyze the multilayer dynamic networks. This paper also extends a generalized mathematical infrastructure to address the problems issued by multilayer complex networks. The model inference is performed using some Markov Chain Monte Carlo sampling strategies, given synthetic and real complex networks data. Experimental results indicate a tremendous improvement in the performance of the proposed multilayer model in terms of sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, F1-score, Matthews correlation coefficient, and accuracy for two important applications of missing link prediction and future link forecasting. The experimental results also indicate the strong predictivepower of the proposed model for the application of cascade prediction in terms of accuracy.

Secrets of the Chinese magic mirror replica

NASA Astrophysics Data System (ADS)

Mak, Se-yuen; Yip, Din-yan

2001-03-01

We examine the structure of five Chinese magic mirror replicas using a special imaging technique developed by the authors. All mirrors are found to have a two-layered structure. The reflecting surface that gives rise to a projected magic pattern on the screen is hidden under a polished half-reflecting top layer. An alternative method of making the magic mirror using ancient technology has been proposed. Finally, we suggest a simple method of reconstructing a mirror replica in the laboratory.

Galactic Observations of Terahertz C+ (GOT C+): [CII] Detection of Warm "Dark Gas" in the ISM

NASA Astrophysics Data System (ADS)

Langer, W. D.; Velusamy, T.; Pineda, J.; Goldsmith, P.; Li, D.; Yorke, H. W.

2011-11-01

The Herschel HIFI Key Program, Galactic Observations of Terahertz C+ (GOT C+) is a survey of [CII] 1.9 THz emission throughout the Galaxy. Comparison of the first results of this survey with HI and CO isotopomer emission reveals excess [CII] emission beyond that expected from HI and CO layers alone, and is best explained as coming from a hidden layer of H2 gas, the so-called ISM "dark gas".

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biology and polymer physics at the single-molecule level.

PubMed

Chu, Steven

2003-04-15

The ability to look at individual molecules has given us new insights into molecular processes. Examples of our recent work are given to illustrate how behaviour that may otherwise be hidden from view can be clearly seen in single-molecule experiments.

Laser projection positioning of spatial contour curves via a galvanometric scanner

NASA Astrophysics Data System (ADS)

Tu, Junchao; Zhang, Liyan

2018-04-01

The technology of laser projection positioning is widely applied in advanced manufacturing fields (e.g. composite plying, parts location and installation). In order to use it better, a laser projection positioning (LPP) system is designed and implemented. Firstly, the LPP system is built by a laser galvanometric scanning (LGS) system and a binocular vision system. Applying Single-hidden Layer Feed-forward Neural Network (SLFN), the system model is constructed next. Secondly, the LGS system and the binocular system, which are respectively independent, are integrated through a datadriven calibration method based on extreme learning machine (ELM) algorithm. Finally, a projection positioning method is proposed within the framework of the calibrated SLFN system model. A well-designed experiment is conducted to verify the viability and effectiveness of the proposed system. In addition, the accuracy of projection positioning are evaluated to show that the LPP system can achieves the good localization effect.

Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

PubMed

Valdés, Julio J; Barton, Alan J

2007-05-01

A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

PubMed

Yang, Yimin; Wu, Q M Jonathan

2016-11-01

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks.

PubMed

Hovakimyan, N; Nardi, F; Calise, A; Kim, Nakwan

2002-01-01

We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated system may be unknown. However, the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. The classical approach requires a state observer. Finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it is sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two and a high-bandwidth attitude command system for a model R-50 helicopter.

Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series.

PubMed

Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J; Denève, Sophie; Gutkin, Boris

2017-01-01

Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new ( in vitro ) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the "Bayesian neuron" (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant.

Detecting structure of haplotypes and local ancestry

USDA-ARS?s Scientific Manuscript database

We present a two-layer hidden Markov model to detect the structure of haplotypes for unrelated individuals. This allows us to model two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), thereby taking advantage of rich haplotype information to infer local an...

Fragmentation of copper current collectors in Li-ion batteries during spherical indentation

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

Wang, Hsin; Watkins, Thomas R.; Simunovic, Srdjan

Large, areal, brittle fracture of copper current collector foils was observed by 3D x-ray computed tomography (XCT) of a spherically indented Li-ion cell. This fracture was hidden and non-catastrophic to a degree because the graphite layers deformed plastically, and held the materials together so that the cracks in the foils could not be seen under optical and electron microscopy. 3D XCT on the indented cell showed “mud cracks” within the copper layer. The cracking of copper foils could not be immediately confirmed when the cell was opened for post-mortem examination. However, an X-ray radiograph on a single foil of themore » Cu anode showed clearly that the copper foil had broken into multiple pieces similar to the brittle cracking of a ceramic under indentation. This new failure mode of anodes on Li-ion cell has very important implications on the behavior of Li-ion cells under mechanical abuse conditions. Furthermore, the fragmentation of current collectors in the anode must be taken into consideration for the electrochemical responses which may lead to capacity loss and affect thermal runaway behavior of the cells.« less

Fragmentation of copper current collectors in Li-ion batteries during spherical indentation

DOE PAGES

Wang, Hsin; Watkins, Thomas R.; Simunovic, Srdjan; ...

2017-08-29

Large, areal, brittle fracture of copper current collector foils was observed by 3D x-ray computed tomography (XCT) of a spherically indented Li-ion cell. This fracture was hidden and non-catastrophic to a degree because the graphite layers deformed plastically, and held the materials together so that the cracks in the foils could not be seen under optical and electron microscopy. 3D XCT on the indented cell showed “mud cracks” within the copper layer. The cracking of copper foils could not be immediately confirmed when the cell was opened for post-mortem examination. However, an X-ray radiograph on a single foil of themore » Cu anode showed clearly that the copper foil had broken into multiple pieces similar to the brittle cracking of a ceramic under indentation. This new failure mode of anodes on Li-ion cell has very important implications on the behavior of Li-ion cells under mechanical abuse conditions. Furthermore, the fragmentation of current collectors in the anode must be taken into consideration for the electrochemical responses which may lead to capacity loss and affect thermal runaway behavior of the cells.« less

Technical note: an R package for fitting sparse neural networks with application in animal breeding.

PubMed

Wang, Yangfan; Mi, Xue; Rosa, Guilherme J M; Chen, Zhihui; Lin, Ping; Wang, Shi; Bao, Zhenmin

2018-05-04

Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.

Modeling constitutive behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot compression using artificial neural network

NASA Astrophysics Data System (ADS)

Mandal, Sumantra

2006-11-01

ABSTRACT In this paper, an artificial neural network (ANN) model has been suggested to predict the constitutive flow behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot deformation. Hot compression tests in the temperature range 850°C- 1250°C and strain rate range 10-3-102 s-1 were carried out. These tests provided the required data for training the neural network and for subsequent testing. The inputs of the neural network are strain, log strain rate and temperature while flow stress is obtained as output. A three layer feed-forward network with ten neurons in a single hidden layer and back-propagation learning algorithm has been employed. A very good correlation between experimental and predicted result has been obtained. The effect of temperature and strain rate on flow behavior has been simulated employing the ANN model. The results have been found to be consistent with the metallurgical trend. Finally, a monte carlo analiysis has been carried out to find out the noise sensitivity of the developed model.

Symmorphic Intersecting Nodal Rings in Semiconducting Layers

NASA Astrophysics Data System (ADS)

Gong, Cheng; Xie, Yuee; Chen, Yuanping; Kim, Heung-Sik; Vanderbilt, David

2018-03-01

The unique properties of topological semimetals have strongly driven efforts to seek for new topological phases and related materials. Here, we identify a critical condition for the existence of intersecting nodal rings (INRs) in symmorphic crystals, and further classify all possible kinds of INRs which can be obtained in the layered semiconductors with Amm2 and Cmmm space group symmetries. Several honeycomb structures are suggested to be topological INR semimetals, including layered and "hidden" layered structures. Transitions between the three types of INRs, named as α , β , and γ type, can be driven by external strains in these structures. The resulting surface states and Landau-level structures, more complicated than those resulting from a simple nodal loop, are also discussed.

Hidden-service Statistics Reported by Relays

DTIC Science & Technology

2015-06-01

received, then 7 an adversary that knows the . onion address of a hidden service (and thus can obtain its Introduction Points) could infer how many...hide any single or repeated 9 publication of any given group of at most 8 onion services (e.g. a set of 8 or fewer related onion addresses that are... onion router. In Proceedings of the 13th USENIX Security Symposium, 2004. [5] Cynthia Dwork. Differential privacy. In in ICALP, 2006. [6] Cynthia Dwork

Emergence of higher order rotational symmetry in the hidden order phase of URu 2Si 2

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

Kanchanavatee, N.; Janoschek, M.; Huang, K.

2016-09-30

Electrical resistivity measurements were performed in this paper as functions of temperature, magnetic field, and angle θ between the magnetic field and the c-axis of a URu 2Si 2 single crystal. The resistivity exhibits a two-fold oscillation as a function of θ at high temperatures, which undergoes a 180°-phase shift (sign change) with decreasing temperature at around 35 K. The hidden order transition is manifested as a minimum in the magnetoresistance and amplitude of the two-fold oscillation. Interestingly, the resistivity also showed four-fold, six-fold, and eight-fold symmetries at the hidden order transition. These higher order symmetries were also detected atmore » low temperatures, which could be a sign of the formation of another pseudogap phase above the superconducting transition, consistent with recent evidence for a pseudogap from point-contact spectroscopy measurements and NMR. Measurements of the magnetisation of single crystalline URu 2Si 2 with the magnetic field applied parallel and perpendicular to the crystallographic c-axis revealed regions with linear temperature dependencies between the hidden order transition temperature and about 25 K. Finally, this T-linear behaviour of the magnetisation may be associated with the formation of a precursor phase or ‘pseudogap’ in the density of states in the vicinity of 30–35 K.« less

Single photon and nonlocality

NASA Astrophysics Data System (ADS)

Drezet, Aurelien

2007-03-01

In a paper by Home and Agarwal [1], it is claimed that quantum nonlocality can be revealed in a simple interferometry experiment using only single particles. A critical analysis of the concept of hidden variable used by the authors of [1] shows that the reasoning is not correct.

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

PubMed

Buettner, Florian; Natarajan, Kedar N; Casale, F Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J; Teichmann, Sarah A; Marioni, John C; Stegle, Oliver

2015-02-01

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

23 CFR Appendix A to Part 1313 - Tamper Resistant Driver's License

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Block graphics. (15) Security fonts and graphics with known hidden flaws. (16) Card stock, layer with colors. (17) Micro-graphics. (18) Retroflective security logos. (19) Machine readable technologies such... permit that has one or more of the following security features: (1) Ghost image. (2) Ghost graphic. (3...

"Glitch Logic" and Applications to Computing and Information Security

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Katkoori, Srinivas

2009-01-01

This paper introduces a new method of information processing in digital systems, and discusses its potential benefits to computing and information security. The new method exploits glitches caused by delays in logic circuits for carrying and processing information. Glitch processing is hidden to conventional logic analyses and undetectable by traditional reverse engineering techniques. It enables the creation of new logic design methods that allow for an additional controllable "glitch logic" processing layer embedded into a conventional synchronous digital circuits as a hidden/covert information flow channel. The combination of synchronous logic with specific glitch logic design acting as an additional computing channel reduces the number of equivalent logic designs resulting from synthesis, thus implicitly reducing the possibility of modification and/or tampering with the design. The hidden information channel produced by the glitch logic can be used: 1) for covert computing/communication, 2) to prevent reverse engineering, tampering, and alteration of design, and 3) to act as a channel for information infiltration/exfiltration and propagation of viruses/spyware/Trojan horses.

Uncovering hidden heterogeneity: Geo-statistical models illuminate the fine scale effects of boating infrastructure on sediment characteristics and contaminants.

PubMed

Hedge, L H; Dafforn, K A; Simpson, S L; Johnston, E L

2017-06-30

Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Analysis of single ion channel data incorporating time-interval omission and sampling

PubMed Central

The, Yu-Kai; Timmer, Jens

2005-01-01

Hidden Markov models are widely used to describe single channel currents from patch-clamp experiments. The inevitable anti-aliasing filter limits the time resolution of the measurements and therefore the standard hidden Markov model is not adequate anymore. The notion of time-interval omission has been introduced where brief events are not detected. The developed, exact solutions to this problem do not take into account that the measured intervals are limited by the sampling time. In this case the dead-time that specifies the minimal detectable interval length is not defined unambiguously. We show that a wrong choice of the dead-time leads to considerably biased estimates and present the appropriate equations to describe sampled data. PMID:16849220

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach.

PubMed

Albadr, Musatafa Abbas Abbood; Tiun, Sabrina; Al-Dhief, Fahad Taha; Sammour, Mahmoud A M

2018-01-01

Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

PubMed Central

Tiun, Sabrina; AL-Dhief, Fahad Taha; Sammour, Mahmoud A. M.

2018-01-01

Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%. PMID:29672546

A theory of cerebellar cortex and adaptive motor control based on two types of universal function approximation capability.

PubMed

Fujita, Masahiko

2016-03-01

Lesions of the cerebellum result in large errors in movements. The cerebellum adaptively controls the strength and timing of motor command signals depending on the internal and external environments of movements. The present theory describes how the cerebellar cortex can control signals for accurate and timed movements. A model network of the cerebellar Golgi and granule cells is shown to be equivalent to a multiple-input (from mossy fibers) hierarchical neural network with a single hidden layer of threshold units (granule cells) that receive a common recurrent inhibition (from a Golgi cell). The weighted sum of the hidden unit signals (Purkinje cell output) is theoretically analyzed regarding the capability of the network to perform two types of universal function approximation. The hidden units begin firing as the excitatory inputs exceed the recurrent inhibition. This simple threshold feature leads to the first approximation theory, and the network final output can be any continuous function of the multiple inputs. When the input is constant, this output becomes stationary. However, when the recurrent unit activity is triggered to decrease or the recurrent inhibition is triggered to increase through a certain mechanism (metabotropic modulation or extrasynaptic spillover), the network can generate any continuous signals for a prolonged period of change in the activity of recurrent signals, as the second approximation theory shows. By incorporating the cerebellar capability of two such types of approximations to a motor system, in which learning proceeds through repeated movement trials with accompanying corrections, accurate and timed responses for reaching the target can be adaptively acquired. Simple models of motor control can solve the motor error vs. sensory error problem, as well as the structural aspects of credit (or error) assignment problem. Two physiological experiments are proposed for examining the delay and trace conditioning of eyelid responses, as well as saccade adaptation, to investigate this novel idea of cerebellar processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

PubMed

Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua

2016-05-01

Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

PubMed

Jiang, Yizhang; Chung, Fu-Lai; Ishibuchi, Hisao; Deng, Zhaohong; Wang, Shitong

2015-03-01

The classical fuzzy system modeling methods implicitly assume data generated from a single task, which is essentially not in accordance with many practical scenarios where data can be acquired from the perspective of multiple tasks. Although one can build an individual fuzzy system model for each task, the result indeed tells us that the individual modeling approach will get poor generalization ability due to ignoring the intertask hidden correlation. In order to circumvent this shortcoming, we consider a general framework for preserving the independent information among different tasks and mining hidden correlation information among all tasks in multitask fuzzy modeling. In this framework, a low-dimensional subspace (structure) is assumed to be shared among all tasks and hence be the hidden correlation information among all tasks. Under this framework, a multitask Takagi-Sugeno-Kang (TSK) fuzzy system model called MTCS-TSK-FS (TSK-FS for multiple tasks with common hidden structure), based on the classical L2-norm TSK fuzzy system, is proposed in this paper. The proposed model can not only take advantage of independent sample information from the original space for each task, but also effectively use the intertask common hidden structure among multiple tasks to enhance the generalization performance of the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multitask fuzzy system model in multitask regression learning scenarios.

Multilayer neural networks for reduced-rank approximation.

PubMed

Diamantaras, K I; Kung, S Y

1994-01-01

This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case.

Oaks belowground: mycorrhizas, truffles, and small mammals

Treesearch

Jonathan Frank; Seth Barry; Joseph Madden; Darlene Southworth

2008-01-01

Oaks depend on hidden diversity belowground. Oregon white oaks (Quercus garryana) form ectomycorrhizas with more than 40 species of fungi at a 25-ha site. Several of the most common oak mycorrhizal fungi form hypogeous fruiting bodies or truffles in the upper layer of mineral soil. We collected 18 species of truffles associated with Oregon white...

Feature to prototype transition in neural networks

NASA Astrophysics Data System (ADS)

Krotov, Dmitry; Hopfield, John

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

Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform

NASA Astrophysics Data System (ADS)

Schmid, Volker R.; Bader, Gerhard; Lueder, Ernst H.

1998-02-01

We present a hybrid shape recognition system with an optical Hough transform processor. The features of the Hough space offer a separate cancellation of distortions caused by translations and rotations. Scale invariance is also provided by suitable normalization. The proposed system extends the capabilities of Hough transform based detection from only straight lines to areas bounded by edges. A very compact optical design is achieved by a microlens array processor accepting incoherent light as direct optical input and realizing the computationally expensive connections massively parallel. Our newly developed algorithm extracts rotation and translation invariant normalized patterns of bright spots on a 2D grid. A neural network classifier maps the 2D features via a nonlinear hidden layer onto the classification output vector. We propose initialization of the connection weights according to regions of activity specifically assigned to each neuron in the hidden layer using a competitive network. The presented system is designed for industry inspection applications. Presently we have demonstrated detection of six different machined parts in real-time. Our method yields very promising detection results of more than 96% correctly classified parts.

Short-term prediction of chaotic time series by using RBF network with regression weights.

PubMed

Rojas, I; Gonzalez, J; Cañas, A; Diaz, A F; Rojas, F J; Rodriguez, M

2000-10-01

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

PubMed

Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei

2017-07-01

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

Controlling the metal-to-insulator relaxation of the metastable hidden quantum state in 1T-TaS2

PubMed Central

Vaskivskyi, Igor; Gospodaric, Jan; Brazovskii, Serguei; Svetin, Damjan; Sutar, Petra; Goreshnik, Evgeny; Mihailovic, Ian A.; Mertelj, Tomaz; Mihailovic, Dragan

2015-01-01

Controllable switching between metastable macroscopic quantum states under nonequilibrium conditions induced either by light or with an external electric field is rapidly becoming of great fundamental interest. We investigate the relaxation properties of a “hidden” (H) charge density wave (CDW) state in thin single crystals of the layered dichalcogenide 1T-TaS2, which can be reached by either a single 35-fs optical laser pulse or an ~30-ps electrical pulse. From measurements of the temperature dependence of the resistivity under different excitation conditions, we find that the metallic H state relaxes to the insulating Mott ground state through a sequence of intermediate metastable states via discrete jumps over a “Devil’s staircase.” In between the discrete steps, an underlying glassy relaxation process is observed, which arises because of reciprocal-space commensurability frustration between the CDW and the underlying lattice. We show that the metastable state relaxation rate may be externally stabilized by substrate strain, thus opening the way to the design of nonvolatile ultrafast high-temperature memory devices based on switching between CDW states with large intrinsic differences in electrical resistance. PMID:26601218

Dementia of frontal lobe type and motor neuron disease. A Golgi study of the frontal cortex.

PubMed Central

Ferrer, I; Roig, C; Espino, A; Peiro, G; Matias Guiu, X

1991-01-01

Neuropathological findings in a 38 year old patient with dementia of frontal lobe type and motor neuron disease included pyramidal tracts, myelin pallor and neuron loss, gliosis and chromatolysis in the hypoglossal nucleus, together with frontal atrophy, neuron loss, gliosis and spongiosis in the upper cortical layers of the frontal (and temporal) lobes. Most remaining pyramidal and non-pyramidal neurons (multipolar, bitufted and bipolar cells) in the upper layers (layers II and III) of the frontal cortex (area B) had reduced dendritic arbors, proximal dendritic varicosities and amputation of dendrites as revealed in optimally stained rapid Golgi sections. Pyramidal cells in these layers also showed depletion of dendritic spines. Neurons in the inner layers were preserved. Loss of receptive surfaces in neurons of the upper cortical layers in the frontal cortex are indicative of neuronal disconnection, and are "hidden" contributory morphological substrates for the development of dementia. Images PMID:1744652

Forecasting the prognosis of choroidal melanoma with an artificial neural network.

PubMed

Kaiserman, Igor; Rosner, Mordechai; Pe'er, Jacob

2005-09-01

To develop an artificial neural network (ANN) that will forecast the 5-year mortality from choroidal melanoma. Retrospective, comparative, observational cohort study. One hundred fifty-three eyes of 153 consecutive patients with choroidal melanoma (age, 58.4+/-14.6 years) who were treated with ruthenium 106 brachytherapy between 1988 and 1998 at the Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel. Patients were observed clinically and ultrasonographically (A- and B-mode standardized ultrasonography). Metastatic screening included liver function tests and liver imaging. Backpropagation ANNs composed of 3 or 4 layers of neurons with various types of transfer functions and training protocols were assessed for their ability to predict the 5-year mortality. The ANNs were trained on 77 randomly selected patients and tested on a different set of 76 patients. Artificial neural networks were compared based on their sensitivity, specificity, forecasting accuracy, area under the receiver operating curves, and likelihood ratios (LRs). The best ANN was compared with the results of logistic regression and the performance of an ocular oncologist. The ability of the ANNs to forecast the 5-year mortality from choroidal melanoma. Thirty-one patients died during the follow-up period of metastatic choroidal melanoma. The best ANN (one hidden layer of 16 neurons) had 84% forecasting accuracy and an LR of 31.5. The number of hidden neurons significantly influenced the ANNs' performance (P<0.001). The performance of the ANNs was not significantly influenced by the training protocol, the number of hidden layers, or the type of transfer function. In comparison, logistic regression reached 86% forecasting accuracy, with a very low LR (0.8), whereas the human expert forecasting ability was <70% (LR, 1.85). Artificial neural networks can be used for forecasting the prognosis of choroidal melanoma and may support decision-making in treating this malignancy.

A simple approach for the sonochemical loading of Au, Ag and Pd nanoparticle on functionalized MWCNT and subsequent dispersion studies for removal of organic dyes: Artificial neural network and response surface methodology studies.

PubMed

Moghaddari, Mitra; Yousefi, Fakhri; Ghaedi, Mehrorang; Dashtian, Kheibar

2018-04-01

In this study, the artificial neural network (ANN) and response surface methodology (RSM) based on central composite design (CCD) were applied for modeling and optimization of the simultaneous ultrasound-assisted removal of quinoline yellow (QY) and eosin B (EB). The MWCNT-NH 2 and its composites were prepared by sonochemistry method and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis's. Initial dyes concentrations, adsorbent mass, sonication time and pH contribution on QY and EB removal percentage were investigated by CCD and replication of experiments at conditions suggested by model has results which statistically are close to experimented data. The ultrasound irradiation is associated with raising mass transfer of process so that small amount of the adsorbent (0.025 g) is able to remove high percentage (88.00% and 91.00%) of QY and EB, respectively in short time (6.0 min) at pH = 6. Analysis of experimental data by conventional models is good indication of Langmuir efficiency for fitting and explanation of experimented data. The ANN based on the Levenberg-Marquardt algorithm (LMA) combined of linear transfer function at output layer and tangent sigmoid transfer function at hidden layer with 20 hidden neurons supply best operation conditions for good prediction of adsorption data. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer, while data was divided into training, test and validation sets which contained 70, 15 and 15% of data points respectively. The Average absolute deviation (AAD)% of a collection of 128 data points for MWCNT-NH 2 and composites is 0.58%.for EB and 0.55 for YQ. Copyright © 2017 Elsevier B.V. All rights reserved.

Probing molecular choreography through single-molecule biochemistry.

PubMed

van Oijen, Antoine M; Dixon, Nicholas E

2015-12-01

Single-molecule approaches are having a dramatic impact on views of how proteins work. The ability to observe molecular properties at the single-molecule level allows characterization of subpopulations and acquisition of detailed kinetic information that would otherwise be hidden in the averaging over an ensemble of molecules. In this Perspective, we discuss how such approaches have successfully been applied to in vitro-reconstituted systems of increasing complexity.

Singularities of Three-Layered Complex-Valued Neural Networks With Split Activation Function.

PubMed

Kobayashi, Masaki

2018-05-01

There are three important concepts related to learning processes in neural networks: reducibility, nonminimality, and singularity. Although the definitions of these three concepts differ, they are equivalent in real-valued neural networks. This is also true of complex-valued neural networks (CVNNs) with hidden neurons not employing biases. The situation of CVNNs with hidden neurons employing biases, however, is very complicated. Exceptional reducibility was found, and it was shown that reducibility and nonminimality are not the same. Irreducibility consists of minimality and exceptional reducibility. The relationship between minimality and singularity has not yet been established. In this paper, we describe our surprising finding that minimality and singularity are independent. We also provide several examples based on exceptional reducibility.

Using Optical Coherence Tomography to Reveal the Hidden History of The Landsdowne Virgin of the Yarnwinder by Leonardo da Vinci and Studio.

PubMed

Targowski, Piotr; Iwanicka, Magdalena; Sylwestrzak, Marcin; Frosinini, Cecilia; Striova, Jana; Fontana, Raffaella

2018-06-18

Optical coherence tomography (OCT) was used for non-invasive examination of a well-known, yet complex, painting from the studio of Leonardo da Vinci in combination with routine imaging in various bands of electromagnetic radiation. In contrast with these techniques, OCT provides depth-resolved information. Three post-processing modalities were explored: cross-sectional views, maps of scattering from given depths, and their 3D models. Some hidden alterations of the painting owing to past restorations were traced: retouching and overpainting with their positioning within varnish layers as well as indications of a former transfer to canvas. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Markov Chain Monte Carlo in the Analysis of Single-Molecule Experimental Data

NASA Astrophysics Data System (ADS)

Kou, S. C.; Xie, X. Sunney; Liu, Jun S.

2003-11-01

This article provides a Bayesian analysis of the single-molecule fluorescence lifetime experiment designed to probe the conformational dynamics of a single DNA hairpin molecule. The DNA hairpin's conformational change is initially modeled as a two-state Markov chain, which is not observable and has to be indirectly inferred. The Brownian diffusion of the single molecule, in addition to the hidden Markov structure, further complicates the matter. We show that the analytical form of the likelihood function can be obtained in the simplest case and a Metropolis-Hastings algorithm can be designed to sample from the posterior distribution of the parameters of interest and to compute desired estiamtes. To cope with the molecular diffusion process and the potentially oscillating energy barrier between the two states of the DNA hairpin, we introduce a data augmentation technique to handle both the Brownian diffusion and the hidden Ornstein-Uhlenbeck process associated with the fluctuating energy barrier, and design a more sophisticated Metropolis-type algorithm. Our method not only increases the estimating resolution by several folds but also proves to be successful for model discrimination.

Density of Mars' south polar layered deposits.

PubMed

Zuber, Maria T; Phillips, Roger J; Andrews-Hanna, Jeffrey C; Asmar, Sami W; Konopliv, Alexander S; Lemoine, Frank G; Plaut, Jeffrey J; Smith, David E; Smrekar, Suzanne E

2007-09-21

Both poles of Mars are hidden beneath caps of layered ice. We calculated the density of the south polar layered deposits by combining the gravity field obtained from initial results of radio tracking of the Mars Reconnaissance Orbiter with existing surface topography from the Mars Orbiter Laser Altimeter on the Mars Global Surveyor spacecraft and basal topography from the Mars Advanced Radar for Subsurface and Ionospheric Sounding on the Mars Express spacecraft. The results indicate a best-fit density of 1220 kilograms per cubic meter, which is consistent with water ice that has approximately 15% admixed dust. The results demonstrate that the deposits are probably composed of relatively clean water ice and also refine the martian surface-water inventory.

A potential application in quantum networks—Deterministic quantum operation sharing schemes with Bell states

NASA Astrophysics Data System (ADS)

Zhang, KeJia; Zhang, Long; Song, TingTing; Yang, YingHui

2016-06-01

In this paper, we propose certain different design ideas on a novel topic in quantum cryptography — quantum operation sharing (QOS). Following these unique ideas, three QOS schemes, the "HIEC" (The scheme whose messages are hidden in the entanglement correlation), "HIAO" (The scheme whose messages are hidden with the assistant operations) and "HIMB" (The scheme whose messages are hidden in the selected measurement basis), have been presented to share the single-qubit operations determinately on target states in a remote node. These schemes only require Bell states as quantum resources. Therefore, they can be directly applied in quantum networks, since Bell states are considered the basic quantum channels in quantum networks. Furthermore, after analyse on the security and resource consumptions, the task of QOS can be achieved securely and effectively in these schemes.

Learning algorithm in restricted Boltzmann machines using Kullback-Leibler importance estimation procedure

NASA Astrophysics Data System (ADS)

Yasuda, Muneki; Sakurai, Tetsuharu; Tanaka, Kazuyuki

Restricted Boltzmann machines (RBMs) are bipartite structured statistical neural networks and consist of two layers. One of them is a layer of visible units and the other one is a layer of hidden units. In each layer, any units do not connect to each other. RBMs have high flexibility and rich structure and have been expected to applied to various applications, for example, image and pattern recognitions, face detections and so on. However, most of computational models in RBMs are intractable and often belong to the class of NP-hard problem. In this paper, in order to construct a practical learning algorithm for them, we employ the Kullback-Leibler Importance Estimation Procedure (KLIEP) to RBMs, and give a new scheme of practical approximate learning algorithm for RBMs based on the KLIEP.

Diagnosis and Management of Hidden Caries in a Primary Molar Tooth.

PubMed

Gera, Arwa; Zilberman, Uri

2017-01-01

Hidden caries is a dentinal lesion beneath the dentinoenamel junction, visible on radiographs. A single report described this lesion in primary dentition. This case report describes a case of hidden caries in a mandibular second primary molar, misdiagnosed as malignant swelling. A 3-year-old white girl was referred to the Department of Pediatric Dentistry with a chief complaint of pain and extraoral swelling on the right side of the mandible for the last 3 months. She was earlier referred to the surgical department for biopsy of the lesion. Radiographic and computed tomography scan examination showed a periapical lesion with buccal plate resorption and radiolucency beneath the enamel on the mesial part of tooth 85. The tooth was extracted, and follow-up of 2 years showed normal development of tooth 45. The main problem is early detection and treatment, since the outer surface of enamel may appear intact on tactile examination. Gera A, Zilberman U. Diagnosis and Management of Hidden Caries in a Primary Molar Tooth. Int J Clin Pediatr Dent 2017;10(1):99-102.

Hidden and antiferromagnetic order as a rank-5 superspin in URu2Si2

NASA Astrophysics Data System (ADS)

Rau, Jeffrey G.; Kee, Hae-Young

2012-06-01

We propose a candidate for the hidden order in URu2Si2: a rank-5 E type spin-density wave between uranium 5f crystal-field doublets Γ7(1) and Γ7(2), breaking time-reversal and lattice tetragonal symmetry in a manner consistent with recent torque measurements [Okazaki , ScienceSCIEAS0036-807510.1126/science.1197358 331, 439 (2011)]. We argue that coupling of this order parameter to magnetic probes can be hidden by crystal-field effects, while still having significant effects on transport, thermodynamics, and magnetic susceptibilities. In a simple tight-binding model for the heavy quasiparticles, we show the connection between the hidden order and antiferromagnetic phases arises since they form different components of this single rank-5 pseudospin vector. Using a phenomenological theory, we show that the experimental pressure-temperature phase diagram can be qualitatively reproduced by tuning terms which break pseudospin rotational symmetry. As a test of our proposal, we predict the presence of small magnetic moments in the basal plane oriented in the [110] direction ordered at the wave vector (0,0,1).

Modeling MOOC Student Behavior with Two-Layer Hidden Markov Models

ERIC Educational Resources Information Center

Geigle, Chase; Zhai, ChengXiang

2017-01-01

Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. To address…

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.

Space coding for sensorimotor transformations can emerge through unsupervised learning.

PubMed

De Filippo De Grazia, Michele; Cutini, Simone; Lisi, Matteo; Zorzi, Marco

2012-08-01

The posterior parietal cortex (PPC) is fundamental for sensorimotor transformations because it combines multiple sensory inputs and posture signals into different spatial reference frames that drive motor programming. Here, we present a computational model mimicking the sensorimotor transformations occurring in the PPC. A recurrent neural network with one layer of hidden neurons (restricted Boltzmann machine) learned a stochastic generative model of the sensory data without supervision. After the unsupervised learning phase, the activity of the hidden neurons was used to compute a motor program (a population code on a bidimensional map) through a simple linear projection and delta rule learning. The average motor error, calculated as the difference between the expected and the computed output, was less than 3°. Importantly, analyses of the hidden neurons revealed gain-modulated visual receptive fields, thereby showing that space coding for sensorimotor transformations similar to that observed in the PPC can emerge through unsupervised learning. These results suggest that gain modulation is an efficient coding strategy to integrate visual and postural information toward the generation of motor commands.

Learning to classify in large committee machines

NASA Astrophysics Data System (ADS)

O'kane, Dominic; Winther, Ole

1994-10-01

The ability of a two-layer neural network to learn a specific non-linearly-separable classification task, the proximity problem, is investigated using a statistical mechanics approach. Both the tree and fully connected architectures are investigated in the limit where the number K of hidden units is large, but still much smaller than the number N of inputs. Both have continuous weights. Within the replica symmetric ansatz, we find that for zero temperature training, the tree architecture exhibits a strong overtraining effect. For nonzero temperature the asymptotic error is lowered, but it is still higher than the corresponding value for the simple perceptron. The fully connected architecture is considered for two regimes. First, for a finite number of examples we find a symmetry among the hidden units as each performs equally well. The asymptotic generalization error is finite, and minimal for T-->∞ where it goes to the same value as for the simple perceptron. For a large number of examples we find a continuous transition to a phase with broken hidden-unit symmetry, which has an asymptotic generalization error equal to zero.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

PubMed

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

NASA Astrophysics Data System (ADS)

Samanta, B.; Al-Balushi, K. R.

2003-03-01

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

Autonomous self-configuration of artificial neural networks for data classification or system control

NASA Astrophysics Data System (ADS)

Fink, Wolfgang

2009-05-01

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

Kochen-Specker theorem studied with neutron interferometer.

PubMed

Hasegawa, Yuji; Durstberger-Rennhofer, Katharina; Sponar, Stephan; Rauch, Helmut

2011-04-01

The Kochen-Specker theorem shows the incompatibility of noncontextual hidden variable theories with quantum mechanics. Quantum contextuality is a more general concept than quantum non-locality which is quite well tested in experiments using Bell inequalities. Within neutron interferometry we performed an experimental test of the Kochen-Specker theorem with an inequality, which identifies quantum contextuality, by using spin-path entanglement of single neutrons. Here entanglement is achieved not between different particles, but between degrees of freedom of a single neutron, i.e., between spin and path degree of freedom. Appropriate combinations of the spin analysis and the position of the phase shifter allow an experimental verification of the violation of an inequality derived from the Kochen-Specker theorem. The observed violation 2.291±0.008≰1 clearly shows that quantum mechanical predictions cannot be reproduced by noncontextual hidden variable theories.

A cascaded neuro-computational model for spoken word recognition

NASA Astrophysics Data System (ADS)

Hoya, Tetsuya; van Leeuwen, Cees

2010-03-01

In human speech recognition, words are analysed at both pre-lexical (i.e., sub-word) and lexical (word) levels. The aim of this paper is to propose a constructive neuro-computational model that incorporates both these levels as cascaded layers of pre-lexical and lexical units. The layered structure enables the system to handle the variability of real speech input. Within the model, receptive fields of the pre-lexical layer consist of radial basis functions; the lexical layer is composed of units that perform pattern matching between their internal template and a series of labels, corresponding to the winning receptive fields in the pre-lexical layer. The model adapts through self-tuning of all units, in combination with the formation of a connectivity structure through unsupervised (first layer) and supervised (higher layers) network growth. Simulation studies show that the model can achieve a level of performance in spoken word recognition similar to that of a benchmark approach using hidden Markov models, while enabling parallel access to word candidates in lexical decision making.

An artificial neural network model for periodic trajectory generation

NASA Astrophysics Data System (ADS)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Imprinted and X-linked non-coding RNAs as potential regulators of human placental function

PubMed Central

Buckberry, Sam; Bianco-Miotto, Tina; Roberts, Claire T

2014-01-01

Pregnancy outcome is inextricably linked to placental development, which is strictly controlled temporally and spatially through mechanisms that are only partially understood. However, increasing evidence suggests non-coding RNAs (ncRNAs) direct and regulate a considerable number of biological processes and therefore may constitute a previously hidden layer of regulatory information in the placenta. Many ncRNAs, including both microRNAs and long non-coding transcripts, show almost exclusive or predominant expression in the placenta compared with other somatic tissues and display altered expression patterns in placentas from complicated pregnancies. In this review, we explore the results of recent genome-scale and single gene expression studies using human placental tissue, but include studies in the mouse where human data are lacking. Our review focuses on the ncRNAs epigenetically regulated through genomic imprinting or X-chromosome inactivation and includes recent evidence surrounding the H19 lincRNA, the imprinted C19MC cluster microRNAs, and X-linked miRNAs associated with pregnancy complications. PMID:24081302

Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis

NASA Astrophysics Data System (ADS)

Zhang, Yu-Dong; Zhang, Yin; Phillips, Preetha; Dong, Zhengchao; Wang, Shuihua

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski-Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy.

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations.

PubMed

Horta, Antônio Carlos Luperni; da Silva, Adilson José; Sargo, Cíntia Regina; Gonçalves, Viviane Maimoni; Zangirolami, Teresa Cristina; Giordano, Roberto de Campos

2011-09-01

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.

Superconductivity in metal coated graphene

NASA Astrophysics Data System (ADS)

Uchoa, Bruno; Castro Neto, Antonio

2007-03-01

Graphene, a single atomic layer of graphite, is a two dimensional (2D) zero gap insulator with a high electronic mobility between nearest neighbor carbon sites. The unique electronic properties of graphene, from the semi-metallic behavior to the observation of an anomalous quantum Hall effect and a zero field quantized minimum of conductivity derive from the relativistic nature of its quasiparticles. By doping graphene, it behaves in several aspects as a conventional Fermi liquid, where electrons may form Cooper pairs by coupling with a bosonic mode. In this talk, we develop a mean-field phenomenology of superconductivity in a honeycomb lattice. We predict the possibility of two distinct phases, a singlet s-wave phase and a novel p+ip wave phase in the singlet channel. At half filling, the p+ip phase is gapless and superconductivity is a hidden order. We propose a few possible sources of Cooper pairing instability in graphene coated with alkaline and transition metals, and similar low dimensional graphene based devices.

Neural field model of memory-guided search.

PubMed

Kilpatrick, Zachary P; Poll, Daniel B

2017-12-01

Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing redundancies in the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.

Neural field model of memory-guided search

NASA Astrophysics Data System (ADS)

Kilpatrick, Zachary P.; Poll, Daniel B.

2017-12-01

Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing redundancies in the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.

Asteroseismology of Red-Giant Stars: Mixed Modes, Differential Rotation, and Eccentric Binaries

NASA Astrophysics Data System (ADS)

Beck, Paul G.

2013-12-01

Astronomers are aware of rotation in stars since Galileo Galilei attributed the movement of sunspots to rotation of the Sun in 1613. In contrast to the Sun, whose surface can be resolved by small telescopes or even the (protected) eye, we detect stars as point sources with no spatial information. Numerous techniques have been developed to derive information about stellar rotation. Unfortunately, most observational data allow only for the surface rotational rate to be inferred. The internal rotational profile, which has a great effect on the stellar structure and evolution, remains hidden below the top layers of the star - the essential is hidden to the eyes. Asteroseismology allows us to "sense" indirectly deep below the stellar surface. Oscillations that propagate through the star provide information about the deep stellar interiors while they also distort the stellar surface in characteristic patterns leading to detectable brightness or velocity variations. Also, certain oscillation modes are sensitive to internal rotation and carry information on how the star is spinning deep inside. Thanks to the unprecedented quality of NASA's space telescope Kepler, numerous detailed observations of stars in various evolutionary stages are available. Such high quality data allow that for many stars, rotation can not only be constrained from surface rotation, but also investigated through seismic studies. The work presented in this thesis focuses on the oscillations and internal rotational gradient of evolved single and binary stars. It is shown that the seismic analysis can reach the cores of oscillating red-giant stars and that these cores are rapidly rotating, while nested in a slowly rotating convective envelope.

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-12-07

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

Direct Detection of Hardly Detectable Hidden Chirality of Hydrocarbons and Deuterated Isotopomers by a Helical Polyacetylene through Chiral Amplification and Memory.

PubMed

Maeda, Katsuhiro; Hirose, Daisuke; Okoshi, Natsuki; Shimomura, Kouhei; Wada, Yuya; Ikai, Tomoyuki; Kanoh, Shigeyoshi; Yashima, Eiji

2018-03-07

We report the first direct chirality sensing of a series of chiral hydrocarbons and isotopically chiral compounds (deuterated isotopomers), which are almost impossible to detect by conventional optical spectroscopic methods, by a stereoregular polyacetylene bearing 2,2'-biphenol-derived pendants. The polyacetylene showed a circular dichroism due to a preferred-handed helix formation in response to the hardly detectable hidden chirality of saturated tertiary or chiroptical quaternary hydrocarbons, and deuterated isotopomers. In sharp contrast to the previously reported sensory systems, the chirality detection by the polyacetylene relies on an excess one-handed helix formation induced by the chiral hydrocarbons and deuterated isotopomers via significant amplification of the chirality followed by its static memory, through which chiral information on the minute and hidden chirality can be stored as an excess of a single-handed helix memory for a long time.

Viewing Integrated-Circuit Interconnections By SEM

NASA Technical Reports Server (NTRS)

Lawton, Russel A.; Gauldin, Robert E.; Ruiz, Ronald P.

1990-01-01

Back-scattering of energetic electrons reveals hidden metal layers. Experiment shows that with suitable operating adjustments, scanning electron microscopy (SEM) used to look for defects in aluminum interconnections in integrated circuits. Enables monitoring, in situ, of changes in defects caused by changes in temperature. Gives truer picture of defects, as etching can change stress field of metal-and-passivation pattern, causing changes in defects.

Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking.

PubMed

Martarelli, D; Casettari, L; Shalaby, K S; Soliman, M E; Cespi, M; Bonacucina, G; Fagioli, L; Perinelli, D R; Lam, J K W; Palmieri, G F

2016-01-01

Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging. In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN. Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor. This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.

Neural net classification of x-ray pistachio nut data

NASA Astrophysics Data System (ADS)

Casasent, David P.; Sipe, Michael A.; Schatzki, Thomas F.; Keagy, Pamela M.; Le, Lan Chau

1996-12-01

Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.

A Software Package for Neural Network Applications Development

NASA Technical Reports Server (NTRS)

Baran, Robert H.

1993-01-01

Original Backprop (Version 1.2) is an MS-DOS package of four stand-alone C-language programs that enable users to develop neural network solutions to a variety of practical problems. Original Backprop generates three-layer, feed-forward (series-coupled) networks which map fixed-length input vectors into fixed length output vectors through an intermediate (hidden) layer of binary threshold units. Version 1.2 can handle up to 200 input vectors at a time, each having up to 128 real-valued components. The first subprogram, TSET, appends a number (up to 16) of classification bits to each input, thus creating a training set of input output pairs. The second subprogram, BACKPROP, creates a trilayer network to do the prescribed mapping and modifies the weights of its connections incrementally until the training set is leaned. The learning algorithm is the 'back-propagating error correction procedures first described by F. Rosenblatt in 1961. The third subprogram, VIEWNET, lets the trained network be examined, tested, and 'pruned' (by the deletion of unnecessary hidden units). The fourth subprogram, DONET, makes a TSR routine by which the finished product of the neural net design-and-training exercise can be consulted under other MS-DOS applications.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Neural Network Prediction of Aluminum-Lithium Weld Strengths from Acoustic Emission Amplitude Data

NASA Technical Reports Server (NTRS)

Hill, Eric v. K.; Israel, Peggy L.; Knotts, Gregory L.

1993-01-01

Acoustic Emission (AE) flaw growth activity was monitored in aluminum-lithium weld specimens from the onset tensile loading to failure. Data on actual ultimate strengths together with AE data from the beginning of loading up to 25 percent of the expected ultimate strength were used to train a backpropagation neural network to predict ultimate strengths. Architecturally, the fully interconnected network consisted of an input layer for the AE amplitude data, a hidden layer to accommodate failure mechanism mapping, and an output layer for ultimate strength prediction. The trained network was the applied to the prediction of ultimate strengths in the remaining six specimens. The worst case prediction error was found to be +2.6 percent.

Effect of Absolute Spatial Proximity between a Landmark and a Goal

ERIC Educational Resources Information Center

Chamizo, V. D.; Rodrigo, T.

2004-01-01

In two experiments rats were trained in a Morris pool to find a hidden platform in the presence of a single landmark. Circular black curtains surrounded the pool, with the single landmark inside this enclosure, so that no other room cues could provide additional information about the location of the platform. This landmark was hung from a false…

Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.

PubMed

ElAdel, Asma; Zaied, Mourad; Amar, Chokri Ben

2017-11-01

Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization is still a challenging subject. In this paper, we propose a fast DCNN based on Fast Wavelet Transform (FWT), intelligent dropout and layer skipping. The proposed approach led to improve the image retrieval accuracy as well as the searching time. This was possible thanks to three key advantages: First, the rapid way to compute the features using FWT. Second, the proposed intelligent dropout method is based on whether or not a unit is efficiently and not randomly selected. Third, it is possible to classify the image using efficient units of earlier layer(s) and skipping all the subsequent hidden layers directly to the output layer. Our experiments were performed on CIFAR-10 and MNIST datasets and the obtained results are very promising. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimal random search for a single hidden target.

PubMed

Snider, Joseph

2011-01-01

A single target is hidden at a location chosen from a predetermined probability distribution. Then, a searcher must find a second probability distribution from which random search points are sampled such that the target is found in the minimum number of trials. Here it will be shown that if the searcher must get very close to the target to find it, then the best search distribution is proportional to the square root of the target distribution regardless of dimension. For a Gaussian target distribution, the optimum search distribution is approximately a Gaussian with a standard deviation that varies inversely with how close the searcher must be to the target to find it. For a network where the searcher randomly samples nodes and looks for the fixed target along edges, the optimum is either to sample a node with probability proportional to the square root of the out-degree plus 1 or not to do so at all.

Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

PubMed Central

Chen, Yang; Shen, Kuang

2017-01-01

To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process. PMID:28943680

Oxytocin enhances pupil dilation and sensitivity to 'hidden' emotional expressions.

PubMed

Leknes, Siri; Wessberg, Johan; Ellingsen, Dan-Mikael; Chelnokova, Olga; Olausson, Håkan; Laeng, Bruno

2013-10-01

Sensing others' emotions through subtle facial expressions is a highly important social skill. We investigated the effects of intranasal oxytocin treatment on the evaluation of explicit and 'hidden' emotional expressions and related the results to individual differences in sensitivity to others' subtle expressions of anger and happiness. Forty healthy volunteers participated in this double-blind, placebo-controlled crossover study, which shows that a single dose of intranasal oxytocin (40 IU) enhanced or 'sharpened' evaluative processing of others' positive and negative facial expression for both explicit and hidden emotional information. Our results point to mechanisms that could underpin oxytocin's prosocial effects in humans. Importantly, individual differences in baseline emotional sensitivity predicted oxytocin's effects on the ability to sense differences between faces with hidden emotional information. Participants with low emotional sensitivity showed greater oxytocin-induced improvement. These participants also showed larger task-related pupil dilation, suggesting that they also allocated the most attentional resources to the task. Overall, oxytocin treatment enhanced stimulus-induced pupil dilation, consistent with oxytocin enhancement of attention towards socially relevant stimuli. Since pupil dilation can be associated with increased attractiveness and approach behaviour, this effect could also represent a mechanism by which oxytocin increases human affiliation.

Data-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodes

PubMed Central

Su, Ri-Qi; Wang, Wen-Xu; Wang, Xiao; Lai, Ying-Cheng

2016-01-01

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified. PMID:26909187

What about These Children? Assessing Poverty among the "Hidden Population" of Multiracial Children in Single-Mother Families. University of Kentucky Center for Poverty Research Discussion Paper Series, DP2010-09

ERIC Educational Resources Information Center

Bratter, Jenifer; Damaske, Sarah

2010-01-01

Capturing the conditions of children of color living in single-parent families has become more complex due to the growing presence of interracial households. This analysis assesses the size and poverty status of single-female headed families housing multiracial children. Using data from the 2000 Census, we find that 9 percent of female-headed…

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate).

PubMed

Shahsavari, Shadab; Rezaie Shirmard, Leila; Amini, Mohsen; Abedin Dokoosh, Farid

2017-01-01

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

New colored optical security elements using Rolic's LPP/LCP technology: devices for first- to third-level inspection

NASA Astrophysics Data System (ADS)

Moia, Franco

2002-04-01

With linear photo-polymerization (LPP) ROLIC has invented a photo-patternable technology enabling to align not only conventional liquid crystals but also liquid crystals polymers (LCP). ROLIC's optical security device technology derives from its LPP/LCP technology. LPP/LCP security devices are created by structured photo-alignment of an LPP layer through phot-masks, thus generating a high resolution, photo-patterned aligning layer which carries the aligning information of the image to be created. The subsequent LCP layer transforms the aligning information into an optical phase image with low and/or very high information content, such as invisible photographic pictures. The building block capability of the LPP/LCP technology allows the manufacturing of cholesteric and non-cholesteric LPP/LCP devices which cover 1st and/or 2nd level applications. Apart from black/white security devices colored information zones can be integrated. Moreover, we have developed an LPP/LCP security device which covers all three- 1st, 2nd and 3rd- inspection levels in one and the same authentication device: besides a color shift by tilting the device (1st level) and the detection of normally hidden information by use of a simple sheet polarizer (2nd level) the new device contains encrypted hidden information which can be visualized only by superimposing an LPP/LCP inspection tool (key) for decryption (3rd level). This optical key is also based on the LPP/LCP technology and is itself a 3rd level security device.

Probing many-body localization with neural networks

NASA Astrophysics Data System (ADS)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

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

Use of bremsstrahlung radiation to identify hidden weak β- sources: feasibility and possible use in radio-guided surgery

NASA Astrophysics Data System (ADS)

Carlotti, D.; Collamati, F.; Faccini, R.; Fresch, P.; Iacoangeli, F.; Mancini-Terracciano, C.; Marafini, M.; Mirabelli, R.; Recchia, L.; Russomando, A.; Solfaroli Camillocci, E.; Toppi, M.; Traini, G.; Bocci, V.

2017-11-01

The recent interest in β^- radionuclides for radio-guided surgery derives from the feature of the β radiation to release energy in few millimeters of tissue. Such feature can be used to locate residual tumors with a probe located in its immediate vicinity, determining the resection margins with an accuracy of millimeters. The drawback of this technique is that it does not allow to identify tumors hidden in more than few mm of tissue. Conversely, the bremsstrahlung X-rays emitted by the interaction of the β- radiation with the nuclei of the tissue are relatively penetrating. To complement the β- probes, we have therefore developed a detector based on cadmium telluride, an X-ray detector with a high quantum efficiency working at room temperature. We measured the secondary emission of bremsstrahlung photons in a target of Polymethylmethacrylate (PMMA) with a density similar to living tissue. The results show that this device allows to detect a 1 ml residual or lymph-node with an activity of 1 kBq hidden under a layer of 10 mm of PMMA with a 3:1 signal to noise, i.e. with a five sigma discrimination in less than 5 s.

Self-growing neural network architecture using crisp and fuzzy entropy

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.

1992-01-01

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed.

Self-growing neural network architecture using crisp and fuzzy entropy

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.

1992-01-01

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

[Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology].

PubMed

Milewski, Robert; Jamiołkowski, Jacek; Milewska Anna, Justyna; Domitrz, Jan; Szamatowicz, Jacek; Wołczyński, Sławomir

2009-12-01

Prognosis of pregnancy for patients treated with IVF ICSI/ET methods, using artificial neural networks. Retrospective study of 1007 cycles of infertility treatment of 899 patients of Department of Reproduction and Gynecological Endocrinology in Bialystok. The subjects were treated with IVF ICSI/ET method from August 2005 to September 2008. Classifying artificial neural network is described in the paper Architecture of the network is three-layered perceptron consisting of 45 neurons in the input layer 14 neurons in the hidden layer and a single output neuron. The source data for the network are 36 variables. 24 of them are nominal variables and the rest are quantitative variables. Among non-pregnancy cases only 59 prognosis of the network were incorrect. The results of treatment were correctly forecast in 68.5% of cases. The pregnancy was accurately confirmed in 49.1% of cases and lack of pregnancy in 86.5% of cases. Treatment of infertility with the use of in vitro fertilization methods continues to have too low efficiency per one treatment cycle. To improve this indicator it is necessary to find dependencies, which describe the model of IVF treatment. The application of advanced methods of bioinformatics allows to predict the result of the treatment more effectively With the help of artificial neural networks, we are able to forecast the failure of the treatment using IFV ICSI/ET procedure with almost 90% probability of certainty These possibilities can be used to predict negative cases.

Single Landmark Learning in Rats: Sex Differences in a Navigation Task

ERIC Educational Resources Information Center

Forcano, L.; Santamaria, J.; Mackintosh, N. J.; Chamizo, V. D.

2009-01-01

In Experiments 1 and 2, rats were trained in a Morris pool to find a hidden platform located some distance away from a single landmark. Males learned to swim to the platform faster than females, but on test trials without the platform, males, unlike females, spent less time in the platform quadrant of the pool in the second half of each test trial…

The Keck keyword layer

NASA Technical Reports Server (NTRS)

Conrad, A. R.; Lupton, W. F.

1992-01-01

Each Keck instrument presents a consistent software view to the user interface programmer. The view consists of a small library of functions, which are identical for all instruments, and a large set of keywords, that vary from instrument to instrument. All knowledge of the underlying task structure is hidden from the application programmer by the keyword layer. Image capture software uses the same function library to collect data for the image header. Because the image capture software and the instrument control software are built on top of the same keyword layer, a given observation can be 'replayed' by extracting keyword-value pairs from the image header and passing them back to the control system. The keyword layer features non-blocking as well as blocking I/O. A non-blocking keyword write operation (such as setting a filter position) specifies a callback to be invoked when the operation is complete. A non-blocking keyword read operation specifies a callback to be invoked whenever the keyword changes state. The keyword-callback style meshes well with the widget-callback style commonly used in X window programs. The first keyword library was built for the two Keck optical instruments. More recently, keyword libraries have been developed for the infrared instruments and for telescope control. Although the underlying mechanisms used for inter-process communication by each of these systems vary widely (Lick MUSIC, Sun RPC, and direct socket I/O, respectively), a basic user interface has been written that can be used with any of these systems. Since the keyword libraries are bound to user interface programs dynamically at run time, only a single set of user interface executables is needed. For example, the same program, 'xshow', can be used to display continuously the telescope's position, the time left in an instrument's exposure, or both values simultaneously. Less generic tools that operate on specific keywords, for example an X display that controls optical instrument exposures, have also been written using the keyword layer.

Enhanced magneto-optical imaging of internal stresses in the removed surface layer

NASA Astrophysics Data System (ADS)

Agalidi, Yuriy; Kozhukhar, Pavlo; Levyi, Sergii; Turbin, Dmitriy

2015-10-01

The paper describes a software method of reconstructing the state of the removed surface layer by visualising internal stresses in the underlying layers of the sample. Such a problem typically needs to be solved as part of forensic investigation that aims to reveal original marking of a sample with removed surface layer. For example, one may be interested in serial numbers of weapons or vehicles that had the surface layer of metal removed from the number plate. Experimental results of studying gradient internal stress fields in ferromagnetic sample using the NDI method of magneto-optical imaging (MOI) are presented. Numerical modelling results of internal stresses enclosed in the surface marking region are analysed and compared to the experimental results of magneto-optical imaging (MOI). MOI correction algorithm intended for reconstructing internal stress fields in the removed surface layer by extracting stresses retained by the underlying layers is described. Limiting ratios between parameters of a marking font are defined for the considered correction algorithm. Enhanced recognition properties for hidden stresses left by marking symbols are experimentally verified and confirmed.

Two-Dimensional Superconductivity Emerged at Monatomic Bi(2-) Square Net in Layered Y2O2Bi via Oxygen Incorporation.

PubMed

Sei, Ryosuke; Kitani, Suguru; Fukumura, Tomoteru; Kawaji, Hitoshi; Hasegawa, Tetsuya

2016-09-07

Discovery of layered superconductors such as cuprates and iron-based compounds has unveiled new science and compounds. In these superconductors, quasi-two-dimensional layers including transition metal cations play principal role in the superconductivity via carrier doping by means of aliovalent-ion substitution. Here, we report on a two-dimensional superconductivity at 2 K in ThCr2Si2-type layered oxide Y2O2Bi possessing conducting monatomic Bi(2-) square net, possibly associated with an exotic superconductivity. The superconductivity emerges only in excessively oxygen-incorporated Y2O2Bi with expanded inter-net distance, in stark contrast to nonsuperconducting pristine Y2O2Bi reported previously. This result suggests that the element incorporation into hidden interstitial site could be an alternative approach to conventional substitution and intercalation methods for search of novel superconductors.

HIPPI: highly accurate protein family classification with ensembles of HMMs.

PubMed

Nguyen, Nam-Phuong; Nute, Michael; Mirarab, Siavash; Warnow, Tandy

2016-11-11

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .

Nonlinear dynamical modes of climate variability: from curves to manifolds

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

The necessity of efficient dimensionality reduction methods capturing dynamical properties of the system from observed data is evident. Recent study shows that nonlinear dynamical mode (NDM) expansion is able to solve this problem and provide adequate phase variables in climate data analysis [1]. A single NDM is logical extension of linear spatio-temporal structure (like empirical orthogonal function pattern): it is constructed as nonlinear transformation of hidden scalar time series to the space of observed variables, i. e. projection of observed dataset onto a nonlinear curve. Both the hidden time series and the parameters of the curve are learned simultaneously using Bayesian approach. The only prior information about the hidden signal is the assumption of its smoothness. The optimal nonlinearity degree and smoothness are found using Bayesian evidence technique. In this work we do further extension and look for vector hidden signals instead of scalar with the same smoothness restriction. As a result we resolve multidimensional manifolds instead of sum of curves. The dimension of the hidden manifold is optimized using also Bayesian evidence. The efficiency of the extension is demonstrated on model examples. Results of application to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510

Constructive autoassociative neural network for facial recognition.

PubMed

Fernandes, Bruno J T; Cavalcanti, George D C; Ren, Tsang I

2014-01-01

Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior under varied global and local scale climatic influences from the developed BHMM.

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

PubMed

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

Artificial neural network-aided image analysis system for cell counting.

PubMed

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

Cryogenic adsorption of nitrogen on activated carbon: Experiment and modeling

NASA Astrophysics Data System (ADS)

Zou, Long-Hui; Liu, Hui-Ming; Gong, Ling-Hui

2018-03-01

A cryo-sorption device was built based on a commercial gas sorption analyzer with its sample chamber connected to the 2nd stage of the Gifford-McMahon (GM) cryocooler (by SUMITOMO Corporation), which could provide the operation temperature ranging from 4.5 K to 300 K; The nitrogen adsorption isotherms ranging from 95 to 160 K were obtained by volumetric method on the PICATIF activated carbon. Isosteric heat of adsorption was calculated using the Clausius-Clapeyron equation and was around 8 kJ/mol. Conventional isotherm models and the artificial neural network (ANN) were applied to analyze the adsorption data, the Dual-site Langmuir and the Toth equation turned out to be the most suitable empirical isotherm model; Adsorption equilibrium data at some temperature was used to train the neural network and the rest was used to validate and predict, it turned out that the accuracy of the prediction by the ANN increased with increasing hidden-layer, and it was within ±5% for the three-hidden-layer ANN, and it showed better performance than the conventional isotherm model; Considering large time consumption and complexity of the adsorption experiment, the ANN method can be applied to get more adsorption data based on the already known experimental data.

Automated endoscopic navigation and advisory system from medical image

NASA Astrophysics Data System (ADS)

Kwoh, Chee K.; Khan, Gul N.; Gillies, Duncan F.

1999-05-01

In this paper, we present a review of the research conducted by our group to design an automatic endoscope navigation and advisory system. The whole system can be viewed as a two-layer system. The first layer is at the signal level, which consists of the processing that will be performed on a series of images to extract all the identifiable features. The information is purely dependent on what can be extracted from the 'raw' images. At the signal level, the first task is performed by detecting a single dominant feature, lumen. Few methods of identifying the lumen are proposed. The first method used contour extraction. Contours are extracted by edge detection, thresholding and linking. This method required images to be divided into overlapping squares (8 by 8 or 4 by 4) where line segments are extracted by using a Hough transform. Perceptual criteria such as proximity, connectivity, similarity in orientation, contrast and edge pixel intensity, are used to group edges both strong and weak. This approach is called perceptual grouping. The second method is based on a region extraction using split and merge approach using spatial domain data. An n-level (for a 2' by 2' image) quadtree based pyramid structure is constructed to find the most homogenous large dark region, which in most cases corresponds to the lumen. The algorithm constructs the quadtree from the bottom (pixel) level upward, recursively and computes the mean and variance of image regions corresponding to quadtree nodes. On reaching the root, the largest uniform seed region, whose mean corresponds to a lumen is selected that is grown by merging with its neighboring regions. In addition to the use of two- dimensional information in the form of regions and contours, three-dimensional shape can provide additional information that will enhance the system capabilities. Shape or depth information from an image is estimated by various methods. A particular technique suitable for endoscopy is the shape from shading, which is developed to obtain the relative depth of the colon surface in the image by assuming a point light source very close to the camera. If we assume the colon has a shape similar to a tube, then a reasonable approximation of the position of the center of the colon (lumen) will be a function of the direction in which the majority of the normal vectors of shape are pointing. The second layer is the control layer and at this level, a decision model must be built for endoscope navigation and advisory system. The system that we built is the models of probabilistic networks that create a basic, artificial intelligence system for navigation in the colon. We have constructed the probabilistic networks from correlated objective data using the maximum weighted spanning tree algorithm. In the construction of a probabilistic network, it is always assumed that the variables starting from the same parent are conditionally independent. However, this may not hold and will give rise to incorrect inferences. In these cases, we proposed the creation of a hidden node to modify the network topology, which in effect models the dependency of correlated variables, to solve the problem. The conditional probability matrices linking the hidden node to its neighbors are determined using a gradient descent method which minimizing the objective cost function. The error gradients can be treated as updating messages and ca be propagated in any direction throughout any singly connected network to adjust the network parameters. With the above two- level approach, we have been able to build an automated endoscope navigation and advisory system successfully.

View-Dependent Streamline Deformation and Exploration

PubMed Central

Tong, Xin; Edwards, John; Chen, Chun-Ming; Shen, Han-Wei; Johnson, Chris R.; Wong, Pak Chung

2016-01-01

Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely. PMID:26600061

View-Dependent Streamline Deformation and Exploration

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

Tong, Xin; Edwards, John; Chen, Chun-Ming

Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual cluttering for visualizing 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures.more » Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.« less

Camouflaging in Digital Image for Secure Communication

NASA Astrophysics Data System (ADS)

Jindal, B.; Singh, A. P.

2013-06-01

The present paper reports on a new type of camouflaging in digital image for hiding crypto-data using moderate bit alteration in the pixel. In the proposed method, cryptography is combined with steganography to provide a two layer security to the hidden data. The novelty of the algorithm proposed in the present work lies in the fact that the information about hidden bit is reflected by parity condition in one part of the image pixel. The remaining part of the image pixel is used to perform local pixel adjustment to improve the visual perception of the cover image. In order to examine the effectiveness of the proposed method, image quality measuring parameters are computed. In addition to this, security analysis is also carried by comparing the histograms of cover and stego images. This scheme provides a higher security as well as robustness to intentional as well as unintentional attacks.

View-Dependent Streamline Deformation and Exploration.

PubMed

Tong, Xin; Edwards, John; Chen, Chun-Ming; Shen, Han-Wei; Johnson, Chris R; Wong, Pak Chung

2016-07-01

Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.

Hidden reentrant and Larkin-Ovchinnikov-Fulde-Ferrell superconducting phases in a magnetic field in a (TMTSF)2ClO4.

PubMed

Lebed, A G

2011-08-19

We solve a long-standing problem about a theoretical description of the upper critical magnetic field, parallel to conducting layers and perpendicular to conducting chains, in a (TMTSF)(2)ClO(4) superconductor. In particular, we explain why the experimental upper critical field, H(c2)(b')≃6 T, is higher than both the quasiclassical upper critical field and the Clogston paramagnetic limit. We show that this property is due to the coexistence of the hidden reentrant and Larkin-Ovchinnikov-Fulde-Ferrell phases in a magnetic field in the form of three plane waves with nonzero momenta of the Cooper pairs. Our results are in good qualitative and quantitative agreement with the recent experimental measurements of H(c2)(b') and support a singlet d-wave-like scenario of superconductivity in (TMTSF)(2)ClO(4). © 2011 American Physical Society

Deep SOMs for automated feature extraction and classification from big data streaming

NASA Astrophysics Data System (ADS)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

NASA Astrophysics Data System (ADS)

Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

2018-02-01

Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

Imaging through scattering media by Fourier filtering and single-pixel detection

NASA Astrophysics Data System (ADS)

Jauregui-Sánchez, Y.; Clemente, P.; Lancis, J.; Tajahuerce, E.

2018-02-01

We present a novel imaging system that combines the principles of Fourier spatial filtering and single-pixel imaging in order to recover images of an object hidden behind a turbid medium by transillumination. We compare the performance of our single-pixel imaging setup with that of a conventional system. We conclude that the introduction of Fourier gating improves the contrast of images in both cases. Furthermore, we show that the combination of single-pixel imaging and Fourier spatial filtering techniques is particularly well adapted to provide images of objects transmitted through scattering media.

Diversity in a Hidden Community: Tardiagrades in Lichens.

ERIC Educational Resources Information Center

Shofner, Marcia; Vodopich, Darrell

1993-01-01

Describes an interesting field experiment examining the distribution and diversity of a single community using lichens and the animals living in them. Combining field experience and laboratory work reveals not only the biology of some unusual organisms, but also community ecology and diversity. (PR)

Entanglement generation and manipulation in the Hong-Ou-Mandel experiment: a hidden scenario beyond two-photon interference

NASA Astrophysics Data System (ADS)

Yang, Li-Kai; Cai, Han; Peng, Tao; Wang, Da-Wei

2018-06-01

The Hong‑Ou‑Mandel (HOM) effect was long believed to be a two-photon interference phenomenon. It describes the fact that two indistinguishable photons mixed at a beam splitter will bunch together to one of the two output modes. Considering the two single-photon emitters such as trapped ions, we explore a hidden scenario of the HOM effect, where entanglement can be generated between the two ions when a single photon is detected by one of the detectors. A second photon emitted by the entangled photon sources will be subsequently detected by the same detector. However, we can also control the fate of the second photon by manipulating the entangled state. Instead of two-photon interference, the phase of the entangled state is responsible for the photon’s path in our proposal. Toward a feasible experimental realization, we conduct a quantum jump simulation on the system to show its robustness against experimental errors.

Violation of a Bell-like inequality in single-neutron interferometry.

PubMed

Hasegawa, Yuji; Loidl, Rudolf; Badurek, Gerald; Baron, Matthias; Rauch, Helmut

2003-09-04

Non-local correlations between spatially separated systems have been extensively discussed in the context of the Einstein, Podolsky and Rosen (EPR) paradox and Bell's inequalities. Many proposals and experiments designed to test hidden variable theories and the violation of Bell's inequalities have been reported; usually, these involve correlated photons, although recently an experiment was performed with (9)Be(+) ions. Nevertheless, it is of considerable interest to show that such correlations (arising from quantum mechanical entanglement) are not simply a peculiarity of photons. Here we measure correlations between two degrees of freedom (comprising spatial and spin components) of single neutrons; this removes the need for a source of entangled neutron pairs, which would present a considerable technical challenge. A Bell-like inequality is introduced to clarify the correlations that can arise between observables of otherwise independent degrees of freedom. We demonstrate the violation of this Bell-like inequality: our measured value is 2.051 +/- 0.019, clearly above the value of 2 predicted by classical hidden variable theories.

Prediction of β-turns in proteins from multiple alignment using neural network

PubMed Central

Kaur, Harpreet; Raghava, Gajendra Pal Singh

2003-01-01

A neural network-based method has been developed for the prediction of β-turns in proteins by using multiple sequence alignment. Two feed-forward back-propagation networks with a single hidden layer are used where the first-sequence structure network is trained with the multiple sequence alignment in the form of PSI-BLAST–generated position-specific scoring matrices. The initial predictions from the first network and PSIPRED-predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross-validation on a set of 426 nonhomologous protein chains. The corresponding Qpred, Qobs, and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published β-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach. PMID:12592033

Door detection in images based on learning by components

NASA Astrophysics Data System (ADS)

Cicirelli, Grazia; D'Orazio, Tiziana; Ancona, Nicola

2001-10-01

In this paper we present a vision-based technique for detecting targets of the environment which has to be reached by an autonomous mobile robot during its navigational task. The targets the robot has to reach are the doors of our office building. Color and shape information are used as identifying features for detecting principal components of the door. In fact in images the door can appear of different dimensions depending on the attitude of the robot with respect to the door, therefore detection of the door is performed by detecting its most significant components in the image. Positive and negative examples, in form of image patterns, are manually selected from real images for training two neural classifiers in order to recognize the single components. Each classifier has been realized by a feed-forward neural network with one hidden layer and sigmoid activation function. Moreover for selecting negative examples, relevant for the problem at hand, a bootstrap technique has been used during the training process. Finally the detecting system has been applied to several test real images for evaluating its performance.

A continually online-trained neural network controller for brushless DC motor drives

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

Rubaai, A.; Kotaru, R.; Kankam, M.D.

2000-04-01

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless dc motor drives. The dynamics of the motor/load are modeled online, and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This schemes incorporates three multilayered feedforward neural networks that are online trained, using the Levenburg-Marquadtmore » training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods. The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.« less

Triangular Quantum Loop Topography for Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Yi; Kim, Eun-Ah

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems there has been little success in training neural networks to identify topological phases. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce triangular quantum loop (TQL) topography: a procedure of constructing a multi-dimensional image from the ''sample'' Hamiltonian or wave function using two-point functions that form triangles. Feeding the TQL topography to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. Given the versatility of the TQL topography procedure that can handle different lattice geometries, disorder, interaction and even degeneracy our work paves the route towards powerful applications of machine learning in the study of topological quantum matters.

Adaptive Approximation-Based Regulation Control for a Class of Uncertain Nonlinear Systems Without Feedback Linearizability.

PubMed

Wang, Ning; Sun, Jing-Chao; Han, Min; Zheng, Zhongjiu; Er, Meng Joo

2017-09-06

In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.

Mystery of the Hidden Cosmos [Complex Dark Matter

DOE PAGES

Dobrescu, Bogdan A.; Lincoln, Don

2015-06-16

Scientists know there must be more matter in the universe than what is visible. Searches for this dark matter have focused on a single unseen particle, but decades of experiments have been unsuccessful at finding it. Exotic possibilities for dark matter are looking increasingly plausible. Rather than just one particle, dark matter could contain an entire world of particles and forces that barely interact with normal matter. Complex dark matter could form dark atoms and molecules and even clump together to make hidden galactic disks that overlap with the spiral arms of the Milky Way and other galaxies. Experiments aremore » under way to search for evidence of such a dark sector.« less

Uncovering the density of nanowire surface trap states hidden in the transient photoconductance.

PubMed

Xu, Qiang; Dan, Yaping

2016-09-21

The gain of nanoscale photoconductors is closely correlated with surface trap states. Mapping out the density of surface trap states in the semiconductor bandgap is crucial for engineering the performance of nanoscale photoconductors. Traditional capacitive techniques for the measurement of surface trap states are not readily applicable to nanoscale devices. Here, we demonstrate a simple technique to extract the information on the density of surface trap states hidden in the transient photoconductance that is widely observed. With this method, we found that the density of surface trap states of a single silicon nanowire is ∼10(12) cm(-2) eV(-1) around the middle of the upper half bandgap.

Two states or not two states: Single-molecule folding studies of protein L

NASA Astrophysics Data System (ADS)

Aviram, Haim Yuval; Pirchi, Menahem; Barak, Yoav; Riven, Inbal; Haran, Gilad

2018-03-01

Experimental tools of increasing sophistication have been employed in recent years to study protein folding and misfolding. Folding is considered a complex process, and one way to address it is by studying small proteins, which seemingly possess a simple energy landscape with essentially only two stable states, either folded or unfolded. The B1-IgG binding domain of protein L (PL) is considered a model two-state folder, based on measurements using a wide range of experimental techniques. We applied single-molecule fluorescence resonance energy transfer (FRET) spectroscopy in conjunction with a hidden Markov model analysis to fully characterize the energy landscape of PL and to extract the kinetic properties of individual molecules of the protein. Surprisingly, our studies revealed the existence of a third state, hidden under the two-state behavior of PL due to its small population, ˜7%. We propose that this minority intermediate involves partial unfolding of the two C-terminal β strands of PL. Our work demonstrates that single-molecule FRET spectroscopy can be a powerful tool for a comprehensive description of the folding dynamics of proteins, capable of detecting and characterizing relatively rare metastable states that are difficult to observe in ensemble studies.

Longitudinal transvaginal ultrasound evaluation of cesarean scar niche incidence and depth in the first two years after single- or double-layer uterotomy closure: a randomized controlled trial.

PubMed

Bamberg, Christian; Hinkson, Larry; Dudenhausen, Joachim W; Bujak, Verena; Kalache, Karim D; Henrich, Wolfgang

2017-12-01

Cesarean deliveries are the most common abdominal surgery procedure globally, and the optimal way to suture the hysterotomy remains a matter of debate. The aim of this study was to assess the incidence of cesarean scar niches and the depth after single- or double-layer uterine closure. We performed a randomized controlled trial in which women were allocated to three uterotomy suture techniques: continuous single-layer unlocked, continuous locked single-layer, or double-layer sutures. Transvaginal ultrasound was performed six weeks and 6-24 months after cesarean delivery [Clinicaltrials.gov (NCT02338388)]. The study included 435 women. Six weeks after delivery, the incidence of niche was not significantly different between the groups (p = 0.52): 40% for single-layer unlocked, 32% for single-layer locked and 43% for double-layer sutures. The mean ± SD niche depths were 3.0 ± 1.4 mm for single-layer unlocked, 3.6 ± 1.7 mm for single-layer locked and 3.3 ± 1.3 mm for double-layer sutures (p = 1.0). There were no significant differences (p = 0.58) in niche incidence between the three groups at the second ultrasound follow up: 30% for single-layer unlocked, 23% for single-layer locked and 29% for double-layer sutures. The mean ± SD niche depth was 3.1 ± 1.5 mm after single-layer unlocked, 2.8 ± 1.5 mm after single-layer locked and 2.5 ± 1.2 mm after double-layer sutures (p = 0.61). There was a trend (p = 0.06) for the residual myometrium thickness to be thicker after double-layer repair at the long-term follow up. The incidence of cesarean scar niche formation and the niche depth was independent of the hysterotomy closure technique. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Face Recognition with the Karhunen-Loeve Transform

DTIC Science & Technology

1991-12-01

anthropometry community? 1-2 Methodology As part of this thesis, face recognition software is developed on the Silicon Graphics 4D Personal Iris...the anthropometry community. Standards The most important performance criteria is classification accuracy which is the per- centage of correct...demonstrated by Tarr (24). Reconstructed Output Image yl y2 ... y64 16 hidden layer units xl x2 ... x64 Input 64 by 64 pixel Image Figure 2.6. After the

Cascaded VLSI Chips Help Neural Network To Learn

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher; Thakoor, Anilkumar P.

1993-01-01

Cascading provides 12-bit resolution needed for learning. Using conventional silicon chip fabrication technology of VLSI, fully connected architecture consisting of 32 wide-range, variable gain, sigmoidal neurons along one diagonal and 7-bit resolution, electrically programmable, synaptic 32 x 31 weight matrix implemented on neuron-synapse chip. To increase weight nominally from 7 to 13 bits, synapses on chip individually cascaded with respective synapses on another 32 x 32 matrix chip with 7-bit resolution synapses only (without neurons). Cascade correlation algorithm varies number of layers effectively connected into network; adds hidden layers one at a time during learning process in such way as to optimize overall number of neurons and complexity and configuration of network.

Two-layer anti-reflection strategies for implant applications

NASA Astrophysics Data System (ADS)

Guerrero, Douglas J.; Smith, Tamara; Kato, Masakazu; Kimura, Shigeo; Enomoto, Tomoyuki

2006-03-01

A two-layer bottom anti-reflective coating (BARC) concept in which a layer that develops slowly is coated on top of a bottom layer that develops more rapidly was demonstrated. Development rate control was achieved by selection of crosslinker amount and BARC curing conditions. A single-layer BARC was compared with the two-layer BARC concept. The single-layer BARC does not clear out of 200-nm deep vias. When the slower developing single-layer BARC was coated on top of the faster developing layer, the vias were cleared. Lithographic evaluation of the two-layer BARC concept shows the same resolution advantages as the single-layer system. Planarization properties of a two-layer BARC system are better than for a single-layer system, when comparing the same total nominal thicknesses.

Two-layer contractive encodings for learning stable nonlinear features.

PubMed

Schulz, Hannes; Cho, Kyunghyun; Raiko, Tapani; Behnke, Sven

2015-04-01

Unsupervised learning of feature hierarchies is often a good strategy to initialize deep architectures for supervised learning. Most existing deep learning methods build these feature hierarchies layer by layer in a greedy fashion using either auto-encoders or restricted Boltzmann machines. Both yield encoders which compute linear projections of input followed by a smooth thresholding function. In this work, we demonstrate that these encoders fail to find stable features when the required computation is in the exclusive-or class. To overcome this limitation, we propose a two-layer encoder which is less restricted in the type of features it can learn. The proposed encoder is regularized by an extension of previous work on contractive regularization. This proposed two-layer contractive encoder potentially poses a more difficult optimization problem, and we further propose to linearly transform hidden neurons of the encoder to make learning easier. We demonstrate the advantages of the two-layer encoders qualitatively on artificially constructed datasets as well as commonly used benchmark datasets. We also conduct experiments on a semi-supervised learning task and show the benefits of the proposed two-layer encoders trained with the linear transformation of perceptrons. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tomographic inversion of time-domain resistivity and chargeability data for the investigation of landfills using a priori information.

PubMed

De Donno, Giorgio; Cardarelli, Ettore

2017-01-01

In this paper, we present a new code for the modelling and inversion of resistivity and chargeability data using a priori information to improve the accuracy of the reconstructed model for landfill. When a priori information is available in the study area, we can insert them by means of inequality constraints on the whole model or on a single layer or assigning weighting factors for enhancing anomalies elongated in the horizontal or vertical directions. However, when we have to face a multilayered scenario with numerous resistive to conductive transitions (the case of controlled landfills), the effective thickness of the layers can be biased. The presented code includes a model-tuning scheme, which is applied after the inversion of field data, where the inversion of the synthetic data is performed based on an initial guess, and the absolute difference between the field and synthetic inverted models is minimized. The reliability of the proposed approach has been supported in two real-world examples; we were able to identify an unauthorized landfill and to reconstruct the geometrical and physical layout of an old waste dump. The combined analysis of the resistivity and chargeability (normalised) models help us to remove ambiguity due to the presence of the waste mass. Nevertheless, the presence of certain layers can remain hidden without using a priori information, as demonstrated by a comparison of the constrained inversion with a standard inversion. The robustness of the above-cited method (using a priori information in combination with model tuning) has been validated with the cross-section from the construction plans, where the reconstructed model is in agreement with the original design. Copyright © 2016 Elsevier Ltd. All rights reserved.

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

PubMed

Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Phonon exchange by two-dimensional electrons in intermediate magnetic fields

NASA Astrophysics Data System (ADS)

Gopalakrishnan, Gokul

The discovery of the integer and fractional quantum Hall effects have broadened the exploration of the two-dimensional electron gas to regimes where complex and exciting physics lay previously hidden. While many experimental investigations have focused on the regime of large magnetic fields where transport properties are determined by contributions from a single Landau level, the regime of intermediate fields, where multiple Landau levels are involved, has been much less explored. This dissertation is a report on a previously unobserved interaction probed by a novel type of magneto-transport measurement performed in this intermediate regime, in bilayer two-dimensional electron systems. This measurement technique, known as electron drag, directly measures interlayer electron-electron scattering rates, by measuring the voltage induced in one of the layers when a current is driven through the other. The scattering mechanism, which may be Coulomb or phonon mediated, depends critically on both the separation between the layers and the electron density. When electron drag is measured in the presence of a perpendicular magnetic field in suitable samples, the resulting magnetodrag signal reveals new information about the electronic states as well as properties of a phonon mediated scattering mechanism. This phonon scattering mechanism is reflected in previously unobserved oscillations. These oscillations, which are periodic in the inverse field, are argued to arise from a resonant interlayer exchange of 2 kF phonons. Measurements of the temperature, density and layer-spacing dependences of magnetodrag resistivity are reported and are shown to confirm this particular mechanism. Additionally, analysis of the temperature dependence reveals a strong sensitivity to Landau level widths. Based on this analysis, a means of characterizing the broadening of Landau levels and hence, electronic lifetimes in this regime, which are otherwise difficult to characterize, is proposed.

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

PubMed

Dai, Guoxian; Xie, Jin; Fang, Yi

2018-07-01

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

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.

Layer-specific gene expression in epileptogenic type II focal cortical dysplasia: normal-looking neurons reveal the presence of a hidden laminar organization

PubMed Central

2014-01-01

Background Type II focal cortical dysplasias (FCDs) are malformations of cortical development characterised by the disorganisation of the normal neocortical structure and the presence of dysmorphic neurons (DNs) and balloon cells (BCs). The pathogenesis of FCDs has not yet been clearly established, although a number of histopathological patterns and molecular findings suggest that they may be due to abnormal neuronal and glial proliferation and migration processes. In order to gain further insights into cortical layering disruption and investigate the origin of DNs and BCs, we used in situ RNA hybridisation of human surgical specimens with a neuropathologically definite diagnosis of Type IIa/b FCD and a panel of layer-specific genes (LSGs) whose expression covers all cortical layers. We also used anti-phospho-S6 ribosomal protein antibody to investigate mTOR pathway hyperactivation. Results LSGs were expressed in both normal and abnormal cells (BCs and DNs) but their distribution was different. Normal-looking neurons, which were visibly reduced in the core of the lesion, were apparently located in the appropriate cortical laminae thus indicating a partial laminar organisation. On the contrary, DNs and BCs, labelled with anti-phospho-S6 ribosomal protein antibody, were spread throughout the cortex without any apparent rule and showed a highly variable LSG expression pattern. Moreover, LSGs did not reveal any differences between Type IIa and IIb FCD. Conclusion These findings suggest the existence of hidden cortical lamination involving normal-looking neurons, which retain their ability to migrate correctly in the cortex, unlike DNs which, in addition to their morphological abnormalities and mTOR hyperactivation, show an altered migratory pattern. Taken together these data suggest that an external or environmental hit affecting selected precursor cells during the very early stages of cortical development may disrupt normal cortical development. PMID:24735483

Black holes, hidden symmetries, and complete integrability

NASA Astrophysics Data System (ADS)

Frolov, Valeri P.; Krtouš, Pavel; Kubizňák, David

2017-11-01

The study of higher-dimensional black holes is a subject which has recently attracted vast interest. Perhaps one of the most surprising discoveries is a realization that the properties of higher-dimensional black holes with the spherical horizon topology and described by the Kerr-NUT-(A)dS metrics are very similar to the properties of the well known four-dimensional Kerr metric. This remarkable result stems from the existence of a single object called the principal tensor. In our review we discuss explicit and hidden symmetries of higher-dimensional Kerr-NUT-(A)dS black hole spacetimes. We start with discussion of the Killing and Killing-Yano objects representing explicit and hidden symmetries. We demonstrate that the principal tensor can be used as a "seed object" which generates all these symmetries. It determines the form of the geometry, as well as guarantees its remarkable properties, such as special algebraic type of the spacetime, complete integrability of geodesic motion, and separability of the Hamilton-Jacobi, Klein-Gordon, and Dirac equations. The review also contains a discussion of different applications of the developed formalism and its possible generalizations.

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.

PubMed

Ong, Lee-Ling S; Xinghua Zhang; Kundukad, Binu; Dauwels, Justin; Doyle, Patrick; Asada, H Harry

2016-08-01

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Black holes, hidden symmetries, and complete integrability.

PubMed

Frolov, Valeri P; Krtouš, Pavel; Kubizňák, David

2017-01-01

The study of higher-dimensional black holes is a subject which has recently attracted vast interest. Perhaps one of the most surprising discoveries is a realization that the properties of higher-dimensional black holes with the spherical horizon topology and described by the Kerr-NUT-(A)dS metrics are very similar to the properties of the well known four-dimensional Kerr metric. This remarkable result stems from the existence of a single object called the principal tensor. In our review we discuss explicit and hidden symmetries of higher-dimensional Kerr-NUT-(A)dS black hole spacetimes. We start with discussion of the Killing and Killing-Yano objects representing explicit and hidden symmetries. We demonstrate that the principal tensor can be used as a "seed object" which generates all these symmetries. It determines the form of the geometry, as well as guarantees its remarkable properties, such as special algebraic type of the spacetime, complete integrability of geodesic motion, and separability of the Hamilton-Jacobi, Klein-Gordon, and Dirac equations. The review also contains a discussion of different applications of the developed formalism and its possible generalizations.

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Nuclear magnetic resonance studies of pseudospin fluctuations in URu 2 Si 2

DOE PAGES

Shirer, K. R.; Haraldsen, J. T.; Dioguardi, A. P.; ...

2013-09-26

Here, we report 29Si nuclear magnetic resonance measurements in single crystals and aligned powders of URu 2Si 2 in the hidden order and paramagnetic phases. The spin-lattice relaxation data reveal evidence of pseudospin fluctuations of U moments in the paramagnetic phase. We find evidence for partial suppression of the density of states below 30 K and analyze the data in terms of a two-component spin-fermion model. We propose that this behavior is a realization of a pseudogap between the hidden-order transition T HO and 30 K. This behavior is then compared to other materials that demonstrate precursor fluctuations in amore » pseudogap regime above a ground state with long-range order.« less

Exploiting Hidden Layer Responses of Deep Neural Networks for Language Recognition

DTIC Science & Technology

2016-09-08

trained DNNs. We evaluated this ap- proach in NIST 2015 language recognition evaluation. The per- formances achieved by the proposed approach are very...activations, used in direct DNN-LID. Results from the LID experiments support our hypothesis. The LID experiments are performed on NIST Language Recognition...of-the-art I- vector system [3, 10, 11] in evaluation (eval) set of NIST LRE 2015. Combination of proposed technique and state-of-the-art I-vector

Connectionism and Compositional Semantics

DTIC Science & Technology

1989-05-01

can use their hidden layers to learn difficult discriminations. such as panty or the Penzias two clumps/three clumps problem, where the output is...sauce." For novel sentences that are similar to the training sentences (e.g., train on "the girl hit the boy," test on -the boy hit the girl "), the...overridden by semantic considerations. as in this example from Wendy Lehnert (personal communicanon): (5) John saw the girl with the telescope in a red

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

PubMed

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

Charge Density Waves and the Hidden Nesting of Purple Bronze KMo6O17

NASA Astrophysics Data System (ADS)

Su, Lei; Pereira, Vitor

The layered purple bronze KMo6O17, with its robust triple CDW phase up to high temperatures, became the emblematic example of the ''hidden nesting'' concept. Recent experiments suggest that, on the surface layers, its CDW phase can be stabilized at much higher temperatures, and with a tenfold increase in the electronic gap in comparison with the bulk. Despite such interesting fermiology and properties, the K and Na purple bronzes remain largely unexplored systems, most particularly so at the theoretical level. We introduce the first multi-orbital effective tight-binding model to describe the effect of electron-electron interactions in this system. Upon fixing all the effective hopping parameters in the normal state against an ab-initio band structure, and with only the overall scale of the interactions as sole adjustable parameter, we find that a self-consistent Hartree-Fock solution reproduces extremely well the experimental behavior of the charge density wave (CDW) order parameter in the full range 0 < T < Tc , as well as the precise reciprocal space locations of the partial gap opening and Fermi arc development. The interaction strengths extracted from fitting to the experimental CDW gap are consistent with those derived from an independent Stoner-type analysis This work was supported by the Singapore National Research Foundation under Grant NRF-CRP6-2010-05.

Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

NASA Astrophysics Data System (ADS)

Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

2017-04-01

In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

Is extreme learning machine feasible? A theoretical assessment (part I).

PubMed

Liu, Xia; Lin, Shaobo; Fang, Jian; Xu, Zongben

2015-01-01

An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have demonstrated the feasibility and high efficiency of ELM-like systems. It has, however, been open if this is true for any general applications. In this two-part paper, we conduct a comprehensive feasibility analysis of ELM. In Part I, we provide an answer to the question by theoretically justifying the following: 1) for some suitable activation functions, such as polynomials, Nadaraya-Watson and sigmoid functions, the ELM-like systems can attain the theoretical generalization bound of the FNNs with all connections adjusted, i.e., they do not degrade the generalization capability of the FNNs even when the connections with and within hidden neurons are randomly fixed; 2) the number of hidden neurons needed for an ELM-like system to achieve the theoretical bound can be estimated; and 3) whenever the activation function is taken as polynomial, the deduced hidden layer output matrix is of full column-rank, therefore the generalized inverse technique can be efficiently applied to yield the solution of an ELM-like system, and, furthermore, for the nonpolynomial case, the Tikhonov regularization can be applied to guarantee the weak regularity while not sacrificing the generalization capability. In Part II, however, we reveal a different aspect of the feasibility of ELM: there also exists some activation functions, which makes the corresponding ELM degrade the generalization capability. The obtained results underlie the feasibility and efficiency of ELM-like systems, and yield various generalizations and improvements of the systems as well.

Nonlinear Autoregressive Exogenous modeling of a large anaerobic digester producing biogas from cattle waste.

PubMed

Dhussa, Anil K; Sambi, Surinder S; Kumar, Shashi; Kumar, Sandeep; Kumar, Surendra

2014-10-01

In waste-to-energy plants, there is every likelihood of variations in the quantity and characteristics of the feed. Although intermediate storage tanks are used, but many times these are of inadequate capacity to dampen the variations. In such situations an anaerobic digester treating waste slurry operates under dynamic conditions. In this work a special type of dynamic Artificial Neural Network model, called Nonlinear Autoregressive Exogenous model, is used to model the dynamics of anaerobic digesters by using about one year data collected on the operating digesters. The developed model consists of two hidden layers each having 10 neurons, and uses 18days delay. There are five neurons in input layer and one neuron in output layer for a day. Model predictions of biogas production rate are close to plant performance within ±8% deviation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

PubMed

Coop, Robert; Mishtal, Aaron; Arel, Itamar

2013-10-01

Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.

[The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

PubMed

Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

2016-03-01

The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology combined with the artificial neural networks is proved to be a reliable and new method for the identification of the original place of Traditional Chinese Medicine.

Factor analysis of auto-associative neural networks with application in speaker verification.

PubMed

Garimella, Sri; Hermansky, Hynek

2013-04-01

Auto-associative neural network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer, which has fewer numbers of nodes than the dimensionality of input. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the universal background model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure may lead to overfitting as all the parameters of UBM-AANN are adapted. In this paper, we introduce and develop the factor analysis theory of AANNs to alleviate this problem. We hypothesize that only the weight matrix connecting the last nonlinear hidden layer and the output layer is speaker-specific, and further restrict it to a common low-dimensional subspace during adaptation. The subspace is learned using large amounts of development data, and is held fixed during adaptation. Thus, only the coordinates in a subspace, also known as i-vector, need to be estimated using speaker-specific data. The update equations are derived for learning both the common low-dimensional subspace and the i-vectors corresponding to speakers in the subspace. The resultant i-vector representation is used as a feature for the probabilistic linear discriminant analysis model. The proposed system shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields a 23% relative improvement in equal error rate over the previously proposed weighted least squares-based subspace AANNs system. The experiments on NIST-10 SRE confirm that these improvements are consistent and generalize across datasets.

A Prospective Randomized Clinical Trial of Single vs. Double Layer Closure of Hysterotomy at the Time of Cesarean Delivery: The Effect on Uterine Scar Thickness.

PubMed

Bamberg, Christian; Dudenhausen, Joachim W; Bujak, Verena; Rodekamp, Elke; Brauer, Martin; Hinkson, Larry; Kalache, Karim; Henrich, Wolfgang

2018-06-01

 We undertook a randomized clinical trial to examine the outcome of a single vs. a double layer uterine closure using ultrasound to assess uterine scar thickness.  Participating women were allocated to one of three uterotomy suture techniques: continuous single layer unlocked suturing, continuous locked single layer suturing, or double layer suturing. Transvaginal ultrasound of uterine scar thickness was performed 6 weeks and 6 - 24 months after Cesarean delivery. Sonographers were blinded to the closure technique.  An "intent-to-treat" and "as treated" ANOVA analysis included 435 patients (n = 149 single layer unlocked suturing, n = 157 single layer locked suturing, and n = 129 double layer suturing). 6 weeks postpartum, the median scar thickness did not differ among the three groups: 10.0 (8.5 - 12.3 mm) single layer unlocked vs. 10.1 (8.2 - 12.7 mm) single layer locked vs. 10.8 (8.1 - 12.8 mm) double layer; (p = 0.84). At the time of the second follow-up, the uterine scar was not significantly (p = 0.06) thicker if the uterus had been closed with a double layer closure 7.3 (5.7 - 9.1 mm), compared to single layer unlocked 6.4 (5.0 - 8.8 mm) or locked suturing techniques 6.8 (5.2 - 8.7 mm). Women who underwent primary or elective Cesarean delivery showed a significantly (p = 0.03, p = 0.02, "as treated") increased median scar thickness after double layer closure vs. single layer unlocked suture.  A double layer closure of the hysterotomy is associated with a thicker myometrium scar only in primary or elective Cesarean delivery patients. © Georg Thieme Verlag KG Stuttgart · New York.

A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

NASA Astrophysics Data System (ADS)

Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

2018-03-01

Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

The Hidden Costs of Low Four-Year Graduation Rates

ERIC Educational Resources Information Center

Sullivan, Daniel F.

2010-01-01

The single most important step colleges and universities--especially public colleges and universities--can take to lower the student and family cost of college attendance is to improve retention, thereby increasing the four-year graduation rate. The author believes that institutions with high rates of retention to graduation have those high rates…

Hidden Savings in your Bus Budget

ERIC Educational Resources Information Center

Newby, Ruth

2005-01-01

School transportation industry statistics show the annual average costs for operating and maintaining a single school bus range from $34,000 to $38,000. Operating a school bus fleet at high efficiency has a real impact on the dollars saved for a school district and the reliability of transportation service to students. In this article, the author…

Hidden Markov model for dependent mark loss and survival estimation

USGS Publications Warehouse

Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.

2014-01-01

Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

Hidden phase in a two-dimensional Sn layer stabilized by modulation hole doping

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

Ming, Fangfei; Mulugeta Amare, Daniel; Tu, Weisong

Semiconductor surfaces and ultrathin interfaces exhibit an interesting variety of two-dimensional quantum matter phases, such as charge density waves, spin density waves and superconducting condensates. Yet, the electronic properties of these broken symmetry phases are extremely difficult to control due to the inherent difficulty of doping a strictly two-dimensional material without introducing chemical disorder. Here we successfully exploit a modulation doping scheme to uncover, in conjunction with a scanning tunnelling microscope tip-assist, a hidden equilibrium phase in a hole-doped bilayer of Sn on Si(111). This new phase is intrinsically phase separated into insulating domains with polar and nonpolar symmetries. Itsmore » formation involves a spontaneous symmetry breaking process that appears to be electronically driven, notwithstanding the lack of metallicity in this system. This modulation doping approach allows access to novel phases of matter, promising new avenues for exploring competing quantum matter phases on a silicon platform.« less

(In)Visibility Online: The Benefits of Online Patient Forums for People with a Hidden Illness: The Case of Multiple Chemical Sensitivity (MCS).

PubMed

Phillips, Tarryn; Rees, Tyson

2018-06-01

Sufferers of medically unexplained conditions that are not observable in the clinic can experience multiple layers of invisibility: a lack of biomedical diagnosis; legal skepticism; political disinterest; and a loss of their prior social identity. For those with environmental sensitivities, this is compounded by literal hiddenness due to often being housebound. Drawing on an online survey of people with multiple chemical sensitivity, this article examines how the everyday experience of invisibility is mitigated by engaging with other patients online. Respondents used online forums to undertake various forms of "visibility work," including attempts to crystallize their suffering into something recognizable medically, legally, and politically, and to reconstruct an identity considered valid and deserving-although the therapeutic potential of online support was contingent on intra-group politics. This study demonstrates that online forums allow biomedicine's "invisible others" to struggle for alternative forms of recognition beyond the clinical gaze. © 2017 by the American Anthropological Association.

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects

NASA Astrophysics Data System (ADS)

Fang, Shin-Yi; Smith, Garrett; Tabor, Whitney

2018-04-01

This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of "modality-specific" encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship.

Hidden phase in a two-dimensional Sn layer stabilized by modulation hole doping

DOE PAGES

Ming, Fangfei; Mulugeta Amare, Daniel; Tu, Weisong; ...

2017-03-07

Semiconductor surfaces and ultrathin interfaces exhibit an interesting variety of two-dimensional quantum matter phases, such as charge density waves, spin density waves and superconducting condensates. Yet, the electronic properties of these broken symmetry phases are extremely difficult to control due to the inherent difficulty of doping a strictly two-dimensional material without introducing chemical disorder. Here we successfully exploit a modulation doping scheme to uncover, in conjunction with a scanning tunnelling microscope tip-assist, a hidden equilibrium phase in a hole-doped bilayer of Sn on Si(111). This new phase is intrinsically phase separated into insulating domains with polar and nonpolar symmetries. Itsmore » formation involves a spontaneous symmetry breaking process that appears to be electronically driven, notwithstanding the lack of metallicity in this system. This modulation doping approach allows access to novel phases of matter, promising new avenues for exploring competing quantum matter phases on a silicon platform.« less

Terahertz reflection interferometry for automobile paint layer thickness measurement

NASA Astrophysics Data System (ADS)

Rahman, Aunik; Tator, Kenneth; Rahman, Anis

2015-05-01

Non-destructive terahertz reflection interferometry offers many advantages for sub-surface inspection such as interrogation of hidden defects and measurement of layers' thicknesses. Here, we describe a terahertz reflection interferometry (TRI) technique for non-contact measurement of paint panels where the paint is comprised of different layers of primer, basecoat, topcoat and clearcoat. Terahertz interferograms were generated by reflection from different layers of paints on a metallic substrate. These interferograms' peak spacing arising from the delay-time response of respective layers, allow one to model the thicknesses of the constituent layers. Interferograms generated at different incident angles show that the interferograms are more pronounced at certain angles than others. This "optimum" angle is also a function of different paint and substrate combinations. An automated angular scanning algorithm helps visualizing the evolution of the interferograms as a function of incident angle and also enables the identification of optimum reflection angle for a given paint-substrate combination. Additionally, scanning at different points on a substrate reveals that there are observable variations from one point to another of the same sample over its entire surface area. This ability may be used as a quality control tool for in-situ inspection in a production line. Keywords: Terahertz reflective interferometry, Paint and coating layers, Non-destructive

Modular representation of layered neural networks.

PubMed

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rashba effect in single-layer antimony telluroiodide SbTeI

DOE PAGES

Zhuang, Houlong L.; Cooper, Valentino R.; Xu, Haixuan; ...

2015-09-04

Exploring spin-orbit coupling (SOC) in single-layer materials is important for potential spintronics applications. In this paper, using first-principles calculations, we show that single-layer antimony telluroiodide SbTeI behaves as a two-dimensional semiconductor exhibiting a G 0W 0 band gap of 1.82 eV. More importantly, we observe the Rashba spin splitting in the SOC band structure of single-layer SbTeI with a sizable Rashba coupling parameter of 1.39 eV Å, which is significantly larger than that of a number of two-dimensional systems including surfaces and interfaces. The low formation energy and real phonon modes of single-layer SbTeI imply that it is stable. Finally,more » our study suggests that single-layer SbTeI is a candidate single-layer material for applications in spintronics devices.« less

Bit-serial neuroprocessor architecture

NASA Technical Reports Server (NTRS)

Tawel, Raoul (Inventor)

2001-01-01

A neuroprocessor architecture employs a combination of bit-serial and serial-parallel techniques for implementing the neurons of the neuroprocessor. The neuroprocessor architecture includes a neural module containing a pool of neurons, a global controller, a sigmoid activation ROM look-up-table, a plurality of neuron state registers, and a synaptic weight RAM. The neuroprocessor reduces the number of neurons required to perform the task by time multiplexing groups of neurons from a fixed pool of neurons to achieve the successive hidden layers of a recurrent network topology.

Effect of design selection on response surface performance

NASA Technical Reports Server (NTRS)

Carpenter, William C.

1993-01-01

Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net and the number of designs needed to train an approximation is discussed.

Development and application of deep convolutional neural network in target detection

NASA Astrophysics Data System (ADS)

Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

2018-04-01

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

Single-layer ZnMN2 (M = Si, Ge, Sn) zinc nitrides as promising photocatalysts.

PubMed

Bai, Yujie; Luo, Gaixia; Meng, Lijuan; Zhang, Qinfang; Xu, Ning; Zhang, Haiyang; Wu, Xiuqiang; Kong, Fanjie; Wang, Baolin

2018-05-30

Searching for two-dimensional semiconductor materials that are suitable for visible-light photocatalytic water splitting provides a sustainable solution to deal with the future energy crisis and environmental problems. Herein, based on first-principles calculations, single-layer ZnMN2 (M = Si, Ge, Sn) zinc nitrides are proposed as efficient photocatalysts for water splitting. Stability analyses show that the single-layer ZnMN2 zinc nitrides exhibit energetic and dynamical stability. The electronic properties reveal that all of the single-layer ZnMN2 zinc nitrides are semiconductors. Interestingly, single-layer ZnSnN2 is a direct band gap semiconductor with a desirable band gap (1.74 eV), and the optical adsorption spectrum confirms its optical absorption in the visible light region. The hydrogen evolution reaction (HER) calculations show that the catalytic activity for single-layer ZnMN2 (M = Ge, Sn) is better than that of single-layer ZnSiN2. Furthermore, the band gaps and band edge positions for the single-layer ZnMN2 zinc nitrides can be effectively tuned by biaxial strain. Especially, single-layer ZnGeN2 can be effectively tuned to match better with the redox potentials of water and enhance the light absorption in the visible light region at a tensile strain of 5%, which is confirmed by the corresponding optical absorption spectrum. Our results provide guidance for experimental synthesis efforts and future searches for single-layer materials suitable for photocatalytic water splitting.

Single-Molecule Titration in a Protein Nanoreactor Reveals the Protonation/Deprotonation Mechanism of a C:C Mismatch in DNA.

PubMed

Ren, Hang; Cheyne, Cameron G; Fleming, Aaron M; Burrows, Cynthia J; White, Henry S

2018-04-18

Measurement of single-molecule reactions can elucidate microscopic mechanisms that are often hidden from ensemble analysis. Herein, we report the acid-base titration of a single DNA duplex confined within the wild-type α-hemolysin (α-HL) nanopore for up to 3 h, while monitoring the ionic current through the nanopore. Modulation between two states in the current-time trace for duplexes containing the C:C mismatch in proximity to the latch constriction of α-HL is attributed to the base flipping of the C:C mismatch. As the pH is lowered, the rate for the C:C mismatch to flip from the intra-helical state to the extra-helical state ( k intra-extra ) decreases, while the rate for base flipping from the extra-helical state to the intra-helical state ( k extra-intra ) remains unchanged. Both k intra-extra and k extra-intra are on the order of 1 × 10 -2 s -1 to 1 × 10 -1 s -1 and remain stable over the time scale of the measurement (several hours). Analysis of the pH-dependent kinetics of base flipping using a hidden Markov kinetic model demonstrates that protonation/deprotonation occurs while the base pair is in the intra-helical state. We also demonstrate that the rate of protonation is limited by transport of H + into the α-HL nanopore. Single-molecule kinetic isotope experiments exhibit a large kinetic isotope effect (KIE) for k intra-extra ( k H / k D ≈ 5) but a limited KIE for k extra-intra ( k H / k D ≈ 1.3), supporting our model. Our experiments correspond to the longest single-molecule measurements performed using a nanopore, and demonstrate its application in interrogating mechanisms of single-molecule reactions in confined geometries.

Field Extension of Real Values of Physical Observables in Classical Theory can Help Attain Quantum Results

NASA Astrophysics Data System (ADS)

Wang, Hai; Kumar, Asutosh; Cho, Minhyung; Wu, Junde

2018-04-01

Physical quantities are assumed to take real values, which stems from the fact that an usual measuring instrument that measures a physical observable always yields a real number. Here we consider the question of what would happen if physical observables are allowed to assume complex values. In this paper, we show that by allowing observables in the Bell inequality to take complex values, a classical physical theory can actually get the same upper bound of the Bell expression as quantum theory. Also, by extending the real field to the quaternionic field, we can puzzle out the GHZ problem using local hidden variable model. Furthermore, we try to build a new type of hidden-variable theory of a single qubit based on the result.

Interferometric Computation Beyond Quantum Theory

NASA Astrophysics Data System (ADS)

Garner, Andrew J. P.

2018-03-01

There are quantum solutions for computational problems that make use of interference at some stage in the algorithm. These stages can be mapped into the physical setting of a single particle travelling through a many-armed interferometer. There has been recent foundational interest in theories beyond quantum theory. Here, we present a generalized formulation of computation in the context of a many-armed interferometer, and explore how theories can differ from quantum theory and still perform distributed calculations in this set-up. We shall see that quaternionic quantum theory proves a suitable candidate, whereas box-world does not. We also find that a classical hidden variable model first presented by Spekkens (Phys Rev A 75(3): 32100, 2007) can also be used for this type of computation due to the epistemic restriction placed on the hidden variable.

Evidence for Spin Singlet Pairing with Strong Uniaxial Anisotropy in URu2Si2 Using Nuclear Magnetic Resonance

NASA Astrophysics Data System (ADS)

Hattori, T.; Sakai, H.; Tokunaga, Y.; Kambe, S.; Matsuda, T. D.; Haga, Y.

2018-01-01

In order to identify the spin contribution to superconducting pairing compatible with the so-called "hidden order", Si 29 nuclear magnetic resonance measurements have been performed using a high-quality single crystal of URu2 Si2 . A clear reduction of the Si 29 Knight shift in the superconducting state has been observed under a magnetic field applied along the crystalline c axis, corresponding to the magnetic easy axis. These results provide direct evidence for the formation of spin-singlet Cooper pairs. Consequently, results indicating a very tiny change of the in-plane Knight shift reported previously demonstrate extreme uniaxial anisotropy for the spin susceptibility in the hidden order state.

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

Atwater, Harry A.; Leite, Marina S.; Warmann, Emily C.

A virtual substrate includes a handle support and a strain-relieved single crystalline layer on the handle support. A method of making the virtual substrate includes growing a coherently-strained single crystalline layer on an initial growth substrate, removing the initial growth substrate to relieve the strain on the single crystalline layer, and applying the strain-relieved single crystalline layer on a handle support.

Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant— Picrorhiza kurrooa

NASA Astrophysics Data System (ADS)

Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

2017-02-01

Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

Prediction of strain values in reinforcements and concrete of a RC frame using neural networks

NASA Astrophysics Data System (ADS)

Vafaei, Mohammadreza; Alih, Sophia C.; Shad, Hossein; Falah, Ali; Halim, Nur Hajarul Falahi Abdul

2018-03-01

The level of strain in structural elements is an important indicator for the presence of damage and its intensity. Considering this fact, often structural health monitoring systems employ strain gauges to measure strains in critical elements. However, because of their sensitivity to the magnetic fields, inadequate long-term durability especially in harsh environments, difficulties in installation on existing structures, and maintenance cost, installation of strain gauges is not always possible for all structural components. Therefore, a reliable method that can accurately estimate strain values in critical structural elements is necessary for damage identification. In this study, a full-scale test was conducted on a planar RC frame to investigate the capability of neural networks for predicting the strain values. Two neural networks each of which having a single hidden layer was trained to relate the measured rotations and vertical displacements of the frame to the strain values measured at different locations of the frame. Results of trained neural networks indicated that they accurately estimated the strain values both in reinforcements and concrete. In addition, the trained neural networks were capable of predicting strains for the unseen input data set.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

PubMed

Argelaguet, Ricard; Velten, Britta; Arnol, Damien; Dietrich, Sascha; Zenz, Thorsten; Marioni, John C; Buettner, Florian; Huber, Wolfgang; Stegle, Oliver

2018-06-20

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

PubMed

Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

2017-03-01

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

Multiscale unfolding of real networks by geometric renormalization

NASA Astrophysics Data System (ADS)

García-Pérez, Guillermo; Boguñá, Marián; Serrano, M. Ángeles

2018-06-01

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group by introducing correlations between coexisting scales. Here, we provide a framework for the investigation of complex networks at different resolutions. The approach is based on geometric representations, which have been shown to sustain network navigability and to reveal the mechanisms that govern network structure and evolution. We define a geometric renormalization group for networks by embedding them into an underlying hidden metric space. We find that real scale-free networks show geometric scaling under this renormalization group transformation. We unfold the networks in a self-similar multilayer shell that distinguishes the coexisting scales and their interactions. This in turn offers a basis for exploring critical phenomena and universality in complex networks. It also affords us immediate practical applications, including high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space, which betters those on single layers.

Directed Vertical Diffusion of Photovoltaic Active Layer Components into Porous ZnO-Based Cathode Buffer Layers.

PubMed

Kang, Jia-Jhen; Yang, Tsung-Yu; Lan, Yi-Kang; Wu, Wei-Ru; Su, Chun-Jen; Weng, Shih-Chang; Yamada, Norifumi L; Su, An-Chung; Jeng, U-Ser

2018-04-01

Cathode buffer layers (CBLs) can effectively further the efficiency of polymer solar cells (PSCs), after optimization of the active layer. Hidden between the active layer and cathode of the inverted PSC device configuration is the critical yet often unattended vertical diffusion of the active layer components across CBL. Here, a novel methodology of contrast variation with neutron and anomalous X-ray reflectivity to map the multicomponent depth compositions of inverted PSCs, covering from the active layer surface down to the bottom of the ZnO-based CBL, is developed. Uniquely revealed for a high-performance model PSC are the often overlooked porosity distributions of the ZnO-based CBL and the differential diffusions of the polymer PTB7-Th and fullerene derivative PC 71 BM of the active layer into the CBL. Interface modification of the ZnO-based CBL with fullerene derivative PCBEOH for size-selective nanochannels can selectively improve the diffusion of PC 71 BM more than that of the polymer. The deeper penetration of PC 71 BM establishes a gradient distribution of fullerene derivatives over the ZnO/PCBE-OH CBL, resulting in markedly improved electron mobility and device efficiency of the inverted PSC. The result suggests a new CBL design concept of progressive matching of the conduction bands. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Culture and Character Education in a Jewish Day School: A Case Study of Life and Experience

ERIC Educational Resources Information Center

Roso, Calvin G.

2013-01-01

This article addresses how to teach character comprehensively by studying ways a school's concurrent curricula (the official curriculum, the operational curriculum, the extra curriculum, and the hidden curriculum) can be used to teach character to students. A single case study analyzes the curriculum at a Jewish day school by examining school…

Exploring the Hidden Agenda in the Representation of Culture in International and Localised ELT Textbooks

ERIC Educational Resources Information Center

Tajeddin, Zia; Teimournezhad, Shohreh

2015-01-01

The rise of English as an international language (EIL) has challenged the focus on native-speaker culture in second language teaching and learning. Exposing learners to a single culture is no longer considered sufficient as intercultural language teaching and understanding gains momentum. The aim of this study was to investigate the representation…

The solid angle hidden in polyhedron gravitation formulations

NASA Astrophysics Data System (ADS)

Werner, Robert A.

2017-03-01

Formulas of a homogeneous polyhedron's gravitational potential typically include two arctangent terms for every edge of every face and a special term to eliminate a possible facial singularity. However, the arctangent and singularity terms are equivalent to the face's solid angle viewed from the field point. A face's solid angle can be evaluated with a single arctangent, saving computation.

Single layer multi-color luminescent display and method of making

NASA Technical Reports Server (NTRS)

Robertson, James B. (Inventor)

1992-01-01

The invention is a multi-color luminescent display comprising an insulator substrate and a single layer of host material, which may be a phosphor deposited thereon that hosts one or more different impurities, therein forming a pattern of selected and distinctly colored phosphors such as blue, green, and red phosphors in a single layer of host material. Transparent electrical conductor means may be provided for subjecting selected portions of the pattern of colored phosphors to an electric field, thereby forming a multi-color, single layer electroluminescent display. A method of forming a multi-color luminescent display includes the steps of depositing on an insulator substrate a single layer of host material, which itself may be a phosphor, with the properties to host varying quantities of different impurities and introducing one or more of said different impurities into selected areas of the said single layer of host material by thermal diffusion or ion implantation to form a pattern of phosphors of different colors in the said single layer of host material.

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Graviweak Unification, Invisible Universe and Dark Energy

NASA Astrophysics Data System (ADS)

Das, C. R.; Laperashvili, L. V.; Tureanu, A.

2013-07-01

We consider a graviweak unification model with the assumption of the existence of a hidden (invisible) sector of our Universe, parallel to the visible world. This Hidden World (HW) is assumed to be a Mirror World (MW) with broken mirror parity. We start with a diffeomorphism invariant theory of a gauge field valued in a Lie algebra g, which is broken spontaneously to the direct sum of the space-time Lorentz algebra and the Yang-Mills algebra: ˜ {g} = {{su}}(2) (grav)L ⊕ {{su}}(2)L — in the ordinary world, and ˜ {g}' = {{su}}(2){' (grav)}R ⊕ {{su}}(2)'R — in the hidden world. Using an extension of the Plebanski action for general relativity, we recover the actions for gravity, SU(2) Yang-Mills and Higgs fields in both (visible and invisible) sectors of the Universe, and also the total action. After symmetry breaking, all physical constants, including the Newton's constants, cosmological constants, Yang-Mills couplings, and other parameters, are determined by a single parameter g present in the initial action, and by the Higgs VEVs. The dark energy problem of this model predicts a too large supersymmetric breaking scale (MSUSY 1010GeV), which is not within the reach of the LHC experiments.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

A topological approach unveils system invariances and broken symmetries in the brain.

PubMed

Tozzi, Arturo; Peters, James F

2016-05-01

Symmetries are widespread invariances underscoring countless systems, including the brain. A symmetry break occurs when the symmetry is present at one level of observation but is hidden at another level. In such a general framework, a concept from algebraic topology, namely, the Borsuk-Ulam theorem (BUT), comes into play and sheds new light on the general mechanisms of nervous symmetries. The BUT tells us that we can find, on an n-dimensional sphere, a pair of opposite points that have the same encoding on an n - 1 sphere. This mapping makes it possible to describe both antipodal points with a single real-valued vector on a lower dimensional sphere. Here we argue that this topological approach is useful for the evaluation of hidden nervous symmetries. This means that symmetries can be found when evaluating the brain in a proper dimension, although they disappear (are hidden or broken) when we evaluate the same brain only one dimension lower. In conclusion, we provide a topological methodology for the evaluation of the most general features of brain activity, i.e., the symmetries, cast in a physical/biological fashion that has the potential to be operationalized. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Hybrid FEM-ANN Approach for Slope Instability Prediction

NASA Astrophysics Data System (ADS)

Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.

2016-09-01

Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

PubMed

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Density functional theory study of bulk and single-layer magnetic semiconductor CrPS4

NASA Astrophysics Data System (ADS)

Zhuang, Houlong L.; Zhou, Jia

2016-11-01

Searching for two-dimensional (2D) materials with multifunctionality is one of the main goals of current research in 2D materials. Magnetism and semiconducting are certainly two desirable functional properties for a single 2D material. In line with this goal, here we report a density functional theory (DFT) study of bulk and single-layer magnetic semiconductor CrPS4. We find that the ground-state magnetic structure of bulk CrPS4 exhibits the A-type antiferromagnetic ordering, which transforms to ferromagnetic (FM) ordering in single-layer CrPS4. The calculated formation energy and phonon spectrum confirm the stability of single-layer CrPS4. The band gaps of FM single-layer CrPS4 calculated with a hybrid density functional are within the visible-light range. We also study the effects of FM ordering on the optical absorption spectra and band alignments for water splitting, indicating that single-layer CrPS4 could be a potential photocatalyst. Our work opens up ample opportunities of energy-related applications of single-layer CrPS4.

Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.

PubMed

Aliabadi, Mohsen; Farhadian, Maryam; Darvishi, Ebrahim

2015-08-01

Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using artificial neural networks, this study aims to present an empirical model for the prediction of the hearing loss threshold among noise-exposed workers. Two hundred and ten workers employed in a steel factory were chosen, and their occupational exposure histories were collected. To determine the hearing loss threshold, the audiometric test was carried out using a calibrated audiometer. The personal noise exposure was also measured using a noise dosimeter in the workstations of workers. Finally, data obtained five variables, which can influence the hearing loss, were used for the development of the prediction model. Multilayer feed-forward neural networks with different structures were developed using MATLAB software. Neural network structures had one hidden layer with the number of neurons being approximately between 5 and 15 neurons. The best developed neural networks with one hidden layer and ten neurons could accurately predict the hearing loss threshold with RMSE = 2.6 dB and R(2) = 0.89. The results also confirmed that neural networks could provide more accurate predictions than multiple regressions. Since occupational hearing loss is frequently non-curable, results of accurate prediction can be used by occupational health experts to modify and improve noise exposure conditions.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Convective heat transfer and pressure drop of aqua based TiO2 nanofluids at different diameters of nanoparticles: Data analysis and modeling with artificial neural network

NASA Astrophysics Data System (ADS)

Hemmat Esfe, Mohammad; Nadooshan, Afshin Ahmadi; Arshi, Ali; Alirezaie, Ali

2018-03-01

In this study, experimental data related to the Nusselt number and pressure drop of aqueous nanofluids of Titania is modeled and estimated by using ANN with 2 hidden layers and 8 neurons in each layer. Also in this study the effect of various effective variables in the Nusselt number and pressure drop is surveyed. This study indicated that the neural network modeling has been able to model experimental data with great accuracy. The modeling regression coefficient for the data of Nusselt number and relative pressure drop is 99.94% and 99.97% respectively. Besides, it represented that the increment of the Reynolds number and concentration made the increment of Nusselt number and pressure drop of aqueous nanofluid.

Electron emitting device and method of making the same

DOEpatents

Olsen, Gregory Hammond; Martinelli, Ramon Ubaldo; Ettenberg, Michael

1977-04-19

A substrate of single crystalline gallium arsenide has on a surface thereof a layer of single crystalline indium gallium phosphide. A layer of single crystalline gallium arsenide is on the indium gallium phosphide layer and a work function reducing material is on the gallium arsenide layer. The substrate has an opening therethrough exposing a portion of the indium gallium phosphide layer.

Diffuse Reflectance Spectroscopy of Hidden Objects, Part I: Interpretation of the Reflection-Absorption-Scattering Fractions in Near-Infrared (NIR) Spectra of Polyethylene Films.

PubMed

Pomerantsev, Alexey L; Rodionova, Oxana Ye; Skvortsov, Alexej N

2017-08-01

Investigation of a sample covered by an interfering layer is required in many fields, e.g., for process control, biochemical analysis, and many other applications. This study is based on the analysis of spectra collected by near-infrared (NIR) diffuse reflectance spectroscopy. Each spectrum is a composition of a useful, target spectrum and a spectrum of an interfering layer. To recover the target spectrum, we suggest using a new phenomenological approach, which employs the multivariate curve resolution (MCR) method. In general terms, the problem is very complex. We start with a specific problem of analyzing a system, which consists of several layers of polyethylene (PE) film and underlayer samples with known spectral properties. To separate information originating from PE layers and the target, we modify the system versus both the number of the PE layers as well as the reflectance properties of the target sample. We consider that the interfering spectrum of the layer can be modeled using three components, which can be tentatively called transmission, absorption, and scattering contributions. The novelty of our approach is that we do not remove the reflectance and scattering effects from the spectra, but study them in detail aiming to use this information to recover the target spectrum.

Equivalence between contextuality and negativity of the Wigner function for qudits

NASA Astrophysics Data System (ADS)

Delfosse, Nicolas; Okay, Cihan; Bermejo-Vega, Juan; Browne, Dan E.; Raussendorf, Robert

2017-12-01

Understanding what distinguishes quantum mechanics from classical mechanics is crucial for quantum information processing applications. In this work, we consider two notions of non-classicality for quantum systems, negativity of the Wigner function and contextuality for Pauli measurements. We prove that these two notions are equivalent for multi-qudit systems with odd local dimension. For a single qudit, the equivalence breaks down. We show that there exist single qudit states that admit a non-contextual hidden variable model description and whose Wigner functions are negative.

Ultrathin rhodium nanosheets.

PubMed

Duan, Haohong; Yan, Ning; Yu, Rong; Chang, Chun-Ran; Zhou, Gang; Hu, Han-Shi; Rong, Hongpan; Niu, Zhiqiang; Mao, Junjie; Asakura, Hiroyuki; Tanaka, Tsunehiro; Dyson, Paul Joseph; Li, Jun; Li, Yadong

2014-01-01

Despite significant advances in the fabrication and applications of graphene-like materials, it remains a challenge to prepare single-layered metallic materials, which have great potential applications in physics, chemistry and material science. Here we report the fabrication of poly(vinylpyrrolidone)-supported single-layered rhodium nanosheets using a facile solvothermal method. Atomic force microscope shows that the thickness of a rhodium nanosheet is <4 Å. Electron diffraction and X-ray absorption spectroscopy measurements suggest that the rhodium nanosheets are composed of planar single-atom-layered sheets of rhodium. Density functional theory studies reveal that the single-layered Rh nanosheet involves a δ-bonding framework, which stabilizes the single-layered structure together with the poly(vinylpyrrolidone) ligands. The poly(vinylpyrrolidone)-supported single-layered rhodium nanosheet represents a class of metallic two-dimensional structures that might inspire further fundamental advances in physics, chemistry and material science.

Characterization of the guinea pig animal model and subsequent comparison of the behavioral effects of selective dopaminergic drugs and methamphetamine

PubMed Central

Lee, Kiera-Nicole; Pellom, Samuel T.; Oliver, Ericka; Chirwa, Sanika

2014-01-01

Though not commonly used in behavior tests guinea pigs may offer subtle behavior repertoires that better mimic human activity and warrant study. To test this, 31 Hartley guinea pigs (male, 200–250 g) were evaluated in PhenoTyper cages using the video-tracking EthoVision XT 7.0 software. Results showed that guinea pigs spent more time in the hidden zone (small box in corner of cage) than the food/water zone, or arena zone. Guinea pigs exhibited thigmotaxis (a wall following strategy) and were active throughout the light and dark phases. Eating and drinking occurred throughout the light and dark phases. An injection of 0.25 mg/kg SCH23390, the dopamine D1 receptors (D1R) antagonist, produced significant decreases in time spent in the hidden zone. There were insignificant changes in time spent in the hidden zone for guinea pigs treated with 7.5 mg SKF38393 (D1R agonist), 1.0 mg/kg sulpiride (D2R antagonist), and 1.0 or 10.0 mg/kg methamphetamine. Locomotor activity profiles were unchanged after injections of saline, SKF38393, SCH23390 and sulpiride. By contrast, a single injection or repeated administration for 7 days of low-dose methamphetamine induced transient hyperactivity but this declined to baseline levels over the 22-hour observation period. Guinea pigs treated with high-dose methamphetamine displayed sustained hyperactivity and travelled significantly greater distances over the circadian cycle. Subsequent 7-day treatment with high-dose methamphetamine induced motor sensitization and significant increases in total distances moved relative to single drug injections or saline controls. These results highlight the versatility and unique features of the guinea pig for studying brain-behavior interactions. PMID:24436154

A hidden Markov model approach to neuron firing patterns.

PubMed

Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

1996-11-01

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

PubMed

Imamverdiyev, Yadigar; Abdullayeva, Fargana

2018-06-01

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. The form of the RBM that allows application of the continuous data is used. In this type of RBM, the probability distribution of the visible layer is replaced by a Gaussian distribution. Comparative analysis of the accuracy of the proposed method with Bernoulli-Bernoulli RBM, Gaussian-Bernoulli RBM, deep belief network type deep learning methods on DoS attack detection is provided. Detection accuracy of the methods is verified on the NSL-KDD data set. Higher accuracy from the proposed multilayer deep Gaussian-Bernoulli type RBM is obtained.

Deep RNNs for video denoising

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Song, Li; Yang, Xiaokang

2016-09-01

Video denoising can be described as the problem of mapping from a specific length of noisy frames to clean one. We propose a deep architecture based on Recurrent Neural Network (RNN) for video denoising. The model learns a patch-based end-to-end mapping between the clean and noisy video sequences. It takes the corrupted video sequences as the input and outputs the clean one. Our deep network, which we refer to as deep Recurrent Neural Networks (deep RNNs or DRNNs), stacks RNN layers where each layer receives the hidden state of the previous layer as input. Experiment shows (i) the recurrent architecture through temporal domain extracts motion information and does favor to video denoising, and (ii) deep architecture have large enough capacity for expressing mapping relation between corrupted videos as input and clean videos as output, furthermore, (iii) the model has generality to learned different mappings from videos corrupted by different types of noise (e.g., Poisson-Gaussian noise). By training on large video databases, we are able to compete with some existing video denoising methods.

Artificial neural network intelligent method for prediction

NASA Astrophysics Data System (ADS)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

A deep belief network approach using VDRAS data for nowcasting

NASA Astrophysics Data System (ADS)

Han, Lei; Dai, Jie; Zhang, Wei; Zhang, Changjiang; Feng, Hanlei

2018-04-01

Nowcasting or very short-term forecasting convective storms is still a challenging problem due to the high nonlinearity and insufficient observation of convective weather. As the understanding of the physical mechanism of convective weather is also insufficient, the numerical weather model cannot predict convective storms well. Machine learning approaches provide a potential way to nowcast convective storms using various meteorological data. In this study, a deep belief network (DBN) is proposed to nowcast convective storms using the real-time re-analysis meteorological data. The nowcasting problem is formulated as a classification problem. The 3D meteorological variables are fed directly to the DBN with dimension of input layer 6*6*80. Three hidden layers are used in the DBN and the dimension of output layer is two. A box-moving method is presented to provide the input features containing the temporal and spatial information. The results show that the DNB can generate reasonable prediction results of the movement and growth of convective storms.

Hidden symmetries in N-layer dielectric stacks

NASA Astrophysics Data System (ADS)

Liu, Haihao; Shoufie Ukhtary, M.; Saito, Riichiro

2017-11-01

The optical properties of a multilayer system with arbitrary N layers of dielectric media are investigated. Each layer is one of two dielectric media, with a thickness one-quarter the wavelength of light in that medium, corresponding to a central frequency f 0. Using the transfer matrix method, the transmittance T is calculated for all possible 2 N sequences for small N. Unexpectedly, it is found that instead of 2 N different values of T at f 0 (T 0), there are only (N/2+1) discrete values of T 0, for even N, and (N + 1) for odd N. We explain this high degeneracy in T 0 values by finding symmetry operations on the sequences that do not change T 0. Analytical formulae were derived for the T 0 values and their degeneracies as functions of N and an integer parameter for each sequence we call ‘charge’. Additionally, the bandwidth at f 0 and filter response of the transmission spectra are investigated, revealing asymptotic behavior at large N.

Reproductive isolation and patterns of genetic differentiation in a cryptic butterfly species complex

PubMed Central

Dincâ, V; Wiklund, C; Lukhtanov, V A; Kodandaramaiah, U; Norén, K; Dapporto, L; Wahlberg, N; Vila, R; Friberg, M

2013-01-01

Molecular studies of natural populations are often designed to detect and categorize hidden layers of cryptic diversity, and an emerging pattern suggests that cryptic species are more common and more widely distributed than previously thought. However, these studies are often decoupled from ecological and behavioural studies of species divergence. Thus, the mechanisms by which the cryptic diversity is distributed and maintained across large spatial scales are often unknown. In 1988, it was discovered that the common Eurasian Wood White butterfly consisted of two species (Leptidea sinapis and Leptidea reali), and the pair became an emerging model for the study of speciation and chromosomal evolution. In 2011, the existence of a third cryptic species (Leptidea juvernica) was proposed. This unexpected discovery raises questions about the mechanisms preventing gene flow and about the potential existence of additional species hidden in the complex. Here, we compare patterns of genetic divergence across western Eurasia in an extensive data set of mitochondrial and nuclear DNA sequences with behavioural data on inter- and intraspecific reproductive isolation in courtship experiments. We show that three species exist in accordance with both the phylogenetic and biological species concepts and that additional hidden diversity is unlikely to occur in Europe. The Leptidea species are now the best studied cryptic complex of butterflies in Europe and a promising model system for understanding the formation of cryptic species and the roles of local processes, colonization patterns and heterospecific interactions for ecological and evolutionary divergence. PMID:23909947

Room-temperature ultrafast nonlinear spectroscopy of a single molecule

NASA Astrophysics Data System (ADS)

Liebel, Matz; Toninelli, Costanza; van Hulst, Niek F.

2018-01-01

Single-molecule spectroscopy aims to unveil often hidden but potentially very important contributions of single entities to a system's ensemble response. Albeit contributing tremendously to our ever growing understanding of molecular processes, the fundamental question of temporal evolution, or change, has thus far been inaccessible, thus painting a static picture of a dynamic world. Here, we finally resolve this dilemma by performing ultrafast time-resolved transient spectroscopy on a single molecule. By tracing the femtosecond evolution of excited electronic state spectra of single molecules over hundreds of nanometres of bandwidth at room temperature, we reveal their nonlinear ultrafast response in an effective three-pulse scheme with fluorescence detection. A first excitation pulse is followed by a phase-locked de-excitation pulse pair, providing spectral encoding with 25 fs temporal resolution. This experimental realization of true single-molecule transient spectroscopy demonstrates that two-dimensional electronic spectroscopy of single molecules is experimentally within reach.

Discovering the Science Hidden behind Real Objects

ERIC Educational Resources Information Center

Desforges, Ruth

2018-01-01

The Zoological Society of London (ZSL) has a huge collection of unique and curious objects from the natural world that have been loaned to us by HM Revenue and Customs after being seized at the UK border. Among the turtle shells and snake skins, the strangest of these is perhaps the freestanding rhino-foot ash tray. This single object can open up…

Decay detection in red oak trees using a combination of visual inspection, acoustic testing, and resistance microdrilling

Treesearch

Xiping Wang; R. Bruce Allison

2008-01-01

Arborists are often challenged to identify internal structural defects hidden from view within tree trunks. This article reports the results of a study using a trunk inspection protocol combining visual observation, single-path stress wave testing, acoustic tomography, and resistance microdrilling to detect internal defects. Two century-old red oak (Quercus rubra)...

"Ask Each Pupil About Her Methods of Cleaning": Ideologies of Language and Gender in Americanisation Instruction (1900-1924)

ERIC Educational Resources Information Center

Pavlenko, Aneta

2005-01-01

The focus of this paper is on the complex interaction between ideologies of language, gender and identity during the Americanisation era (1900-1924) in the USA. I will argue that the Americanisation movement had a "hidden curriculum" which singled out immigrant women--and in particular mothers--for specific kinds of English instruction.…

Extracting duration information in a picture category decoding task using hidden Markov Models

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

NASA Astrophysics Data System (ADS)

Abdelrahim, Mohamed Mahmoud Hosny

2001-11-01

In this research, an "Intelligent Imagery System Prototype" (IISP) was developed. IISP is an integration tool that facilitates the environment for active, direct, and on-the-fly usage of high resolution imagery, internally linked to hidden GIS vector layers, to query the real world phenomena and, consequently, to perform exploratory types of spatial analysis based on a clear/undisturbed image scene. The IISP was designed and implemented using the software components approach to verify the hypothesis that a fully rectified, partially rectified, or even unrectified digital image can be internally linked to a variety of different hidden vector databases/layers covering the end user area of interest, and consequently may be reliably used directly as a base for "on-the-fly" querying of real-world phenomena and for performing exploratory types of spatial analysis. Within IISP, differentially rectified, partially rectified (namely, IKONOS GEOCARTERRA(TM)), and unrectified imagery (namely, scanned aerial photographs and captured video frames) were investigated. The system was designed to handle four types of spatial functions, namely, pointing query, polygon/line-based image query, database query, and buffering. The system was developed using ESRI MapObjects 2.0a as the core spatial component within Visual Basic 6.0. When used to perform the pre-defined spatial queries using different combinations of image and vector data, the IISP provided the same results as those obtained by querying pre-processed vector layers even when the image used was not orthorectified and the vector layers had different parameters. In addition, the real-time pixel location orthorectification technique developed and presented within the IKONOS GEOCARTERRA(TM) case provided a horizontal accuracy (RMSE) of +/- 2.75 metres. This accuracy is very close to the accuracy level obtained when purchasing the orthorectified IKONOS PRECISION products (RMSE of +/- 1.9 metre). The latter cost approximately four times as much as the IKONOS GEOCARTERRA(TM) products. The developed IISP is a step closer towards the direct and active involvement of high-resolution remote sensing imagery in querying the real world and performing exploratory types of spatial analysis. (Abstract shortened by UMI.)

Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning.

PubMed

Gramatikov, Boris I

2017-04-27

Reliable detection of central fixation and eye alignment is essential in the diagnosis of amblyopia ("lazy eye"), which can lead to blindness. Our lab has developed and reported earlier a pediatric vision screener that performs scanning of the retina around the fovea and analyzes changes in the polarization state of light as the scan progresses. Depending on the direction of gaze and the instrument design, the screener produces several signal frequencies that can be utilized in the detection of central fixation. The objective of this study was to compare artificial neural networks with classical statistical methods, with respect to their ability to detect central fixation reliably. A classical feedforward, pattern recognition, two-layer neural network architecture was used, consisting of one hidden layer and one output layer. The network has four inputs, representing normalized spectral powers at four signal frequencies generated during retinal birefringence scanning. The hidden layer contains four neurons. The output suggests presence or absence of central fixation. Backpropagation was used to train the network, using the gradient descent algorithm and the cross-entropy error as the performance function. The network was trained, validated and tested on a set of controlled calibration data obtained from 600 measurements from ten eyes in a previous study, and was additionally tested on a clinical set of 78 eyes, independently diagnosed by an ophthalmologist. In the first part of this study, a neural network was designed around the calibration set. With a proper architecture and training, the network provided performance that was comparable to classical statistical methods, allowing perfect separation between the central and paracentral fixation data, with both the sensitivity and the specificity of the instrument being 100%. In the second part of the study, the neural network was applied to the clinical data. It allowed reliable separation between normal subjects and affected subjects, its accuracy again matching that of the statistical methods. With a proper choice of a neural network architecture and a good, uncontaminated training data set, the artificial neural network can be an efficient classification tool for detecting central fixation based on retinal birefringence scanning.

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database

PubMed Central

Avdagic, Zikrija; Purisevic, Elvir; Omanovic, Samir; Coralic, Zlatan

2009-01-01

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513. PMID:21347158

Predicting cloud-to-ground lightning with neural networks

NASA Technical Reports Server (NTRS)

Barnes, Arnold A., Jr.; Frankel, Donald; Draper, James Stark

1991-01-01

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consist of ground based field mill data, meteorological tower data, lightning location data, and radiosonde data. High values of the field mill data and rapid changes in the field mill data, offset in time, provide the forecasts or desired output values used to train the neural network through backpropagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed.

A comparison of polynomial approximations and artificial neural nets as response surfaces

NASA Technical Reports Server (NTRS)

Carpenter, William C.; Barthelemy, Jean-Francois M.

1992-01-01

Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net, and the number of designs needed to train an approximation is discussed.

Numerical studies of the topological Chern numbers in two dimensional electron system

NASA Astrophysics Data System (ADS)

Sheng, Donna

2004-03-01

I will report on the numerical results of the exact calculation of the topological Chern numbers in fractional and bilayer quantum Hall systems[1]. I will show that following the evolution of the Chern numbers as a function of the disorder strength and/or layer separations, various quantum phase transitions as well as the characteristic transport properties of the phases, can be determined. The hidden topological ordering in other two dimensional electron systems will also be discussed. 1. D. N. Sheng et. al., Phys. Rev. Lett. 90, 256802 (2003).

MITLL 2015 Language Recognition Evaluation System Description

DTIC Science & Technology

2016-01-27

912 8.18 qsl-rus Russian 2021 37.80 ara-ary Maghrebi 919 46.91 spa-car Carib. Spa. 194 30.59 ara-arz Egyptian 440 97.27 spa-eur Eur. Spa. 366 8.55...qsl-pol Polish 695 32.14 ara-arb MSA 912 8.18 qsl-rus Russian 2021 37.80 ara-ary Maghrebi 919 46.91 spa-car Carib. Spa. 194 30.59 ara-arz Egyptian ...BOTTLENECK I-VECTOR SYSTEM (BNF1) The Deep Neural Network architecture that we used for this system was composed of seven hidden layers. The sixth

Single-layer group IV-V and group V-IV-III-VI semiconductors: Structural stability, electronic structures, optical properties, and photocatalysis

NASA Astrophysics Data System (ADS)

Lin, Jia-He; Zhang, Hong; Cheng, Xin-Lu; Miyamoto, Yoshiyuki

2017-07-01

Recently, single-layer group III monochalcogenides have attracted both theoretical and experimental interest at their potential applications in photonic devices, electronic devices, and solar energy conversion. Excited by this, we theoretically design two kinds of highly stable single-layer group IV-V (IV =Si ,Ge , and Sn; V =N and P) and group V-IV-III-VI (IV =Si ,Ge , and Sn; V =N and P; III =Al ,Ga , and In; VI =O and S) compounds with the same structures with single-layer group III monochalcogenides via first-principles simulations. By using accurate hybrid functional and quasiparticle methods, we show the single-layer group IV-V and group V-IV-III-VI are indirect bandgap semiconductors with their bandgaps and band edge positions conforming to the criteria of photocatalysts for water splitting. By applying a biaxial strain on single-layer group IV-V, single-layer group IV nitrides show a potential on mechanical sensors due to their bandgaps showing an almost linear response for strain. Furthermore, our calculations show that both single-layer group IV-V and group V-IV-III-VI have absorption from the visible light region to far-ultraviolet region, especially for single-layer SiN-AlO and SnN-InO, which have strong absorption in the visible light region, resulting in excellent potential for solar energy conversion and visible light photocatalytic water splitting. Our research provides valuable insight for finding more potential functional two-dimensional semiconductors applied in optoelectronics, solar energy conversion, and photocatalytic water splitting.

Queer Youth in Family Therapy.

PubMed

Harvey, Rebecca G; Stone Fish, Linda

2015-09-01

Trends in popular belief about same-sex relationships have undergone noteworthy change in the United States over the last decade. Yet this change has been marked by stark polarizations and has occurred at varying rates depending upon regional, community, racial, religious, and individual family context. For queer youth and their families, this cultural transformation has broadened opportunities and created a new set of risks and vulnerabilities. At the same time, youth's increasingly open and playful gender fluidity and sexual identity is complicated by unique intersections of class, race, religion, and immigration. Effective family therapy with queer youth requires practitioner's and treatment models that are sensitive to those who bear the burden of multiple oppressions and the hidden resilience embedded in their layered identities. We present case examples of our model of family therapy which addresses refuge, supports difficult dialogs, and nurtures queerness by looking for hidden resilience in the unique intersections of queer youths' lives. These intersections provide transformational potential for youth, their families and even for family therapists as we are all nurtured and challenged to think more complexly about intersectionality, sexuality, and gender. © 2015 Family Process Institute.

A new algorithm to detect earthquakes outside the seismic network: preliminary results

NASA Astrophysics Data System (ADS)

Giudicepietro, Flora; Esposito, Antonietta Maria; Ricciolino, Patrizia

2017-04-01

In this text we are going to present a new technique for detecting earthquakes outside the seismic network, which are often the cause of fault of automatic analysis system. Our goal is to develop a robust method that provides the discrimination result as quickly as possible. We discriminate local earthquakes from regional earthquakes, both recorded at SGG station, equipped with short period sensors, operated by Osservatorio Vesuviano (INGV) in the Southern Apennines (Italy). The technique uses a Multi Layer Perceptron (MLP) neural network with an architecture composed by an input layer, a hidden layer and a single node output layer. We pre-processed the data using the Linear Predictive Coding (LPC) technique to extract the spectral features of the signals in a compact form. We performed several experiments by shortening the signal window length. In particular, we used windows of 4, 2 and 1 seconds containing the onset of the local and the regional earthquakes. We used a dataset of 103 local earthquakes and 79 regional earthquakes, most of which occurred in Greece, Albania and Crete. We split the dataset into a training set, for the network training, and a testing set to evaluate the network's capacity of discrimination. In order to assess the network stability, we repeated this procedure six times, randomly changing the data composition of the training and testing set and the initial weights of the net. We estimated the performance of this method by calculating the average of correct detection percentages obtained for each of the six permutations. The average performances are 99.02%, 98.04% and 98.53%, which concern respectively the experiments carried out on 4, 2 and 1 seconds signal windows. The results show that our method is able to recognize the earthquakes outside the seismic network using only the first second of the seismic records, with a suitable percentage of correct detection. Therefore, this algorithm can be profitably used to make earthquake automatic analyses more robust and reliable. Finally, with appropriate tuning, it can be integrated in multi-parametric systems for monitoring high natural risk areas.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Two-step fabrication of single-layer rectangular SnSe flakes

NASA Astrophysics Data System (ADS)

Jiang, Jizhou; Wong, Calvin Pei Yu; Zou, Jing; Li, Shisheng; Wang, Qixing; Chen, Jianyi; Qi, Dianyu; Wang, Hongyu; Eda, Goki; Chua, Daniel H. C.; Shi, Yumeng; Zhang, Wenjing; Thye Shen Wee, Andrew

2017-06-01

Recent findings about ultrahigh thermoelectric performances in SnSe single crystals have stimulated research on this binary semiconductor material. Furthermore, single-layer SnSe is an interesting analogue of phosphorene, with potential applications in two-dimensional (2D) nanoelectronics. Although significant advances in the synthesis of SnSe nanocrystals have been made, fabrication of well-defined large-sized single-layer SnSe flakes in a facile way still remains a challenge. The growth of single-layer rectangular SnSe flakes with a thickness of ~6.8 Å and lateral dimensions of about 30 µm  ×  50 µm is demonstrated by a two-step synthesis method, where bulk rectangular SnSe flakes were synthesized first by a vapor transport deposition method followed by a nitrogen etching technique to fabricate single-layer rectangular SnSe flakes in an atmospheric pressure system. The as-obtained rectangular SnSe flakes exhibited a pure crystalline phase oriented along the a-axis direction. Field-effect transistor devices fabricated on individual single-layer rectangular SnSe flakes using gold electrodes exhibited p-doped ambipolar behavior and a hole mobility of about 0.16 cm2 V-1 s-1. This two-step fabrication method can be helpful for growing other similar 2D large-sized single-layer materials.

[Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

PubMed

Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

2015-10-01

To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

Estimating wheat and maize daily evapotranspiration using artificial neural network

NASA Astrophysics Data System (ADS)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

Application of artificial neural network to predict clay sensitivity in a high landslide prone area using CPTu data- A case study in Southwest of Sweden

NASA Astrophysics Data System (ADS)

Shahri, Abbas; Mousavinaseri, Mahsasadat; Naderi, Shima; Espersson, Maria

2015-04-01

Application of Artificial Neural Networks (ANNs) in many areas of engineering, in particular to geotechnical engineering problems such as site characterization has demonstrated some degree of success. The present paper aims to evaluate the feasibility of several various types of ANN models to predict the clay sensitivity of soft clays form piezocone penetration test data (CPTu). To get the aim, a research database of CPTu data of 70 test points around the Göta River near the Lilli Edet in the southwest of Sweden which is a high prone land slide area were collected and considered as input for ANNs. For training algorithms the quick propagation, conjugate gradient descent, quasi-Newton, limited memory quasi-Newton and Levenberg-Marquardt were developed tested and trained using the CPTu data to provide a comparison between the results of field investigation and ANN models to estimate the clay sensitivity. The reason of using the clay sensitivity parameter in this study is due to its relation to landslides in Sweden.A special high sensitive clay namely quick clay is considered as the main responsible for experienced landslides in Sweden which has high sensitivity and prone to slide. The training and testing program was started with 3-2-1 ANN architecture structure. By testing and trying several various architecture structures and changing the hidden layer in order to have a higher output resolution the 3-4-4-3-1 architecture structure for ANN in this study was confirmed. The tested algorithm showed that increasing the hidden layers up to 4 layers in ANN can improve the results and the 3-4-4-3-1 architecture structure ANNs for prediction of clay sensitivity represent reliable and reasonable response. The obtained results showed that the conjugate gradient descent algorithm with R2=0.897 has the best performance among the tested algorithms. Keywords: clay sensitivity, landslide, Artificial Neural Network

Realization of Chinese word segmentation based on deep learning method

NASA Astrophysics Data System (ADS)

Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

2017-08-01

In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

Radial basis function network learns ceramic processing and predicts related strength and density

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.; Baaklini, George Y.; Vary, Alex; Tjia, Robert E.

1993-01-01

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

Growth of multilayered polycrystalline reaction rims in the MgO-SiO2 system, part I: experiments

NASA Astrophysics Data System (ADS)

Gardés, E.; Wunder, B.; Wirth, R.; Heinrich, W.

2011-01-01

Growth of transport-controlled reaction layers between single crystals of periclase and quartz, and forsterite and quartz was investigated experimentally at 1.5 GPa, 1100°C to 1400°C, 5 min to 72 h under dry and melt-free conditions using a piston-cylinder apparatus. Starting assemblies consisting of Per | Qtz | Fo sandwiches produced polycrystalline double layers of forsterite and enstatite between periclase and quartz, and enstatite single layers between forsterite and quartz. The position of inert Pt-markers initially deposited at the interface of the reactants and inspection of mass balance confirmed that both layer-producing reactions are controlled by MgO diffusion, while SiO2 is relatively immobile. BSE and TEM imaging revealed thicknesses from 0.6 μm to 14 μm for double layers and from 0 to 6.8 μm for single layers. Both single and double layers displayed non-parabolic growth together with pronounced grain coarsening. Textural evolution and growth rates for each reaction are directly comparable. Forsterite-enstatite double layers are always wider than enstatite single layers, and the growth of enstatite in the double layer is slower than that in the single layer. In double layers, the enstatite/forsterite layer thickness ratio significantly increases with temperature, reflecting different MgO mobilities as temperature varies. Thus, thickness ratios in multilayered reaction zones may contain a record of temperature, but also that of any physico-chemical parameter that modifies the mobilities of the chemical components between the various layers. This potential is largely unexplored in geologically relevant systems, which calls for further experimental studies of multilayered reaction zones.

Single-unit-cell layer established Bi 2 WO 6 3D hierarchical architectures: Efficient adsorption, photocatalysis and dye-sensitized photoelectrochemical performance

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

Huang, Hongwei; Cao, Ranran; Yu, Shixin

Single-layer catalysis sparks huge interests and gains widespread attention owing to its high activity. Simultaneously, three-dimensional (3D) hierarchical structure can afford large surface area and abundant reactive sites, contributing to high efficiency. Herein, we report an absorbing single-unit-cell layer established Bi2WO6 3D hierarchical architecture fabricated by a sodium dodecyl benzene sulfonate (SDBS)-assisted assembled strategy. The DBS- long chains can adsorb on the (Bi2O2)2+ layers and hence impede stacking of the layers, resulting in the single-unit-cell layer. We also uncovered that SDS with a shorter chain is less effective than SDBS. Due to the sufficient exposure of surface O atoms, single-unit-cellmore » layer 3D Bi2WO6 shows strong selectivity for adsorption on multiform organic dyes with different charges. Remarkably, the single-unit-cell layer 3D Bi2WO6 casts profoundly enhanced photodegradation activity and especially a superior photocatalytic H2 evolution rate, which is 14-fold increase in contrast to the bulk Bi2WO6. Systematic photoelectrochemical characterizations disclose that the substantially elevated carrier density and charge separation efficiency take responsibility for the strengthened photocatalytic performance. Additionally, the possibility of single-unit-cell layer 3D Bi2WO6 as dye-sensitized solar cells (DSSC) has also been attempted and it was manifested to be a promising dye-sensitized photoanode for oxygen evolution reaction (ORR). Our work not only furnish an insight into designing single-layer assembled 3D hierarchical architecture, but also offer a multi-functional material for environmental and energy applications.« less

Diverse and tunable electronic structures of single-layer metal phosphorus trichalcogenides for photocatalytic water splitting

NASA Astrophysics Data System (ADS)

Liu, Jian; Li, Xi-Bo; Wang, Da; Lau, Woon-Ming; Peng, Ping; Liu, Li-Min

2014-02-01

The family of bulk metal phosphorus trichalcogenides (APX3, A = MII, M_{0.5}^IM_{0.5}^{III}; X = S, Se; MI, MII, and MIII represent Group-I, Group-II, and Group-III metals, respectively) has attracted great attentions because such materials not only own magnetic and ferroelectric properties, but also exhibit excellent properties in hydrogen storage and lithium battery because of the layered structures. Many layered materials have been exfoliated into two-dimensional (2D) materials, and they show distinct electronic properties compared with their bulks. Here we present a systematical study of single-layer metal phosphorus trichalcogenides by density functional theory calculations. The results show that the single layer metal phosphorus trichalcogenides have very low formation energies, which indicates that the exfoliation of single layer APX3 should not be difficult. The family of single layer metal phosphorus trichalcogenides exhibits a large range of band gaps from 1.77 to 3.94 eV, and the electronic structures are greatly affected by the metal or the chalcogenide atoms. The calculated band edges of metal phosphorus trichalcogenides further reveal that single-layer ZnPSe3, CdPSe3, Ag0.5Sc0.5PSe3, and Ag0.5In0.5PX3 (X = S and Se) have both suitable band gaps for visible-light driving and sufficient over-potentials for water splitting. More fascinatingly, single-layer Ag0.5Sc0.5PSe3 is a direct band gap semiconductor, and the calculated optical absorption further convinces that such materials own outstanding properties for light absorption. Such results demonstrate that the single layer metal phosphorus trichalcogenides own high stability, versatile electronic properties, and high optical absorption, thus such materials have great chances to be high efficient photocatalysts for water-splitting.

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.

Confirmation of theoretical colour predictions for layering dental composite materials.

PubMed

Mikhail, Sarah S; Johnston, William M

2014-04-01

The aim of this study is to confirm the theoretical colour predictions for single and double layers of dental composite materials on an opaque backing. Single and double layers of composite resins were fabricated, placed in optical contact with a grey backing and measured for spectral radiance. The spectral reflectance and colour were directly determined. Absorption and scattering coefficients as previously reported, the measured thickness of the single layers and the effective reflectance of the grey backing were utilized to theoretically predict the reflectance of the single layer using corrected Kubelka-Munk reflectance theory. For double layers the predicted effective reflectance of the single layer was used as the reflectance of the backing of the second layer and the thickness of the second layer was used to predict the reflectance of the double layer. Colour differences, using both the CIELAB and CIEDE2000 formulae, measured the discrepancy between each directly determined colour and its corresponding theoretical colour. The colour difference discrepancies generally ranged around the perceptibility threshold but were consistently below the respective acceptability threshold. This theory can predict the colour of layers of composite resin within acceptability limits and generally also within perceptibility limits. This theory could therefore be incorporated into computer-based optical measuring instruments that can automate the shade selections for layers of a more opaque first layer under a more translucent second layer for those clinical situations where an underlying background colour and a desirable final colour can be measured. Copyright © 2014 Elsevier Ltd. All rights reserved.

The effect of the hole injection layer on the performance of single layer organic light-emitting diodes

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

Wenjin, Zeng; Ran, Bi; Hongmei, Zhang, E-mail: iamhmzhang@njupt.edu.cn, E-mail: iamwhuang@njupt.edu.cn

2014-12-14

Efficient single-layer organic light-emitting diodes (OLEDs) were reported based on a green fluorescent dye 10-(2-benzothiazolyl)-2,3,6,7-tetrahydro-1,1,7,7–tetramethyl-1H,5H,11H-(1) benzopyropyrano (6,7-8-I,j)quinolizin-11-one (C545T). Herein, poly(3,4-ethylenedioxy thiophene) poly(styrene sulfonate) were, respectively, applied as the injection layer for comparison. The hole transport properties of the emission layer with different hole injection materials are well investigated via current-voltage measurement. It was clearly found that the hole injection layers (HILs) play an important role in the adjustment of the electron/hole injection to attain transport balance of charge carriers in the single emission layer of OLEDs with electron-transporting host. The layer of tris-(8-hydroxyquinoline) aluminum played a dual role of hostmore » and electron-transporting materials within the emission layer. Therefore, appropriate selection of hole injection layer is a key factor to achieve high efficiency OLEDs with single emission layer.« less

Computational discovery of ferromagnetic semiconducting single-layer CrSnTe 3

DOE PAGES

Zhuang, Houlong L.; Xie, Yu; Kent, P. R. C.; ...

2015-07-06

Despite many single-layer materials being reported in the past decade, few of them exhibit magnetism. Here we perform first-principles calculations using accurate hybrid density functional methods (HSE06) to predict that single-layer CrSnTe 3 (CST) is a ferromagnetic semiconductor, with band gaps of 0.9 and 1.2 eV for the majority and minority spin channels, respectively. We determine the Curie temperature as 170 K, significantly higher than that of single-layer CrSiTe 3 (90K) and CrGeTe 3 (130 K). This is due to the enhanced ionicity of the Sn-Te bond, which in turn increases the superexchange coupling between the magnetic Cr atoms. Wemore » further explore the mechanical and dynamical stability and strain response of this single-layer material for possible epitaxial growth. Lastly, our study provides an intuitive approach to understand and design novel single-layer magnetic semiconductors for a wide range of spintronics and energy applications.« less

Tunable phase transition in single-layer TiSe2 via electric field

NASA Astrophysics Data System (ADS)

Liu, Lei; Zhuang, Houlong L.

2018-06-01

Phase transition represents an intriguing physical phenomenon that exists in a number of single-layer transition-metal dichalcogenides. This phenomenon often occurs below a critical temperature and breaks the long-range crystalline order leading to a reconstructed superstructure called the charge-density wave (CDW) structure, which can therefore be recovered by external stimuli such as temperature. Alternatively, we show here that another external stimulation, electric field can also result in the phase transition between the regular and CDW structures of a single-layer transition-metal dichalcogenide. We used single-layer TiSe2 as an example to elucidate the mechanism of the CDW followed by calculations of the electronic structure using a hybrid density functional. We found that applying electric field can tune the phase transition between the 1T and CDW phases of single-layer TiSe2. Our work opens up a route of tuning the phase transition of single-layer materials via electric field.

Trigonocranus emmeae Fieber, 1876 (Hemiptera, Fulgoromorpha, Cixiidae) – a new species for Poland

PubMed Central

Musik, Krzysztof; Walczak, Marcin; Depa, Łukasz; Łukasz Junkiert; Anna Jedynowicz

2013-01-01

Abstract Single macropterous female of Trigonocranus emmeae Fieber, 1876 has been found during the faunistic studies in semi-natural plant communities of Oświęcim city in southern Poland. It is the first record of this species in Poland. Trigonocranus emmeae is rarely collected within the wide range of its distribution, mostly due to its hidden life mode. PMID:24039522

Hidden momentum of electrons, nuclei, atoms, and molecules

NASA Astrophysics Data System (ADS)

Cameron, Robert P.; Cotter, J. P.

2018-04-01

We consider the positions and velocities of electrons and spinning nuclei and demonstrate that these particles harbour hidden momentum when located in an electromagnetic field. This hidden momentum is present in all atoms and molecules, however it is ultimately canceled by the momentum of the electromagnetic field. We point out that an electron vortex in an electric field might harbour a comparatively large hidden momentum and recognize the phenomenon of hidden hidden momentum.

Multilayer Nanoporous Graphene Membranes for Water Desalination.

PubMed

Cohen-Tanugi, David; Lin, Li-Chiang; Grossman, Jeffrey C

2016-02-10

While single-layer nanoporous graphene (NPG) has shown promise as a reverse osmosis (RO) desalination membrane, multilayer graphene membranes can be synthesized more economically than the single-layer material. In this work, we build upon the knowledge gained to date toward single-layer graphene to explore how multilayer NPG might serve as a RO membrane in water desalination using classical molecular dynamic simulations. We show that, while multilayer NPG exhibits similarly promising desalination properties to single-layer membranes, their separation performance can be designed by manipulating various configurational variables in the multilayer case. This work establishes an atomic-level understanding of the effects of additional NPG layers, layer separation, and pore alignment on desalination performance, providing useful guidelines for the design of multilayer NPG membranes.

Single layers and multilayers of GaN and AlN in square-octagon structure: Stability, electronic properties, and functionalization

NASA Astrophysics Data System (ADS)

Gürbüz, E.; Cahangirov, S.; Durgun, E.; Ciraci, S.

2017-11-01

Further to planar single-layer hexagonal structures, GaN and AlN can also form free-standing, single-layer structures constructed from squares and octagons. We performed an extensive analysis of dynamical and thermal stability of these structures in terms of ab initio finite-temperature molecular dynamics and phonon calculations together with the analysis of Raman and infrared active modes. These single-layer square-octagon structures of GaN and AlN display directional mechanical properties and have wide, indirect fundamental band gaps, which are smaller than their hexagonal counterparts. These density functional theory band gaps, however, increase and become wider upon correction. Under uniaxial and biaxial tensile strain, the fundamental band gaps decrease and can be closed. The electronic and magnetic properties of these single-layer structures can be modified by adsorption of various adatoms, or by creating neutral cation-anion vacancies. The single-layer structures attain magnetic moment by selected adatoms and neutral vacancies. In particular, localized gap states are strongly dependent on the type of vacancy. The energetics, binding, and resulting electronic structure of bilayer, trilayer, and three-dimensional (3D) layered structures constructed by stacking the single layers are affected by vertical chemical bonds between adjacent layers. In addition to van der Waals interaction, these weak vertical bonds induce buckling in planar geometry and enhance their binding, leading to the formation of stable 3D layered structures. In this respect, these multilayers are intermediate between van der Waals solids and wurtzite crystals, offering a wide range of tunability.

Epistemic View of Quantum States and Communication Complexity of Quantum Channels

NASA Astrophysics Data System (ADS)

Montina, Alberto

2012-09-01

The communication complexity of a quantum channel is the minimal amount of classical communication required for classically simulating a process of state preparation, transmission through the channel and subsequent measurement. It establishes a limit on the power of quantum communication in terms of classical resources. We show that classical simulations employing a finite amount of communication can be derived from a special class of hidden variable theories where quantum states represent statistical knowledge about the classical state and not an element of reality. This special class has attracted strong interest very recently. The communication cost of each derived simulation is given by the mutual information between the quantum state and the classical state of the parent hidden variable theory. Finally, we find that the communication complexity for single qubits is smaller than 1.28 bits. The previous known upper bound was 1.85 bits.

Object permanence in cats (Felis catus): an ecological approach to the study of invisible displacements.

PubMed

Dumas, C

1992-12-01

A single invisible displacement object permanence task was administered to 19 cats (Felis catus). In this task, cats watched a target object from behind a transparent panel. However, cats had to walk around an opaque panel to reach the object. While cats were behind the opaque panel, the object was hidden behind one of two screens. As cats did not perceive the disappearance of the object behind the target screen, the object was invisibly hidden. Results showed that cats solved this task with great flexibility, which markedly contrasts with what has been observed in previous research. The discussion emphasizes the difference between the typical Piagetian task in which the information necessary to succeed must be dealt with in retrospective way, whereas in our task cats had to anticipate a new position of the object. The ecological relevance of this new task is also discussed.

A hidden Markov model approach to neuron firing patterns.

PubMed Central

Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

1996-01-01

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing. Images FIGURE 3 PMID:8913581

Hidden transition in multiferroic and magnetodielectric CuCrO2 evidenced by ac-susceptibility

NASA Astrophysics Data System (ADS)

Shukla, Kaushak K.; Pal, Arkadeb; Singh, Abhishek; Singh, Rahul; Saha, J.; Sinha, A. K.; Ghosh, A. K.; Patnaik, S.; Awasthi, A. M.; Chatterjee, Sandip

2017-04-01

Ferroelectric polarization, magnetic-field dependence of the dielectric constant and ac and dc magnetizations of frustrated CuCrO2 have been measured. A new spin freezing transition below 32 K is observed which is thermally driven. The nature of the spin freezing is to be a single-ion process. Dilution by the replacements of Cr ions by magnetic Mn ions showed suppression of the spin freezing transition suggesting it to be fundamentally a single-ion freezing process. The observed freezing, which is seemingly associated to geometrical spin frustration, represents a novel form of magnetic glassy behavior.

Estimating rate constants from single ion channel currents when the initial distribution is known.

PubMed

The, Yu-Kai; Fernandez, Jacqueline; Popa, M Oana; Lerche, Holger; Timmer, Jens

2005-06-01

Single ion channel currents can be analysed by hidden or aggregated Markov models. A classical result from Fredkin et al. (Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, vol I, pp 269-289, 1985) states that the maximum number of identifiable parameters is bounded by 2n(o)n(c), where n(o) and n(c) denote the number of open and closed states, respectively. We show that this bound can be overcome when the probabilities of the initial distribution are known and the data consist of several sweeps.

Bayesian model selection applied to artificial neural networks used for water resources modeling

NASA Astrophysics Data System (ADS)

Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

2008-04-01

Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Improving the light-emitting properties of single-layered polyfluorene light-emitting devices by simple ionic liquid blending

NASA Astrophysics Data System (ADS)

Horike, Shohei; Nagaki, Hiroto; Misaki, Masahiro; Koshiba, Yasuko; Morimoto, Masahiro; Fukushima, Tatsuya; Ishida, Kenji

2018-03-01

This paper describes an evaluation of ionic liquids (ILs) as potential electrolytes for single-layered light-emitting devices with good emission performance. As optoelectronic devices continue to grow in abundance, high-performance light-emitting devices with a single emission layer are becoming increasingly important for low-cost production. We show that a simple technique of osmosing IL into the polymer layer can result in high luminous efficiency and good response times of single-layered light-emitting polymers, even without the additional stacking of charge carrier injection and transport layers. The IL contributions to the light-emission of the polymer are discussed from the perspectives of energy diagrams and of the electric double layers on the electrodes. Our findings enable a faster, cheaper, and lower-in-waste production of light-emitting devices.

A 19-Month Climatology of Marine Aerosol-Cloud-Radiation Properties Derived From DOE ARM AMF Deployment at the Azores: Part I: Cloud Fraction and Single-Layered MBL Cloud Properties

NASA Technical Reports Server (NTRS)

Dong, Xiquan; Xi, Baike; Kennedy, Aaron; Minnis, Patrick; Wood, Robert

2013-01-01

A 19-month record of total, and single-layered low (0-3 km), middle (3-6 km), and high (> 6 km) cloud fractions (CFs), and the single-layered marine boundary layer (MBL) cloud macrophysical and microphysical properties has been generated from ground-based measurements taken at the ARM Azores site between June 2009 and December 2010. It documents the most comprehensive and longest dataset on marine cloud fraction and MBL cloud properties to date. The annual means of total CF, and single-layered low, middle, and high CFs derived from ARM radar-lidar observations are 0.702, 0.271, 0.01 and 0.106, respectively. More total and single-layered high CFs occurred during winter, while single-layered low CFs were greatest during summer. The diurnal cycles for both total and low CFs are stronger during summer than during winter. The CFs are bimodally distributed in the vertical with a lower peak at approx. 1 km and higher one between 8 and 11 km during all seasons, except summer, when only the low peak occurs. The persistent high pressure and dry conditions produce more single-layered MBL clouds and fewer total clouds during summer, while the low pressure and moist air masses during winter generate more total and multilayered-clouds, and deep frontal clouds associated with midlatitude cyclones.

High gain durable anti-reflective coating with oblate voids

DOEpatents

Maghsoodi, Sina; Brophy, Brenor L.; Colson, Thomas E.; Gonsalves, Peter R.; Abrams, Ze'ev

2016-06-28

Disclosed herein are single layer transparent coatings with an anti-reflective property, a hydrophobic property, and that are highly abrasion resistant. The single layer transparent coatings contain a plurality of oblate voids. At least 1% of the oblate voids are open to a surface of the single layer transparent coatings.

Direct visualization of a two-dimensional topological insulator in the single-layer 1 T'-WT e2

NASA Astrophysics Data System (ADS)

Jia, Zhen-Yu; Song, Ye-Heng; Li, Xiang-Bing; Ran, Kejing; Lu, Pengchao; Zheng, Hui-Jun; Zhu, Xin-Yang; Shi, Zhi-Qiang; Sun, Jian; Wen, Jinsheng; Xing, Dingyu; Li, Shao-Chun

2017-07-01

We have grown nearly freestanding single-layer 1 T'-WT e2 on graphitized 6 H -SiC(0001) by using molecular beam epitaxy (MBE), and characterized its electronic structure with scanning tunneling microscopy/spectroscopy (STM/STS). The existence of topological edge states at the periphery of single-layer WT e2 islands was confirmed. Surprisingly, a bulk band gap at the Fermi level and insulating behaviors were also found in single-layer WT e2 at low temperature, which are likely associated with an incommensurate charge order transition. The realization of two-dimensional topological insulators (2D TIs) in single-layer transition-metal dichalcogenide provides a promising platform for further exploration of the 2D TIs' physics and related applications.

Research on subsurface deformed layer in ultra-precision cutting of single crystal copper by focused ion beam etching method

NASA Astrophysics Data System (ADS)

Chen, Y.; Huang, X. J.; Kong, J. X.

2018-03-01

In this paper, the focused ion beam was used to study the subsurface deformed layer of single crystal copper caused by the nanoscale single-point diamond fly cutting, and the possibility of using nanometer ultra-precision cutting to remove the larger deformation layer caused by traditional rough cutting process was explored. The maximum cutting thickness of single-point diamond cutting was about 146 nm, and the surface of the single-crystal copper after cutting was etched and observed by using the focused ion beam method. It was found that the morphology of the near-surface layer and the intermediate layer of the copper material were larger differences: the near-surface of the material was smaller and more compact, and the intermediate material layer of the material was more coarse sparse. The results showed that the traditional precision cutting would residual significant subsurface deformed layer and the thickness was on micron level. Even more, the subsurface deformed layer was obviously removed from about 12μm to 5μm after single-point diamond fly cutting in this paper. This paper proved that the large-scale subsurface deformed layer caused by traditional cutting process could be removed by nanometer ultra-precision cutting. It was of great significance to further establish the method that control of the deformation of weak rigid components by reducing the depth of the subsurface deformed layers.

[Experimental model for the examination of inner pressure tolerance of telescopic anastomosis and other frequently performed anastomosis types of the esophagus].

PubMed

Szúcs, G; Tóth, I; Bráth, E; Gyáni, K; Miko, I

2001-08-01

We have good results with telescopic anastomosis technique in partial oesophagectomies and gastrectomies. As we could not find data about the healing process of telescopic anastomoses so we started experimenting. Inside pressure tolerance was examined immediately after performing anastomoses by measuring the bursting pressure using the organs of pigs slaughtered in the meat industry. Both oesophago-gastrostomies and oesophago-jejunostomies were performed with telescopic, single layer interrupted, single layer continuous, double layer interrupted and double layer continuous-interrupted technique, 9 of each anastomosis. A series of oesophago-jejunostomies were performed with EEA stapler. 99 anastomoses of 11 types were investigated. We found, that the inner pressure tolerance of telescopic oesophago-gastrostomy is better than any other single layer type variant. On the other hand the double layer type variants have much better pressure tolerance than the telescopic and other two type single layer anastomoses. The difference is statistically significant. In oesophago-jejunostomies the pressure tolerance of telescopic anastomosis is better than of the single layer interrupted type but the difference between the telescopic and single layer continuous type anastomoses is not significant. The pressure tolerance of double layer anastomosis is higher than the telescopic one but the difference is significant only in the continuous-interrupted type. The inner pressure tolerance of telescopic and EEA stapler anastomoses are equal. The investigation of additional features in anastomosis healing is in progress.

Real-Time Adaptive Color Segmentation by Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2004-01-01

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Single layer of Ge quantum dots in HfO2 for floating gate memory capacitors.

PubMed

Lepadatu, A M; Palade, C; Slav, A; Maraloiu, A V; Lazanu, S; Stoica, T; Logofatu, C; Teodorescu, V S; Ciurea, M L

2017-04-28

High performance trilayer memory capacitors with a floating gate of a single layer of Ge quantum dots (QDs) in HfO 2 were fabricated using magnetron sputtering followed by rapid thermal annealing (RTA). The layer sequence of the capacitors is gate HfO 2 /floating gate of single layer of Ge QDs in HfO 2 /tunnel HfO 2 /p-Si wafers. Both Ge and HfO 2 are nanostructured by RTA at moderate temperatures of 600-700 °C. By nanostructuring at 600 °C, the formation of a single layer of well separated Ge QDs with diameters of 2-3 nm at a density of 4-5 × 10 15 m -2 is achieved in the floating gate (intermediate layer). The Ge QDs inside the intermediate layer are arranged in a single layer and are separated from each other by HfO 2 nanocrystals (NCs) about 8 nm in diameter with a tetragonal/orthorhombic structure. The Ge QDs in the single layer are located at the crossing of the HfO 2 NCs boundaries. In the intermediate layer, besides Ge QDs, a part of the Ge atoms is segregated by RTA at the HfO 2 NCs boundaries, while another part of the Ge atoms is present inside the HfO 2 lattice stabilizing the tetragonal/orthorhombic structure. The fabricated capacitors show a memory window of 3.8 ± 0.5 V and a capacitance-time characteristic with 14% capacitance decay in the first 3000-4000 s followed by a very slow capacitance decrease extrapolated to 50% after 10 years. This high performance is mainly due to the floating gate of a single layer of well separated Ge QDs in HfO 2 , distanced from the Si substrate by the tunnel oxide layer with a precise thickness.

Unravelling and controlling hidden imprint fields in ferroelectric capacitors

PubMed Central

Liu, Fanmao; Fina, Ignasi; Bertacco, Riccardo; Fontcuberta, Josep

2016-01-01

Ferroelectric materials have a spontaneous polarization that can point along energetically equivalent, opposite directions. However, when ferroelectric layers are sandwiched between different metallic electrodes, asymmetric electrostatic boundary conditions may induce the appearance of an electric field (imprint field, Eimp) that breaks the degeneracy of the polarization directions, favouring one of them. This has dramatic consequences on functionality of ferroelectric-based devices such as ferroelectric memories or photodetectors. Therefore, to cancel out the Eimp, ferroelectric components are commonly built using symmetric contact configuration. Indeed, in this symmetric contact configuration, when measurements are done under time-varying electric fields of relatively low frequency, an archetypical symmetric single-step switching process is observed, indicating Eimp ≈ 0. However, we report here on the discovery that when measurements are performed at high frequency, a well-defined double-step switching is observed, indicating the presence of Eimp. We argue that this frequency dependence originates from short-living head-to-head or tail-to-tail ferroelectric capacitors in the device. We demonstrate that we can modulate Eimp and the life-time of head-to-head or tail-to-tail polarization configurations by adjusting the polarization screening charges by suitable illumination. These findings are of relevance to understand the effects of internal electric fields on pivotal ferroelectric properties, such as memory retention and photoresponse. PMID:27122309

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

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

In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.

PubMed

Zhang, Hui; Yu, Peng; Zhang, Teng-Guo; Kang, Yan-Li; Zhao, Xiao; Li, Yuan-Yuan; He, Jia-Hui; Zhang, Ji

2015-11-01

Drug-induced myelotoxicity usually leads to decrease the production of platelets, red cells, and white cells. Thus, early identification and characterization of myelotoxicity hazard in drug development is very necessary. The purpose of this investigation was to develop a prediction model of drug-induced myelotoxicity by using a Naïve Bayes classifier. For comparison, other prediction models based on support vector machine and single-hidden-layer feed-forward neural network  methods were also established. Among all the prediction models, the Naïve Bayes classification model showed the best prediction performance, which offered an average overall prediction accuracy of [Formula: see text] for the training set and [Formula: see text] for the external test set. The significant contributions of this study are that we first developed a Naïve Bayes classification model of drug-induced myelotoxicity adverse effect using a larger scale dataset, which could be employed for the prediction of drug-induced myelotoxicity. In addition, several important molecular descriptors and substructures of myelotoxic compounds have been identified, which should be taken into consideration in the design of new candidate compounds to produce safer and more effective drugs, ultimately reducing the attrition rate in later stages of drug development.

Flake Orientation Effects On Physical and Mechanical Properties of Sweetgum Flakeboard

Treesearch

T.F. Shupe; Chung-Yun Hse; E.W. Price

2001-01-01

Research was initiated to determine the effect of flake orientation on the physical and mechanical properties offlakeboard. The panel fabrication techniques investigated were single-layer panels with random and oriented flake distribution, three-layer, five-layer, and seven-layer panels. Single-layer oriented panels had panel directional property ratios of 11.8 and 12....

Quantitative imaging of mammalian transcriptional dynamics: from single cells to whole embryos.

PubMed

Zhao, Ziqing W; White, Melanie D; Bissiere, Stephanie; Levi, Valeria; Plachta, Nicolas

2016-12-23

Probing dynamic processes occurring within the cell nucleus at the quantitative level has long been a challenge in mammalian biology. Advances in bio-imaging techniques over the past decade have enabled us to directly visualize nuclear processes in situ with unprecedented spatial and temporal resolution and single-molecule sensitivity. Here, using transcription as our primary focus, we survey recent imaging studies that specifically emphasize the quantitative understanding of nuclear dynamics in both time and space. These analyses not only inform on previously hidden physical parameters and mechanistic details, but also reveal a hierarchical organizational landscape for coordinating a wide range of transcriptional processes shared by mammalian systems of varying complexity, from single cells to whole embryos.

Predicting human protein function with multi-task deep neural networks.

PubMed

Fa, Rui; Cozzetto, Domenico; Wan, Cen; Jones, David T

2018-01-01

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability.

Raman study of supported molybdenum disulfide single layers

NASA Astrophysics Data System (ADS)

Durrer, William; Manciu, Felicia; Afanasiev, Pavel; Berhault, Gilles; Chianelli, Russell

2008-10-01

Owing to the increasing demand for clean transportation fuels, highly dispersed single layer transition metal sulfides such as MoS2-based catalysts play an important role in catalytic processes for upgrading and removing sulfur from heavy petroleum feed. In its crystalline bulk form, MoS2 is chemically rather inactive due to a strong tendency to form highly stacked layers, but, when dispersed as single-layer nanoclusters on a support, the MoS2 becomes catalytically active in the hydrogenolysis of sulphur and nitrogen from organic compounds (hydrotreating catalysis). In the present studies alumina-supported MoS2 samples were analyzed by confocal Raman spectroscopy. Evidence of peaks at 152 cm-1, 234 cm-1, and 336 cm-1, normally not seen in the Raman spectrum of the standard bulk crystal, confirms the formation of single layers of MoS2. Furthermore, the presence of the 383 cm-1 Raman line suggests the trigonal prismatic coordination of the formed MoS2 single layers. Depending on the sample preparation method, a restacking of MoS2 layers is also observed, mainly for ex-thiomolybdate samples sulfided at 550 C.

Learning Strategy Selection in the Water Maze and Hippocampal CREB Phosphorylation Differ in Two Inbred Strains of Mice

ERIC Educational Resources Information Center

Sung, Jin-Young; Goo, June-Seo; Lee, Dong-Eun; Jin, Da-Qing; Bizon, Jennifer L.; Gallagher, Michela; Han, Jung-Soo

2008-01-01

Learning strategy selection was assessed in two different inbred strains of mice, C57BL/6 and DBA/2, which are used for developing genetically modified mouse models. Male mice received a training protocol in a water maze using alternating blocks of visible and hidden platform trials, during which mice escaped to a single location. After training,…

Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector.

PubMed

Stantchev, Rayko Ivanov; Sun, Baoqing; Hornett, Sam M; Hobson, Peter A; Gibson, Graham M; Padgett, Miles J; Hendry, Euan

2016-06-01

Terahertz (THz) imaging can see through otherwise opaque materials. However, because of the long wavelengths of THz radiation (λ = 400 μm at 0.75 THz), far-field THz imaging techniques suffer from low resolution compared to visible wavelengths. We demonstrate noninvasive, near-field THz imaging with subwavelength resolution. We project a time-varying, intense (>100 μJ/cm(2)) optical pattern onto a silicon wafer, which spatially modulates the transmission of synchronous pulse of THz radiation. An unknown object is placed on the hidden side of the silicon, and the far-field THz transmission corresponding to each mask is recorded by a single-element detector. Knowledge of the patterns and of the corresponding detector signal are combined to give an image of the object. Using this technique, we image a printed circuit board on the underside of a 115-μm-thick silicon wafer with ~100-μm (λ/4) resolution. With subwavelength resolution and the inherent sensitivity to local conductivity, it is possible to detect fissures in the circuitry wiring of a few micrometers in size. THz imaging systems of this type will have other uses too, where noninvasive measurement or imaging of concealed structures is necessary, such as in semiconductor manufacturing or in ex vivo bioimaging.

Hot Jupiter with Hidden Water (Artist Concept)

NASA Image and Video Library

2016-06-08

Hot Jupiters, exoplanets around the same size as Jupiter that orbit very closely to their stars, often have cloud or haze layers in their atmospheres. This may prevent space telescopes from detecting atmospheric water that lies beneath the clouds, according to a study in the Astrophysical Journal. As much as half of the water in the atmospheres of these exoplanets may be blocked by these clouds or hazes, research suggests. The study, led by researchers at NASA's Jet Propulsion Laboratory, Pasadena, California, examined hot Jupiters that had been observed with the Hubble Space Telescope. http://photojournal.jpl.nasa.gov/catalog/PIA20687

Compacted dimensions and singular plasmonic surfaces

NASA Astrophysics Data System (ADS)

Pendry, J. B.; Huidobro, Paloma Arroyo; Luo, Yu; Galiffi, Emanuele

2017-11-01

In advanced field theories, there can be more than four dimensions to space, the excess dimensions described as compacted and unobservable on everyday length scales. We report a simple model, unconnected to field theory, for a compacted dimension realized in a metallic metasurface periodically structured in the form of a grating comprising a series of singularities. An extra dimension of the grating is hidden, and the surface plasmon excitations, though localized at the surface, are characterized by three wave vectors rather than the two of typical two-dimensional metal grating. We propose an experimental realization in a doped graphene layer.

Diverse and tunable electronic structures of single-layer metal phosphorus trichalcogenides for photocatalytic water splitting

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

Liu, Jian; Beijing Computational Science Research Center, Beijing 100084; College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411105, Hunan

2014-02-07

The family of bulk metal phosphorus trichalcogenides (APX{sub 3}, A = M{sup II}, M{sub 0.5}{sup I}M{sub 0.5}{sup III}; X = S, Se; M{sup I}, M{sup II}, and M{sup III} represent Group-I, Group-II, and Group-III metals, respectively) has attracted great attentions because such materials not only own magnetic and ferroelectric properties, but also exhibit excellent properties in hydrogen storage and lithium battery because of the layered structures. Many layered materials have been exfoliated into two-dimensional (2D) materials, and they show distinct electronic properties compared with their bulks. Here we present a systematical study of single-layer metal phosphorus trichalcogenides by density functionalmore » theory calculations. The results show that the single layer metal phosphorus trichalcogenides have very low formation energies, which indicates that the exfoliation of single layer APX{sub 3} should not be difficult. The family of single layer metal phosphorus trichalcogenides exhibits a large range of band gaps from 1.77 to 3.94 eV, and the electronic structures are greatly affected by the metal or the chalcogenide atoms. The calculated band edges of metal phosphorus trichalcogenides further reveal that single-layer ZnPSe{sub 3}, CdPSe{sub 3}, Ag{sub 0.5}Sc{sub 0.5}PSe{sub 3}, and Ag{sub 0.5}In{sub 0.5}PX{sub 3} (X = S and Se) have both suitable band gaps for visible-light driving and sufficient over-potentials for water splitting. More fascinatingly, single-layer Ag{sub 0.5}Sc{sub 0.5}PSe{sub 3} is a direct band gap semiconductor, and the calculated optical absorption further convinces that such materials own outstanding properties for light absorption. Such results demonstrate that the single layer metal phosphorus trichalcogenides own high stability, versatile electronic properties, and high optical absorption, thus such materials have great chances to be high efficient photocatalysts for water-splitting.« less

Boudinage in nature and experiment

NASA Astrophysics Data System (ADS)

Marques, Fernando O.; Fonseca, Pedro D.; Lechmann, Sarah; Burg, Jean-Pierre; Marques, Ana S.; Andrade, Alexandre J. M.; Alves, Carlos

2012-03-01

Deformation of rocks produces structures at all scales that are in many cases periodic (folding or boudinage), with variable amplitude and wavelength. Here we focus on boudinage, a process of primordial importance for tectonics. In the present study, we carried out measurements of natural boudins and experimentally tested the effects of two variables on boudinage: layer thickness and compression rate. The models were made of a competent layer (mostly brittle, as in nature) of either elastic (soft paper) or viscoelastoplastic (clay) material embedded in a ductile matrix of linear viscous silicone putty. The competent layer lied with its greatest surface normal to the principal shortening axis and greatest length parallel to the principal stretching axis. The model was then subjected to pure shear at constant piston velocity and variable competent layer thickness (Model 1), or at different piston velocity and constant layer thickness (Model 2). The results of Model 1 show an exponential dependence of boudin width on competent layer thickness, in disagreement with data from the studied natural occurrence. This indicates that variables other than competent layer thickness are hidden in the linear relationship obtained for the natural boudinage. The results of Model 2 show that the higher the velocity the smaller the boudin width, following a power-law with exponent very similar to that of analytical predictions. The studied natural boudinage occasionally occurs in two orthogonal directions. This chocolate tablet boudinage can be the result of two successive stages of deformation: buckling followed by stretching of competent sandstone layers, or buckling followed by rotation of reverse limbs into the extensional field of simple shear.

Performing differential operation with a silver dendritic metasurface at visible wavelengths.

PubMed

Chen, Huan; An, Di; Li, Zhenchun; Zhao, Xiaopeng

2017-10-30

We design a reflective silver dendritic metasurface that can perform differential operation at visible wavelengths. The metasurface consists of an upper layer of silver dendritic structures, a silica spacer, and a lower layer of silver film. Simulation results show that the metasurface can realize differential operation in red, yellow, and green bands. Such a functionality is readily extended to infrared and communication wavelengths. The metasurface samples that respond to green and red bands are prepared by using the electrochemical deposition method, and their differential operation properties are proved through tests. Silver dendritic metasurfaces that can conduct the mathematical operation in visible light pave the way for realizing miniaturized, integratable all-optical information processing systems. Their differentiation functionality, which is used for real-time ultra-fast edge detection, image contrast enhancement, hidden object detection, and other practical applications, has a great development potential.

Hidden Order and Dimensional Crossover of the Charge Density Waves in TiSe 2

DOE PAGES

Chen, P.; Chan, Y. -H.; Fang, X. -Y.; ...

2016-11-29

Charge density wave (CDW) formation, a key physics issue for materials, arises from interactions among electrons and phonons that can also lead to superconductivity and other competing or entangled phases. The prototypical system TiSe 2, with a particularly simple (2 × 2 × 2) transition and no Kohn anomalies caused by electron-phonon coupling, is a fascinating but unsolved case after decades of research. Our angle-resolved photoemission measurements of the band structure as a function of temperature, aided by first-principles calculations, reveal a hitherto undetected but crucial feature: a (2 × 2) electronic order in each layer sets in at ~232more » K before the widely recognized three-dimensional structural order at ~205 K. The dimensional crossover, likely a generic feature of such layered materials, involves renormalization of different band gaps in two stages.« less

Deep Raman spectroscopy for the non-invasive standoff detection of concealed chemical threat agents.

PubMed

Izake, Emad L; Cletus, Biju; Olds, William; Sundarajoo, Shankaran; Fredericks, Peter M; Jaatinen, Esa

2012-05-30

Deep Raman spectroscopy has been utilized for the standoff detection of concealed chemical threat agents from a distance of 15 m under real life background illumination conditions. By using combined time and space resolved measurements, various explosive precursors hidden in opaque plastic containers were identified non-invasively. Our results confirm that combined time and space resolved Raman spectroscopy leads to higher selectivity towards the sub-layer over the surface layer as well as enhanced rejection of fluorescence from the container surface when compared to standoff spatially offset Raman spectroscopy. Raman spectra that have minimal interference from the packaging material and good signal-to-noise ratio were acquired within 5 s of measurement time. A new combined time and space resolved Raman spectrometer has been designed with nanosecond laser excitation and gated detection, making it of lower cost and complexity than picosecond-based laboratory systems. Copyright © 2012 Elsevier B.V. All rights reserved.

Hacia la predicción del Número R de Wolf de manchas solares utilizando Redes Neuronales con retardos temporales

NASA Astrophysics Data System (ADS)

Francile, C.; Luoni, M. L.

We present a prediction of the time series of the Wolf number R of sunspots using "time lagged feed forward neural networks". We use two types of networks: the focused and distributed ones which were trained with the back propagation of errors algorithm and the temporal back propagation algorithm respectively. As inputs to neural networks we use the time series of the number R averaged annually and monthly with the method IR5. As data sets for training and test we choose certain intervals of the time series similar to other works, in order to compare the results. Finally we discuss the topology of the networks used, the number of delays used, the number of neurons per layer, the number of hidden layers and the results in the prediction of the series between one and six steps ahead. FULL TEXT IN SPANISH

Study on algorithm of process neural network for soft sensing in sewage disposal system

NASA Astrophysics Data System (ADS)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

Temperature based Restricted Boltzmann Machines

NASA Astrophysics Data System (ADS)

Li, Guoqi; Deng, Lei; Xu, Yi; Wen, Changyun; Wang, Wei; Pei, Jing; Shi, Luping

2016-01-01

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.

Fourier analysis: from cloaking to imaging

NASA Astrophysics Data System (ADS)

Wu, Kedi; Cheng, Qiluan; Wang, Guo Ping

2016-04-01

Regarding invisibility cloaks as an optical imaging system, we present a Fourier approach to analytically unify both Pendry cloaks and complementary media-based invisibility cloaks into one kind of cloak. By synthesizing different transfer functions, we can construct different devices to realize a series of interesting functions such as hiding objects (events), creating illusions, and performing perfect imaging. In this article, we give a brief review on recent works of applying Fourier approach to analysis invisibility cloaks and optical imaging through scattering layers. We show that, to construct devices to conceal an object, no constructive materials with extreme properties are required, making most, if not all, of the above functions realizable by using naturally occurring materials. As instances, we experimentally verify a method of directionally hiding distant objects and create illusions by using all-dielectric materials, and further demonstrate a non-invasive method of imaging objects completely hidden by scattering layers.

Modeling of the radiation belt megnetosphere in decisional timeframes

DOEpatents

Koller, Josef; Reeves, Geoffrey D; Friedel, Reiner H.W.

2013-04-23

Systems and methods for calculating L* in the magnetosphere with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, and/or spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models. The feedforward neural network can have a plurality of input-layer units, there being at least one input-layer unit for each physics-based model parameter, a plurality of hidden layer units and at least one output unit for the value of L*.

Detection of Potential Shallow Aquifer Using Electrical Resistivity Imaging (ERI) at UTHM Campus, Johor Malaysia

NASA Astrophysics Data System (ADS)

Izzaty Riwayat, Akhtar; Nazri, Mohd Ariff Ahmad; Hazreek Zainal Abidin, Mohd

2018-04-01

In recent years, Electrical Resistivity Imaging (ERI) has become part of important method in preliminary stage as to gain more information in indicate the hidden water in underground layers. The problem faces by engineers is to determine the exact location of groundwater zone in subsurface layers. ERI seen as the most suitable tools in exploration of groundwater as this method have been applied in geotechnical and geo-environment investigation. This study was conducted using resistivity at UTHM campus to interpret the potential shallow aquifer and potential location for borehole as observation well. A Schlumberger array was setup during data acquisition as this array is capable in imaging deeper profile data and suitable for areas with homogeneous layer. The raw data was processed using RES2DINV software for 2D subsurface image. The result obtained indicate that the thickness of shallow aquifer for both spread line varies between 7.5 m to 15 m. The analysis of rest raw data using IP showed that the chargeability parameter is equal to 0 which strongly indicated the presence of groundwater aquifer in the study area.

An implementation of Elman neural network for polycystic ovary classification based on ultrasound images

NASA Astrophysics Data System (ADS)

Thufailah, I. F.; Adiwijaya; Wisesty, U. N.; Jondri

2018-03-01

Polycystic Ovary Syndrome (PCOS) is a reproduction problem that causes irregular menstruation period. Insulin and androgen hormone have big roles for this problem. This syndrome should be detected shortly, since it is able to cause a more serious disease, such as cardiovascular, diabetes, and obesity. The detection of this syndrome is done by analyzing ovary morphology and hormone test. However, the more economical way of test is by identifying the ovary morphology using ultrasonography. To classify whether one ovary is normal or it has polycystic ovary (PCO) follicle, the analysis will be done manually by a gynecologist. This paper will design a system to detect PCO using Gabor Wavelet method for feature extraction and Elman Neural Network is used to classify PCO and non-PCO. Elman Neural Network is chosen because it contains context layer to recall the previous condition. This paper compared the accuracy and process time of each dataset, then also did testing on elman’s parameters, such as layer delay, hidden layer, and training function. Based on tests done in this paper, the most accurate number is 78.1% with 32 features.

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

PubMed Central

Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

Deep greedy learning under thermal variability in full diurnal cycles

NASA Astrophysics Data System (ADS)

Rauss, Patrick; Rosario, Dalton

2017-08-01

We study the generalization and scalability behavior of a deep belief network (DBN) applied to a challenging long-wave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower. The collections cover multiple full diurnal cycles and include different atmospheric conditions. Using complementary priors, a DBN uses a greedy algorithm that can learn deep, directed belief networks one layer at a time and has two layers form to provide undirected associative memory. The greedy algorithm initializes a slower learning procedure, which fine-tunes the weights, using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of spectral data and their labels, despite significant data variability between and within classes due to environmental and temperature variation occurring within and between full diurnal cycles. We argue, however, that more questions than answers are raised regarding the generalization capacity of these deep nets through experiments aimed at investigating their training and augmented learning behavior.

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

PubMed

Pang, Shan; Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater.

PubMed

Antwi, Philip; Li, Jianzheng; Meng, Jia; Deng, Kaiwen; Koblah Quashie, Frank; Li, Jiuling; Opoku Boadi, Portia

2018-06-01

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH 4 + , VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R 2 ), the BPANN model demonstrated significant performance with R 2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible. Copyright © 2018 Elsevier Ltd. All rights reserved.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks.

PubMed

Ying, Jun; Dutta, Joyita; Guo, Ning; Hu, Chenhui; Zhou, Dan; Sitek, Arkadiusz; Li, Quanzheng

2016-12-21

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Hidden Attractors in Dynamical Systems. From Hidden Oscillations in Hilbert-Kolmogorov Aizerman, and Kalman Problems to Hidden Chaotic Attractor in Chua Circuits

NASA Astrophysics Data System (ADS)

Leonov, G. A.; Kuznetsov, N. V.

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect with small neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure. For localization of hidden attractors it is necessary to develop special procedures, since there are no similar transient processes leading to such attractors. At first, the problem of investigating hidden oscillations arose in the second part of Hilbert's 16th problem (1900). The first nontrivial results were obtained in Bautin's works, which were devoted to constructing nested limit cycles in quadratic systems, that showed the necessity of studying hidden oscillations for solving this problem. Later, the problem of analyzing hidden oscillations arose from engineering problems in automatic control. In the 50-60s of the last century, the investigations of widely known Markus-Yamabe's, Aizerman's, and Kalman's conjectures on absolute stability have led to the finding of hidden oscillations in automatic control systems with a unique stable stationary point. In 1961, Gubar revealed a gap in Kapranov's work on phase locked-loops (PLL) and showed the possibility of the existence of hidden oscillations in PLL. At the end of the last century, the difficulties in analyzing hidden oscillations arose in simulations of drilling systems and aircraft's control systems (anti-windup) which caused crashes. Further investigations on hidden oscillations were greatly encouraged by the present authors' discovery, in 2010 (for the first time), of chaotic hidden attractor in Chua's circuit. This survey is dedicated to efficient analytical-numerical methods for the study of hidden oscillations. Here, an attempt is made to reflect the current trends in the synthesis of analytical and numerical methods.

Lidar observation of transition of cirrus clouds over a tropical station Gadanki (13.45° N, 79.18° E): case studies

NASA Astrophysics Data System (ADS)

Srinivasan, M. A.; Rao, C. Dhananjaya; Krishnaiah, M.

2016-05-01

The present study describes Mie lidar observations of the cirrus cloud passage showing transition between double thin layers into single thick and single thick layer into double thin layers of cirrus over Gadanki region. During Case1: 17 January 2007, Case4: 12 June 2007, Case5: 14 July 2007 and Case6: 24 July 2007 the transition is found to from two thin cirrus layers into single geometrically thick layer. Case2: 14 May 2007 and Case3: 15 May 2007, the transition is found to from single geometrically thick layer into two thin cirrus layers. Linear Depolarization Ratio (LDR) and Back Scatter Ration (BSR) are found to show similar variation with strong peaks during transition; both LDR and Cloud Optical Depth (COD) is found to show similar variation except during transition with strong peaks in COD which is not clearly found from LDR for the all cases. There is a significant weakening of zonal and meridional winds during Case1 which might be due to the transition from multiple to single thick cirrus indicating potential capability of thick cirrus in modulating the wind fields. There exists strong upward wind dominance contributed to significant ascent in cloud-base altitude thereby causing transition of multiple thin layers into single thick cirrus.

Cloud Height Estimation with a Single Digital Camera and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Carretas, Filipe; Janeiro, Fernando M.

2014-05-01

Clouds influence the local weather, the global climate and are an important parameter in the weather prediction models. Clouds are also an essential component of airplane safety when visual flight rules (VFR) are enforced, such as in most small aerodromes where it is not economically viable to install instruments for assisted flying. Therefore it is important to develop low cost and robust systems that can be easily deployed in the field, enabling large scale acquisition of cloud parameters. Recently, the authors developed a low-cost system for the measurement of cloud base height using stereo-vision and digital photography. However, due to the stereo nature of the system, some challenges were presented. In particular, the relative camera orientation requires calibration and the two cameras need to be synchronized so that the photos from both cameras are acquired simultaneously. In this work we present a new system that estimates the cloud height between 1000 and 5000 meters. This prototype is composed by one digital camera controlled by a Raspberry Pi and is installed at Centro de Geofísica de Évora (CGE) in Évora, Portugal. The camera is periodically triggered to acquire images of the overhead sky and the photos are downloaded to the Raspberry Pi which forwards them to a central computer that processes the images and estimates the cloud height in real time. To estimate the cloud height using just one image requires a computer model that is able to learn from previous experiences and execute pattern recognition. The model proposed in this work is an Artificial Neural Network (ANN) that was previously trained with cloud features at different heights. The chosen Artificial Neural Network is a three-layer network, with six parameters in the input layer, 12 neurons in the hidden intermediate layer, and an output layer with only one output. The six input parameters are the average intensity values and the intensity standard deviation of each RGB channel. The output parameter in the output layer is the cloud height estimated by the ANN. The training procedure was performed, using the back-propagation method, in a set of 260 different clouds with heights in the range [1000, 5000] m. The training of the ANN has resulted in a correlation ratio of 0.74. This trained ANN can therefore be used to estimate the cloud height. The previously described system can also measure the wind speed and direction at cloud height by measuring the displacement, in pixels, of a cloud feature between consecutively acquired photos. Also, the geographical north direction can be estimated using this setup through sequential night images with high exposure times. A further advantage of this single camera system is that no camera calibration or synchronization is needed. This significantly reduces the cost and complexity of field deployment of cloud height measurement systems based on digital photography.

A prospective study of two methods of closing surgical scalp wounds.

PubMed

Adeolu, A A; Olabanji, J K; Komolafe, E O; Ademuyiwa, A O; Awe, A O; Oladele, A O

2012-02-01

Scalp wounds are commonly closed in two layers, although single layer closure is feasible. This study prospectively compared the two methods of closing scalp wounds. Patients with non-traumatic scalp wounds were allocated to either the single layer closure group or the multilayer closure group. We obtained relevant data from the patients. The primary outcome measures were wound edge related complications, rate of suturing and cost of sutures used for suturing. Thirty-one wounds were in the single layer closure group and 30 were in the multilayer closure group. Age range was 1-80 years. The most common indication for making a scalp incision was subdural hematoma, representing 27.8% of all the indications. The most common surgery was burr hole drainage of subdural hematoma. Polyglactin acid suture was used for the inner layer and polyamide -00- for the final layer in the multilayer closure group. Only the latter suture was used for the single layer closure method. Total cost of suturing per wound in the single layer closure group was N= 100 (0.70USD) and N= 800 (5.30USD) in the multilayer group. The mean rate of closure was 0.39 ± 1.89 mm/sec for single layer closure and 0.23 ± 0.89 mm/sec in multilayer closure. The difference was statistically significant. Wound edge related complication rate was 19.35% in the single layer closure group and 16.67% in the multilayer closure method group. The difference was not statistically significant (z: 0.00, p value: 1.000; Pearson chi-squared (DF = 1)= 0.0075, p = 0.0785). The study shows that closing the scalp in one layer is much faster and more cost effective compared to the multilayer closure method. We did not observe significant difference in the complication rates in the two methods of closure. Long-term outcome, especially cosmetic outcome, remains to be determined in this preliminary study.

Sensor-Based Human Activity Recognition in a Multi-user Scenario

NASA Astrophysics Data System (ADS)

Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

Quantum processing by remote quantum control

NASA Astrophysics Data System (ADS)

Qiang, Xiaogang; Zhou, Xiaoqi; Aungskunsiri, Kanin; Cable, Hugo; O'Brien, Jeremy L.

2017-12-01

Client-server models enable computations to be hosted remotely on quantum servers. We present a novel protocol for realizing this task, with practical advantages when using technology feasible in the near term. Client tasks are realized as linear combinations of operations implemented by the server, where the linear coefficients are hidden from the server. We report on an experimental demonstration of our protocol using linear optics, which realizes linear combination of two single-qubit operations by a remote single-qubit control. In addition, we explain when our protocol can remain efficient for larger computations, as well as some ways in which privacy can be maintained using our protocol.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Nanomanufacturing of silicon surface with a single atomic layer precision via mechanochemical reactions.

PubMed

Chen, Lei; Wen, Jialin; Zhang, Peng; Yu, Bingjun; Chen, Cheng; Ma, Tianbao; Lu, Xinchun; Kim, Seong H; Qian, Linmao

2018-04-18

Topographic nanomanufacturing with a depth precision down to atomic dimension is of importance for advancement of nanoelectronics with new functionalities. Here we demonstrate a mask-less and chemical-free nanolithography process for regio-specific removal of atomic layers on a single crystalline silicon surface via shear-induced mechanochemical reactions. Since chemical reactions involve only the topmost atomic layer exposed at the interface, the removal of a single atomic layer is possible and the crystalline lattice beneath the processed area remains intact without subsurface structural damages. Molecular dynamics simulations depict the atom-by-atom removal process, where the first atomic layer is removed preferentially through the formation and dissociation of interfacial bridge bonds. Based on the parametric thresholds needed for single atomic layer removal, the critical energy barrier for water-assisted mechanochemical dissociation of Si-Si bonds was determined. The mechanochemical nanolithography method demonstrated here could be extended to nanofabrication of other crystalline materials.

Dfam: a database of repetitive DNA based on profile hidden Markov models.

PubMed

Wheeler, Travis J; Clements, Jody; Eddy, Sean R; Hubley, Robert; Jones, Thomas A; Jurka, Jerzy; Smit, Arian F A; Finn, Robert D

2013-01-01

We present a database of repetitive DNA elements, called Dfam (http://dfam.janelia.org). Many genomes contain a large fraction of repetitive DNA, much of which is made up of remnants of transposable elements (TEs). Accurate annotation of TEs enables research into their biology and can shed light on the evolutionary processes that shape genomes. Identification and masking of TEs can also greatly simplify many downstream genome annotation and sequence analysis tasks. The commonly used TE annotation tools RepeatMasker and Censor depend on sequence homology search tools such as cross_match and BLAST variants, as well as Repbase, a collection of known TE families each represented by a single consensus sequence. Dfam contains entries corresponding to all Repbase TE entries for which instances have been found in the human genome. Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated using RepeatMasker and Repbase. When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9% increase in coverage over consensus sequence search methods on a large human benchmark, while maintaining low false discovery rates, and coverage of the full human genome is 54.5%. The website provides a collection of tools and data views to support improved TE curation and annotation efforts. Dfam is also available for download in flat file format or in the form of MySQL table dumps.

The ``Folk Theorem'' on effective field theory: How does it fare in nuclear physics?

NASA Astrophysics Data System (ADS)

Rho, Mannque

2017-10-01

This is a brief history of what I consider as very important, some of which truly seminal, contributions made by young Korean nuclear theorists, mostly graduate students working on PhD thesis in 1990s and early 2000s, to nuclear effective field theory, nowadays heralded as the first-principle approach to nuclear physics. The theoretical framework employed is an effective field theory anchored on a single scale-invariant hidden local symmetric Lagrangian constructed in the spirit of Weinberg's "Folk Theorem" on effective field theory. The problems addressed are the high-precision calculations on the thermal np capture, the solar pp fusion process, the solar hep process — John Bahcall's challenge to nuclear theorists — and the quenching of g A in giant Gamow-Teller resonances and the whopping enhancement of first-forbidden beta transitions relevant in astrophysical processes. Extending adventurously the strategy to a wild uncharted domain in which a systematic implementation of the "theorem" is far from obvious, the same effective Lagrangian is applied to the structure of compact stars. A surprising, unexpected, result on the properties of massive stars, totally different from what has been obtained up to day in the literature, is predicted, such as the precocious onset of conformal sound velocity together with a hint for the possible emergence in dense matter of hidden symmetries such as scale symmetry and hidden local symmetry.

Evaluation of extra virgin olive oil stability by artificial neural network.

PubMed

Silva, Simone Faria; Anjos, Carlos Alberto Rodrigues; Cavalcanti, Rodrigo Nunes; Celeghini, Renata Maria dos Santos

2015-07-15

The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

PubMed

Bianchini, Monica; Scarselli, Franco

2014-08-01

Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

The secret art of managing healthcare expenses: investigating implicit rationing and autonomy in public healthcare systems.

PubMed

Lauridsen, S M R; Norup, M S; Rossel, P J H

2007-12-01

Rationing healthcare is a difficult task, which includes preventing patients from accessing potentially beneficial treatments. Proponents of implicit rationing argue that politicians cannot resist pressure from strong patient groups for treatments and conclude that physicians should ration without informing patients or the public. The authors subdivide this specific programme of implicit rationing, or "hidden rationing", into local hidden rationing, unsophisticated global hidden rationing and sophisticated global hidden rationing. They evaluate the appropriateness of these methods of rationing from the perspectives of individual and political autonomy and conclude that local hidden rationing and unsophisticated global hidden rationing clearly violate patients' individual autonomy, that is, their right to participate in medical decision-making. While sophisticated global hidden rationing avoids this charge, the authors point out that it nonetheless violates the political autonomy of patients, that is, their right to engage in public affairs as citizens. A defence of any of the forms of hidden rationing is therefore considered to be incompatible with a defence of autonomy.

The SO(3)×SO(3)×U(1) Hubbard model on a square lattice in terms of c and αν fermions and deconfined η-spinons and spinons

NASA Astrophysics Data System (ADS)

Carmelo, J. M. P.

2012-03-01

In this paper, a general description for the Hubbard model with nearest-neighbor transfer integral t and on-site repulsion U on a square lattice with Na2≫1 sites is introduced. It refers to three types of elementary objects whose occupancy configurations generate the state representations of the model extended global SO(3)×SO(3)×U(1) symmetry recently found in Ref. [11] (Carmelo and Östlund, 2010). Such objects emerge from a suitable electron-rotated-electron unitary transformation. It is such that rotated-electron single and double occupancy are good quantum numbers for U≠0. The advantage of the description is that it accounts for the new found hidden U(1) symmetry in SO(3)×SO(3)×U(1)=[SU(2)×SU(2)×U(1)]/Z22 beyond the well-known SO(4)=[SU(2)×SU(2)]/Z2 model (partial) global symmetry. Specifically, the hidden U(1) symmetry state representations store full information on the positions of the spins of the rotated-electron singly occupied sites relative to the remaining sites. Profiting from that complementary information, for the whole U/4t>0 interaction range independent spin state representations are naturally generated in terms of spin-1/2 spinon occupancy configurations in a spin effective lattice. For all states, such an effective lattice has as many sites as spinons. This allows the extension to intermediate U/4t values of the usual large-U/4t descriptions of the spin degrees of freedom of the electrons that singly occupy sites, now in terms of the spins of the singly-occupied sites rotated electrons. The operator description introduced in this paper brings about a more suitable scenario for handling the effects of hole doping. Within this, such effects are accounted for in terms of the residual interactions of the elementary objects whose occupancy configurations generate the state representations of the charge hidden U(1) symmetry and spin SU(2) symmetry, respectively. This problem is investigated elsewhere. The most interesting physical information revealed by the description refers to the model on the subspace generated by the application of one- and two-electron operators onto zero-magnetization ground states. (This is the square-lattice quantum liquid further studied in Ref. [5] (Carmelo, 2010).) However, to access such an information, one must start from the general description introduced in this paper, which refers to the model in the full Hilbert space.

Structure and energetics of carbon, hexagonal boron nitride, and carbon/hexagonal boron nitride single-layer and bilayer nanoscrolls

NASA Astrophysics Data System (ADS)

Siahlo, Andrei I.; Poklonski, Nikolai A.; Lebedev, Alexander V.; Lebedeva, Irina V.; Popov, Andrey M.; Vyrko, Sergey A.; Knizhnik, Andrey A.; Lozovik, Yurii E.

2018-03-01

Single-layer and bilayer carbon and hexagonal boron nitride nanoscrolls as well as nanoscrolls made of bilayer graphene/hexagonal boron nitride heterostructure are considered. Structures of stable states of the corresponding nanoscrolls prepared by rolling single-layer and bilayer rectangular nanoribbons are obtained based on the analytical model and numerical calculations. The lengths of nanoribbons for which stable and energetically favorable nanoscrolls are possible are determined. Barriers to rolling of single-layer and bilayer nanoribbons into nanoscrolls and barriers to nanoscroll unrolling are calculated. Based on the calculated barriers nanoscroll lifetimes in the stable state are estimated. Elastic constants for bending of graphene and hexagonal boron nitride layers used in the model are found by density functional theory calculations.

Identifying hidden voice and video streams

NASA Astrophysics Data System (ADS)

Fan, Jieyan; Wu, Dapeng; Nucci, Antonio; Keralapura, Ram; Gao, Lixin

2009-04-01

Given the rising popularity of voice and video services over the Internet, accurately identifying voice and video traffic that traverse their networks has become a critical task for Internet service providers (ISPs). As the number of proprietary applications that deliver voice and video services to end users increases over time, the search for the one methodology that can accurately detect such services while being application independent still remains open. This problem becomes even more complicated when voice and video service providers like Skype, Microsoft, and Google bundle their voice and video services with other services like file transfer and chat. For example, a bundled Skype session can contain both voice stream and file transfer stream in the same layer-3/layer-4 flow. In this context, traditional techniques to identify voice and video streams do not work. In this paper, we propose a novel self-learning classifier, called VVS-I , that detects the presence of voice and video streams in flows with minimum manual intervention. Our classifier works in two phases: training phase and detection phase. In the training phase, VVS-I first extracts the relevant features, and subsequently constructs a fingerprint of a flow using the power spectral density (PSD) analysis. In the detection phase, it compares the fingerprint of a flow to the existing fingerprints learned during the training phase, and subsequently classifies the flow. Our classifier is not only capable of detecting voice and video streams that are hidden in different flows, but is also capable of detecting different applications (like Skype, MSN, etc.) that generate these voice/video streams. We show that our classifier can achieve close to 100% detection rate while keeping the false positive rate to less that 1%.

Network architectures and circuit function: testing alternative hypotheses in multifunctional networks.

PubMed

Leonard, J L

2000-05-01

Understanding how species-typical movement patterns are organized in the nervous system is a central question in neurobiology. The current explanations involve 'alphabet' models in which an individual neuron may participate in the circuit for several behaviors but each behavior is specified by a specific neural circuit. However, not all of the well-studied model systems fit the 'alphabet' model. The 'equation' model provides an alternative possibility, whereby a system of parallel motor neurons, each with a unique (but overlapping) field of innervation, can account for the production of stereotyped behavior patterns by variable circuits. That is, it is possible for such patterns to arise as emergent properties of a generalized neural network in the absence of feedback, a simple version of a 'self-organizing' behavioral system. Comparison of systems of identified neurons suggest that the 'alphabet' model may account for most observations where CPGs act to organize motor patterns. Other well-known model systems, involving architectures corresponding to feed-forward neural networks with a hidden layer, may organize patterned behavior in a manner consistent with the 'equation' model. Such architectures are found in the Mauthner and reticulospinal circuits, 'escape' locomotion in cockroaches, CNS control of Aplysia gill, and may also be important in the coordination of sensory information and motor systems in insect mushroom bodies and the vertebrate hippocampus. The hidden layer of such networks may serve as an 'internal representation' of the behavioral state and/or body position of the animal, allowing the animal to fine-tune oriented, or particularly context-sensitive, movements to the prevalent conditions. Experiments designed to distinguish between the two models in cases where they make mutually exclusive predictions provide an opportunity to elucidate the neural mechanisms by which behavior is organized in vivo and in vitro. Copyright 2000 S. Karger AG, Basel

Feasibility study of hidden flow imaging based on laser speckle technique using multiperspectives contrast images

NASA Astrophysics Data System (ADS)

Abookasis, David; Moshe, Tomer

2014-11-01

This paper demonstrates the insertion of lens array in the front of a CCD camera in a laser speckle imaging (LSI) like-technique to acquire multiple speckle reflectance projections for imaging blood flow in an intact biological tissue. In some of LSI applications, flow imaging is obtained by thinning or removing of the upper tissue layers to access blood vessels. In contrast, with the proposed approach flow imaging can be achieved while the tissue is intact. In the system, each lens from an hexagonal lens array observed the sample from slightly different perspectives and captured with a CCD camera. In the computer, these multiview raw images are converted to speckled contrast maps. Then, a self-deconvolution shift-and-add algorithm is employed for processing yields high contrast flow information. The method is experimentally validated first with a plastic tube filled with scattering liquid running at different controlled flow rates hidden in a biological tissue and then extensively tested for imaging of cerebral blood flow in an intact rodent head experience different conditions. A total of fifteen mice were used in the experiments divided randomly into three groups as follows: Group 1 (n=5) consisted of injured mice experience hypoxic ischemic brain injury monitored for ~40 min. Group 2 (n=5) injured mice experience anoxic brain injury monitored up to 20 min. Group 3 (n=5) experience functional activation monitored up to ~35 min. To increase tissue transparency and the penetration depth of photons through head tissue layers, an optical clearing method was employed. To our knowledge, this work presents for the first time the use of lens array in LSI scheme.

Sub-basalt Imaging of Hydrocarbon-Bearing Mesozoic Sediments Using Ray-Trace Inversion of First-Arrival Seismic Data and Elastic Finite-Difference Full-Wave Modeling Along Sinor-Valod Profile of Deccan Syneclise, India

NASA Astrophysics Data System (ADS)

Talukdar, Karabi; Behera, Laxmidhar

2018-03-01

Imaging below the basalt for hydrocarbon exploration is a global problem because of poor penetration and significant loss of seismic energy due to scattering, attenuation, absorption and mode-conversion when the seismic waves encounter a highly heterogeneous and rugose basalt layer. The conventional (short offset) seismic data acquisition, processing and modeling techniques adopted by the oil industry generally fails to image hydrocarbon-bearing sub-trappean Mesozoic sediments hidden below the basalt and is considered as a serious problem for hydrocarbon exploration in the world. To overcome this difficulty of sub-basalt imaging, we have generated dense synthetic seismic data with the help of elastic finite-difference full-wave modeling using staggered-grid scheme for the model derived from ray-trace inversion using sparse wide-angle seismic data acquired along Sinor-Valod profile in the Deccan Volcanic Province of India. The full-wave synthetic seismic data generated have been processed and imaged using conventional seismic data processing technique with Kirchhoff pre-stack time and depth migrations. The seismic image obtained correlates with all the structural features of the model obtained through ray-trace inversion of wide-angle seismic data, validating the effectiveness of robust elastic finite-difference full-wave modeling approach for imaging below thick basalts. Using the full-wave modeling also allows us to decipher small-scale heterogeneities imposed in the model as a measure of the rugose basalt interfaces, which could not be dealt with ray-trace inversion. Furthermore, we were able to accurately image thin low-velocity hydrocarbon-bearing Mesozoic sediments sandwiched between and hidden below two thick sequences of high-velocity basalt layers lying above the basement.

An automated approach for annual layer counting in ice cores

NASA Astrophysics Data System (ADS)

Winstrup, M.; Svensson, A.; Rasmussen, S. O.; Winther, O.; Steig, E.; Axelrod, A.

2012-04-01

The temporal resolution of some ice cores is sufficient to preserve seasonal information in the ice core record. In such cases, annual layer counting represents one of the most accurate methods to produce a chronology for the core. Yet, manual layer counting is a tedious and sometimes ambiguous job. As reliable layer recognition becomes more difficult, a manual approach increasingly relies on human interpretation of the available data. Thus, much may be gained by an automated and therefore objective approach for annual layer identification in ice cores. We have developed a novel method for automated annual layer counting in ice cores, which relies on Bayesian statistics. It uses algorithms from the statistical framework of Hidden Markov Models (HMM), originally developed for use in machine speech recognition. The strength of this layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on purely objective criteria for annual layer identification. With this methodology, it is possible to determine the most likely position of multiple layer boundaries in an entire section of ice core data at once. It provides a probabilistic uncertainty estimate of the resulting layer count, hence ensuring a proper treatment of ambiguous layer boundaries in the data. Furthermore multiple data series can be incorporated to be used at once, hence allowing for a full multi-parameter annual layer counting method similar to a manual approach. In this study, the automated layer counting algorithm has been applied to data from the NGRIP ice core, Greenland. The NGRIP ice core has very high temporal resolution with depth, and hence the potential to be dated by annual layer counting far back in time. In previous studies [Andersen et al., 2006; Svensson et al., 2008], manual layer counting has been carried out back to 60 kyr BP. A comparison between the counted annual layers based on the two approaches will be presented and their differences discussed. Within the estimated uncertainties, the two methodologies agree. This shows the potential for a fully automated annual layer counting method to be operational for data sections where the annual layering is unknown.

The Hidden Curriculum as Emancipatory and Non-Emancipatory Tools.

ERIC Educational Resources Information Center

Kanpol, Barry

Moral values implied in school practices and policies constitute the "hidden curriculum." Because the hidden curriculum may promote certain moral values to students, teachers are partially responsible for the moral education of students. A component of the hidden curriculum, institutional political resistance, concerns teacher opposition to…

Organic doping of rotated double layer graphene

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

George, Lijin; Jaiswal, Manu, E-mail: manu.jaiswal@iitm.ac.in

2016-05-06

Charge transfer techniques have been extensively used as knobs to tune electronic properties of two- dimensional systems, such as, for the modulation of conductivity \\ mobility of single layer graphene and for opening the bandgap in bilayer graphene. The charge injected into the graphene layer shifts the Fermi level away from the minimum density of states point (Dirac point). In this work, we study charge transfer in rotated double-layer graphene achieved by the use of organic dopant, Tetracyanoquinodimethane. Naturally occurring bilayer graphene has a well-defined A-B stacking whereas in rotated double-layer the two graphene layers are randomly stacked with differentmore » rotational angles. This rotation is expected to significantly alter the interlayer interaction. Double-layer samples are prepared using layer-by-layer assembly of chemical vapor deposited single-layer graphene and they are identified by characteristic resonance in the Raman spectrum. The charge transfer and distribution of charges between the two graphene layers is studied using Raman spectroscopy and the results are compared with that for single-layer and A-B stacked bilayer graphene doped under identical conditions.« less

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

NASA Astrophysics Data System (ADS)

Sultana, Tahmina; Takagi, Hiroaki; Morimatsu, Miki; Teramoto, Hiroshi; Li, Chun-Biu; Sako, Yasushi; Komatsuzaki, Tamiki

2013-12-01

We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ˜3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

Layer-dependent electrical and optoelectronic responses of ReSe2 nanosheet transistors.

PubMed

Yang, Shengxue; Tongay, Sefaattin; Li, Yan; Yue, Qu; Xia, Jian-Bai; Li, Shun-Shen; Li, Jingbo; Wei, Su-Huai

2014-07-07

The ability to control the appropriate layer thickness of transition metal dichalcogenides (TMDs) affords the opportunity to engineer many properties for a variety of applications in possible technological fields. Here we demonstrate that band-gap and mobility of ReSe2 nanosheet, a new member of the TMDs, increase when the layer number decreases, thus influencing the performances of ReSe2 transistors with different layers. A single-layer ReSe2 transistor shows much higher device mobility of 9.78 cm(2) V(-1) s(-1) than few-layer transistors (0.10 cm(2) V(-1) s(-1)). Moreover, a single-layer device shows high sensitivity to red light (633 nm) and has a light-improved mobility of 14.1 cm(2) V(-1) s(-1). Molecular physisorption is used as "gating" to modulate the carrier density of our single-layer transistors, resulting in a high photoresponsivity (Rλ) of 95 A W(-1) and external quantum efficiency (EQE) of 18 645% in O2 environment. This work highlights the fact that the properties of ReSe2 can be tuned in terms of the number of layers and gas molecule gating, and single-layer ReSe2 with appropriate band-gap is a promising material for future functional device applications.

Double-layer versus single-layer bone-patellar tendon-bone anterior cruciate ligament reconstruction: a prospective randomized study with 3-year follow-up.

PubMed

Mei, Xiaoliang; Zhang, Zhenxiang; Yang, Jingwen

2016-12-01

To evaluate the clinical results of a randomized controlled trial of single-layer versus double-layer bone-patellar tendon-bone (BPTB) anterior cruciate ligament (ACL) reconstruction. Fifty-eight subjects who underwent primary ACL reconstruction with a BPTB allograft were prospectively randomized into two groups: single-layer reconstruction (n = 31) and double-layer reconstruction (n = 27). The following evaluation methods were used: clinical examination, KT-1000 arthrometer measurement, muscle strength, Tegner activity score, Lysholm score, subjective rating scale regarding patient satisfaction and sports performance level, graft retear, contralateral ACL tear, and additional meniscus surgery. Forty-eight subjects (24 in single-layer group and 24 in double-layer group) who were followed up for 3 years were evaluated. Preoperatively, there were no differences between the groups. At 3-year follow-up, the Lachman and pivot-shift test results were better in the double-layer group (P = 0.019 and P < 0.0001, respectively). KT measurements were better in the double-layer group (mean 2.9 versus 1.5 mm; P = 0.0025). The Tegner score was also better in the double-layer group (P = 0.024). There were no significant differences in range of motion, muscle strength, Lysholm score, subjective rating scale, graft retear, and secondary meniscal tear. In ACL reconstruction, double-layer BPTB reconstruction was significantly better than single-layer reconstruction regarding anterior and rotational stability at 3-year follow-up. The results of KT measurements and the Lachman and pivot-shift tests were significantly better in the double-layer group, whereas there was no difference in the anterior drawer test results. The Tegner score was also better in the double-layer group; however, there were no differences in the other subjective findings.

Synergistic effect of temperature and point defect on the mechanical properties of single layer and bi-layer graphene

NASA Astrophysics Data System (ADS)

Debroy, Sanghamitra; Pavan Kumar, V.; Vijaya Sekhar, K.; Acharyya, Swati Ghosh; Acharyya, Amit

2017-10-01

The present study reports a comprehensive molecular dynamics simulation of the effect of a) temperature (300-1073 K at intervals of every 100 K) and b) point defect on the mechanical behaviour of single (armchair and zigzag direction) and bilayer layer graphene (AA and AB stacking). Adaptive intermolecular reactive bond order (AIREBO) potential function was used to describe the many-body short-range interatomic interactions for the single layer graphene sheet. Moreover, Lennard Jones model was considered for bilayer graphene to incorporate the van der Waals interactions among the interlayers of graphene. The effect of temperature on the strain energy of single layer and bilayer graphene was studied in order to understand the difference in mechanical behaviour of the two systems. The strength of the pristine single layer graphene was found to be higher as compared to bilayer AA stacked graphene at all temperatures. It was observed at 1073 K and in the presence of vacancy defect the strength for single layer armchair sheet falls by 30% and for bilayer armchair sheet by 33% as compared to the pristine sheets at 300 K. The AB stacked graphene sheet was found to have a two-step rupture process. The strength of pristine AB sheet was found to decrease by 22% on increase of temperature from 300 K to 1073 K.

Textural analyses of carbon fiber materials by 2D-FFT of complex images obtained by high frequency eddy current imaging (HF-ECI)

NASA Astrophysics Data System (ADS)

Schulze, Martin H.; Heuer, Henning

2012-04-01

Carbon fiber based materials are used in many lightweight applications in aeronautical, automotive, machine and civil engineering application. By the increasing automation in the production process of CFRP laminates a manual optical inspection of each resin transfer molding (RTM) layer is not practicable. Due to the limitation to surface inspection, the quality parameters of multilayer 3 dimensional materials cannot be observed by optical systems. The Imaging Eddy- Current (EC) NDT is the only suitable inspection method for non-resin materials in the textile state that allows an inspection of surface and hidden layers in parallel. The HF-ECI method has the capability to measure layer displacements (misaligned angle orientations) and gap sizes in a multilayer carbon fiber structure. EC technique uses the variation of the electrical conductivity of carbon based materials to obtain material properties. Beside the determination of textural parameters like layer orientation and gap sizes between rovings, the detection of foreign polymer particles, fuzzy balls or visualization of undulations can be done by the method. For all of these typical parameters an imaging classification process chain based on a high resolving directional ECimaging device named EddyCus® MPECS and a 2D-FFT with adapted preprocessing algorithms are developed.

Protein remote homology detection based on bidirectional long short-term memory.

PubMed

Li, Shumin; Chen, Junjie; Liu, Bin

2017-10-10

Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection. In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer. Experimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

PubMed Central

Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

2014-01-01

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

Photoacoustic imaging of hidden dental caries by using a fiber-based probing system

NASA Astrophysics Data System (ADS)

Koyama, Takuya; Kakino, Satoko; Matsuura, Yuji

2017-04-01

Photoacoustic method to detect hidden dental caries is proposed. It was found that high frequency ultrasonic waves are generated from hidden carious part when radiating laser light to occlusal surface of model tooth. By making a map of intensity of these high frequency components, photoacoustic images of hidden caries were successfully obtained. A photoacoustic imaging system using a bundle of hollow optical fiber was fabricated for using clinical application, and clear photoacoustic image of hidden caries was also obtained by this system.

Assessing similarity to primary tissue and cortical layer identity in induced pluripotent stem cell-derived cortical neurons through single-cell transcriptomics

PubMed Central

Handel, Adam E.; Chintawar, Satyan; Lalic, Tatjana; Whiteley, Emma; Vowles, Jane; Giustacchini, Alice; Argoud, Karene; Sopp, Paul; Nakanishi, Mahito; Bowden, Rory; Cowley, Sally; Newey, Sarah; Akerman, Colin; Ponting, Chris P.; Cader, M. Zameel

2016-01-01

Induced pluripotent stem cell (iPSC)-derived cortical neurons potentially present a powerful new model to understand corticogenesis and neurological disease. Previous work has established that differentiation protocols can produce cortical neurons, but little has been done to characterize these at cellular resolution. In particular, it is unclear to what extent in vitro two-dimensional, relatively disordered culture conditions recapitulate the development of in vivo cortical layer identity. Single-cell multiplex reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was used to interrogate the expression of genes previously implicated in cortical layer or phenotypic identity in individual cells. Totally, 93.6% of single cells derived from iPSCs expressed genes indicative of neuronal identity. High proportions of single neurons derived from iPSCs expressed glutamatergic receptors and synaptic genes. And, 68.4% of iPSC-derived neurons expressing at least one layer marker could be assigned to a laminar identity using canonical cortical layer marker genes. We compared single-cell RNA-seq of our iPSC-derived neurons to available single-cell RNA-seq data from human fetal and adult brain and found that iPSC-derived cortical neurons closely resembled primary fetal brain cells. Unexpectedly, a subpopulation of iPSC-derived neurons co-expressed canonical fetal deep and upper cortical layer markers. However, this appeared to be concordant with data from primary cells. Our results therefore provide reassurance that iPSC-derived cortical neurons are highly similar to primary cortical neurons at the level of single cells but suggest that current layer markers, although effective, may not be able to disambiguate cortical layer identity in all cells. PMID:26740550

Dense Plasma Focus: physics and applications (radiation material science, single-shot disclosure of hidden illegal objects, radiation biology and medicine, etc.)

NASA Astrophysics Data System (ADS)

Gribkov, V. A.; Miklaszewski, R.; Paduch, M.; Zielinska, E.; Chernyshova, M.; Pisarczyk, T.; Pimenov, V. N.; Demina, E. V.; Niemela, J.; Crespo, M.-L.; Cicuttin, A.; Tomaszewski, K.; Sadowski, M. J.; Skladnik-Sadowska, E.; Pytel, K.; Zawadka, A.; Giannini, G.; Longo, F.; Talab, A.; Ul'yanenko, S. E.

2015-03-01

The paper presents some outcomes obtained during the year of 2013 of the activity in the frame of the International Atomic Energy Agency Co-ordinated research project "Investigations of Materials under High Repetition and Intense Fusion-Relevant Pulses". The main results are related to the effects created at the interaction of powerful pulses of different types of radiation (soft and hard X-rays, hot plasma and fast ion streams, neutrons, etc. generated in Dense Plasma Focus (DPF) facilities) with various materials including those that are counted as perspective ones for their use in future thermonuclear reactors. Besides we discuss phenomena observed at the irradiation of biological test objects. We examine possible applications of nanosecond powerful pulses of neutrons to the aims of nuclear medicine and for disclosure of hidden illegal objects. Special attention is devoted to discussions of a possibility to create extremely large and enormously diminutive DPF devices and probabilities of their use in energetics, medicine and modern electronics.

Multi-scale chromatin state annotation using a hierarchical hidden Markov model

NASA Astrophysics Data System (ADS)

Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang; Glass, Kimberly; Pinello, Luca; Wang, Jianrong; Kellis, Manolis; Yuan, Guo-Cheng

2017-04-01

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.

Best Hiding Capacity Scheme for Variable Length Messages Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bajaj, Ruchika; Bedi, Punam; Pal, S. K.

Steganography is an art of hiding information in such a way that prevents the detection of hidden messages. Besides security of data, the quantity of data that can be hidden in a single cover medium, is also very important. We present a secure data hiding scheme with high embedding capacity for messages of variable length based on Particle Swarm Optimization. This technique gives the best pixel positions in the cover image, which can be used to hide the secret data. In the proposed scheme, k bits of the secret message are substituted into k least significant bits of the image pixel, where k varies from 1 to 4 depending on the message length. The proposed scheme is tested and results compared with simple LSB substitution, uniform 4-bit LSB hiding (with PSO) for the test images Nature, Baboon, Lena and Kitty. The experimental study confirms that the proposed method achieves high data hiding capacity and maintains imperceptibility and minimizes the distortion between the cover image and the obtained stego image.

Implications of hidden gauged U (1 ) model for B anomalies

NASA Astrophysics Data System (ADS)

Fuyuto, Kaori; Li, Hao-Lin; Yu, Jiang-Hao

2018-06-01

We propose a hidden gauged U (1 )H Z' model to explain deviations from the standard model (SM) values in lepton flavor universality known as RK and RD anomalies. The Z' only interacts with the SM fermions via their mixing with vectorlike doublet fermions after the U (1 )H symmetry breaking, which leads to b →s μ μ transition through the Z' at tree level. Moreover, introducing an additional mediator, inert-Higgs doublet, yields b →c τ ν process via charged scalar contribution at tree level. Using flavio package, we scrutinize adequate sizes of the relevant Wilson coefficients to these two processes by taking various flavor observables into account. It is found that significant mixing between the vectorlike and the second generation leptons is needed for the RK anomaly. A possible explanation of the RD anomaly can also be simultaneously addressed in a motivated situation, where a single scalar operator plays a dominant role, by the successful model parameters for the RK anomaly.

Vibrational and optical properties of MoS2: From monolayer to bulk

NASA Astrophysics Data System (ADS)

Molina-Sánchez, Alejandro; Hummer, Kerstin; Wirtz, Ludger

2015-12-01

Molybdenum disulfide, MoS2, has recently gained considerable attention as a layered material where neighboring layers are only weakly interacting and can easily slide against each other. Therefore, mechanical exfoliation allows the fabrication of single and multi-layers and opens the possibility to generate atomically thin crystals with outstanding properties. In contrast to graphene, it has an optical gap of ~1.9 eV. This makes it a prominent candidate for transistor and opto-electronic applications. Single-layer MoS2 exhibits remarkably different physical properties compared to bulk MoS2 due to the absence of interlayer hybridization. For instance, while the band gap of bulk and multi-layer MoS2 is indirect, it becomes direct with decreasing number of layers. In this review, we analyze from a theoretical point of view the electronic, optical, and vibrational properties of single-layer, few-layer and bulk MoS2. In particular, we focus on the effects of spin-orbit interaction, number of layers, and applied tensile strain on the vibrational and optical properties. We examine the results obtained by different methodologies, mainly ab initio approaches. We also discuss which approximations are suitable for MoS2 and layered materials. The effect of external strain on the band gap of single-layer MoS2 and the crossover from indirect to direct band gap is investigated. We analyze the excitonic effects on the absorption spectra. The main features, such as the double peak at the absorption threshold and the high-energy exciton are presented. Furthermore, we report on the the phonon dispersion relations of single-layer, few-layer and bulk MoS2. Based on the latter, we explain the behavior of the Raman-active A1g and E2g1 modes as a function of the number of layers. Finally, we compare theoretical and experimental results of Raman, photoluminescence, and optical-absorption spectroscopy.

--No Title--

Science.gov Websites

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Natural hidden antibodies reacting with DNA or cardiolipin bind to thymocytes and evoke their death.

PubMed

Zamulaeva, I A; Lekakh, I V; Kiseleva, V I; Gabai, V L; Saenko, A S; Shevchenko, A S; Poverenny, A M

1997-08-18

Both free and hidden natural antibodies to DNA or cardiolipin were obtained from immunoglobulins of a normal donor. The free antibodies reacting with DNA or cardiolipin were isolated by means of affinity chromatography. Antibodies occurring in an hidden state were disengaged from the depleted immunoglobulins by ion-exchange chromatography and were then affinity-isolated on DNA or cardiolipin sorbents. We used flow cytometry to study the ability of free and hidden antibodies to bind to rat thymocytes. Simultaneously, plasma membrane integrity was tested by propidium iodide (PI) exclusion. The hidden antibodies reacted with 65.2 +/- 10.9% of the thymocytes and caused a fast plasma membrane disruption. Cells (28.7 +/- 7.1%) were stained with PI after incubation with the hidden antibodies for 1 h. The free antibodies bound to a very small fraction of the thymocytes and did not evoke death as compared to control without antibodies. The possible reason for the observed effects is difference in reactivity of the free and hidden antibodies to phospholipids. While free antibodies reacted preferentially with phosphotidylcholine, hidden antibodies reacted with cardiolipin and phosphotidylserine.

Neural network and letter recognition

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

Lee, Hue Yeon.

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C-layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken themore » on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the Gabor transform. Pattern dependent choice of center and wavelengths of Gabor filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets.« less

Raising awareness of the hidden curriculum in veterinary medical education: a review and call for research.

PubMed

Whitcomb, Tiffany L

2014-01-01

The hidden curriculum is characterized by information that is tacitly conveyed to and among students about the cultural and moral environment in which they find themselves. Although the hidden curriculum is often defined as a distinct entity, tacit information is conveyed to students throughout all aspects of formal and informal curricula. This unconsciously communicated knowledge has been identified across a wide spectrum of educational environments and is known to have lasting and powerful impacts, both positive and negative. Recently, medical education research on the hidden curriculum of becoming a doctor has come to the forefront as institutions struggle with inconsistencies between formal and hidden curricula that hinder the practice of patient-centered medicine. Similarly, the complex ethical questions that arise during the practice and teaching of veterinary medicine have the potential to cause disagreement between what the institution sets out to teach and what is actually learned. However, the hidden curriculum remains largely unexplored for this field. Because the hidden curriculum is retained effectively by students, elucidating its underlying messages can be a key component of program refinement. A review of recent literature about the hidden curriculum in a variety of fields, including medical education, will be used to explore potential hidden curricula in veterinary medicine and draw attention to the need for further investigation.

Electronic absorption band broadening and surface roughening of phthalocyanine double layers by saturated solvent vapor treatment

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

Kim, Jinhyun; Yim, Sanggyu, E-mail: sgyim@kookmin.ac.kr

2012-10-15

Variations in the electronic absorption (EA) and surface morphology of three types of phthalocyanine (Pc) thin film systems, i.e. copper phthalocyanine (CuPc) single layer, zinc phthalocyanine (ZnPc) single layer, and ZnPc on CuPc (CuPc/ZnPc) double layer film, treated with saturated acetone vapor were investigated. For the treated CuPc single layer film, the surface roughness slightly increased and bundles of nanorods were formed, while the EA varied little. In contrast, for the ZnPc single layer film, the relatively high solubility of ZnPc led to a considerable shift in the absorption bands as well as a large increase in the surface roughnessmore » and formation of long and wide nano-beams, indicating a part of the ZnPc molecules dissolved in acetone, which altered their molecular stacking. For the CuPc/ZnPc film, the saturated acetone vapor treatment resulted in morphological changes in mainly the upper ZnPc layer due to the significantly low solubility of the underlying CuPc layer. The treatment also broadened the EA band, which involved a combination of unchanged CuPc and changed ZnPc absorption.« less

Cytokines in single layer amnion allografts compared to multilayer amnion/chorion allografts for wound healing.

PubMed

Koob, Thomas J; Lim, Jeremy J; Zabek, Nicole; Massee, Michelle

2015-07-01

Human amniotic membrane allografts have proven effective at improving healing of cutaneous wounds. The mechanism of action for these therapeutic effects is poorly understood but is thought to involve the resident growth factors present in near term amniotic tissue. To determine the relative cytokine contribution of the amnion and chorion in amniotic allografts, the content of 18 cytokines involved in wound healing were measured in samples of PURION® Processed dehydrated amnion, chorion, and amnion/chorion membrane (dHACM) grafts by multiplex enzyme-linked immunosorbent assay array. Both amnion and chorion contained similar amounts of each factor when normalized per dry weight; however, when calculated per surface area of tissue applied to a wound, amnion contained on average only 25% as much of each factor as the chorion. Therefore, an allograft containing both amnion and chorion would contain four to five times more cytokine than a single layer amnion allograft alone. Both single layer amnion and multilayer allografts containing amnion and chorion are currently marketed for wound repair. To examine the role of tissue processing technique in cytokine retention, cytokine contents in representative dehydrated single layer wound care products were measured. The results demonstrated that cytokine content varied significantly among the allografts tested, and that PURION® Processed single layer amnion grafts contained more cytokines than other single layer products. These results suggest that PURION® Processed dHACM contains substantially more cytokines than single layer amnion products, and therefore dHACM may be more effective at delivering growth factors to a healing wound than amnion alone. © 2014 Wiley Periodicals, Inc.

Growing vertical ZnO nanorod arrays within graphite: efficient isolation of large size and high quality single-layer graphene.

PubMed

Ding, Ling; E, Yifeng; Fan, Louzhen; Yang, Shihe

2013-07-18

We report a unique strategy for efficiently exfoliating large size and high quality single-layer graphene directly from graphite into DMF dispersions by growing ZnO nanorod arrays between the graphene layers in graphite.

The Elephants' Graveyard: Constraints from Mantle Plumes on the Fate of Subducted Slabs and Implications for the Style of Mantle Convection

NASA Astrophysics Data System (ADS)

Lassiter, J. C.

2007-12-01

The style of mantle convection (e.g., layered- vs. whole-mantle convection) is one of the most hotly contested questions in the Geological Sciences. Geochemical arguments for and against mantle layering have largely focused on mass-balance evidence for the existence of "hidden" geochemical reservoirs. However, the size and location of such reservoirs are largely unconstrained, and most geochemical arguments for mantle layering are consistent with a depleted mantle comprising most of the mantle mass and a comparatively small volume of enriched, hidden material either within D" or within seismically anomalous "piles" beneath southern Africa and the South Pacific. The mass flux associated with subduction of oceanic lithosphere is large and plate subduction is an efficient driver of convective mixing in the mantle. Therefore, the depth to which oceanic lithosphere descends into the mantle is effectively the depth of the upper mantle in any layered mantle model. Numerous geochemical studies provide convincing evidence that many mantle plumes contain material which at one point resided close to the Earth's surface (e.g., recycled oceanic crust ± sediments, possibly subduction-modified mantle wedge material). Fluid dynamic models further reveal that only the central cores of mantle plumes are involved in melt generation. The presence of recycled material in the sources of many ocean island basalts therefore cannot be explained by entrainment of this material during plume ascent, but requires that recycled material resides within or immediately above the thermo-chemical boundary layer(s) that generates mantle plumes. More recent Os- isotope studies of mantle xenoliths from OIB settings reveal the presence not only of recycled crust in mantle plumes, but also ancient melt-depleted harzburgite interpreted to represent ancient recycled oceanic lithosphere [1]. Thus, there is increasing evidence that subducted slabs accumulate in the boundary layer(s) that provide the source of mantle plumes, as suggested 25 years ago by Hofmann & White [2]. Determination of the depth of origin of mantle plumes would provide a 1st-order constraint on the depth of plate subduction and the volume of the "upper" mantle. Improved seismic techniques and deployment of OBS arrays may soon allow robust imaging of mantle plumes in the deep mantle, although preliminary results are controversial [3]. Detection of a conclusive geochemical signature of core/mantle interaction would also provide strong evidence for a deep origin of mantle plumes, although there is considerable debate as to what such a signature would entail. In summary, determination of the depth of origin of mantle plumes may provide the key to deciphering the fate of subducted slabs and the overall style of mantle convection. Although this problem remains unresolved after several decades of work, recent developments in both geophysics and geochemistry provide hope for a final resolution within the next 10 years. [1] M Bizimis, M Griselin, JC Lassiter, VJM Salters, G Sen, EPSL 257, 259-293, 2007. [2] AW Hofmann, WM White, EPSL 57, 421-436, 1982. [3] R Montelli, G Nolet, F Dahlens, G Masters, E Engdahl, S-H Hung, Science 303, 338-343, 2004.

Long Non-Coding RNAs Regulating Immunity in Insects

PubMed Central

Satyavathi, Valluri; Ghosh, Rupam; Subramanian, Srividya

2017-01-01

Recent advances in modern technology have led to the understanding that not all genetic information is coded into protein and that the genomes of each and every organism including insects produce non-coding RNAs that can control different biological processes. Among RNAs identified in the last decade, long non-coding RNAs (lncRNAs) represent a repertoire of a hidden layer of internal signals that can regulate gene expression in physiological, pathological, and immunological processes. Evidence shows the importance of lncRNAs in the regulation of host–pathogen interactions. In this review, an attempt has been made to view the role of lncRNAs regulating immune responses in insects. PMID:29657286

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Evidence against a chondritic Earth.

PubMed

Campbell, Ian H; O'Neill, Hugh St C

2012-03-28

The (142)Nd/(144)Nd ratio of the Earth is greater than the solar ratio as inferred from chondritic meteorites, which challenges a fundamental assumption of modern geochemistry--that the composition of the silicate Earth is 'chondritic', meaning that it has refractory element ratios identical to those found in chondrites. The popular explanation for this and other paradoxes of mantle geochemistry, a hidden layer deep in the mantle enriched in incompatible elements, is inconsistent with the heat flux carried by mantle plumes. Either the matter from which the Earth formed was not chondritic, or the Earth has lost matter by collisional erosion in the later stages of planet formation.

Driver drowsiness detection using ANN image processing

NASA Astrophysics Data System (ADS)

Vesselenyi, T.; Moca, S.; Rus, A.; Mitran, T.; Tătaru, B.

2017-10-01

The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver’s eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.

Galaxy NGC 1448 with Active Galactic Nucleus

NASA Image and Video Library

2017-01-07

NGC 1448, a galaxy with an active galactic nucleus, is seen in this image combining data from the Carnegie-Irvine Galaxy Survey in the optical range and NuSTAR in the X-ray range. This galaxy contains an example of a supermassive black hole hidden by gas and dust. X-ray emissions from NGC 1448, as seen by NuSTAR and Chandra, suggests for the first time that, like IC 3639 in PIA21087, there must be a thick layer of gas and dust hiding the active black hole in this galaxy from our line of sight. http://photojournal.jpl.nasa.gov/catalog/PIA21086

Dark Gauge U(1) symmetry for an alternative left-right model

NASA Astrophysics Data System (ADS)

Kownacki, Corey; Ma, Ernest; Pollard, Nicholas; Popov, Oleg; Zakeri, Mohammadreza

2018-02-01

An alternative left-right model of quarks and leptons, where the SU(2)_R lepton doublet (ν ,l)_R is replaced with (n,l)_R so that n_R is not the Dirac mass partner of ν _L, has been known since 1987. Previous versions assumed a global U(1)_S symmetry to allow n to be identified as a dark-matter fermion. We propose here a gauge extension by the addition of extra fermions to render the model free of gauge anomalies, and just one singlet scalar to break U(1)_S. This results in two layers of dark matter, one hidden behind the other.

Compacted dimensions and singular plasmonic surfaces.

PubMed

Pendry, J B; Huidobro, Paloma Arroyo; Luo, Yu; Galiffi, Emanuele

2017-11-17

In advanced field theories, there can be more than four dimensions to space, the excess dimensions described as compacted and unobservable on everyday length scales. We report a simple model, unconnected to field theory, for a compacted dimension realized in a metallic metasurface periodically structured in the form of a grating comprising a series of singularities. An extra dimension of the grating is hidden, and the surface plasmon excitations, though localized at the surface, are characterized by three wave vectors rather than the two of typical two-dimensional metal grating. We propose an experimental realization in a doped graphene layer. Copyright © 2017, American Association for the Advancement of Science.

Single water channels of aquaporin-1 do not obey the Kedem-Katchalsky equations.

PubMed

Curry, M R; Shachar-Hill, B; Hill, A E

2001-05-15

The Kedem-Katchalsky (KK) equations are often used to obtain information about the osmotic properties and conductance of channels to water. Using human red cell membranes, in which the osmotic flow is dominated by Aquaporin-1, we show here that compared to NaCl the reflexion coefficient of the channel for methylurea, when corrected for solute volume exchange and for the water permeability of the lipid membrane, is 0.54. The channels are impermeable to these two solutes which would seem to rule out flow interaction and require a reflexion coefficient close to 1.0 for both. Thus, two solutes can give very different osmotic flow rates through a semi-permeable pore, a result at variance with both classical theory and the KK formulation. The use of KK equations to analyze osmotic volume changes, which results in a single hybrid reflexion coefficient for each solute, may explain the discrepancy in the literature between such results and those where the equations have not been employed. Osmotic reflexion coefficients substantially different from 1.0 cannot be ascribed to the participation of other 'hidden' parallel aqueous channels consistently with known properties of the membrane. Furthermore, we show that this difference cannot be due to second-order effects, such as a solute-specific interaction with water in only part of the channel, because the osmosis is linear with driving force down to zero solute concentration, a finding which also rules out the involvement of unstirred-layer effects. Reflexion coefficients smaller than 1.0 do not necessitate water-solute flow interaction in permeable aqueous channels; rather, the osmotic behaviour of impermeable molecular-sized pores can be explained by differences in the fundamental nature of water flow in regions either accessible or inaccessible to solute, created by a varying cross-section of the channel.

Heating up the Galaxy with hidden photons

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

Dubovsky, Sergei; Hernández-Chifflet, Guzmán, E-mail: dubovsky@nyu.edu, E-mail: ghc236@nyu.edu

2015-12-01

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

Heating up the Galaxy with hidden photons

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

Dubovsky, Sergei; Hernández-Chifflet, Guzmán; Instituto de Física, Facultad de Ingeniería, Universidad de la República,Montevideo, 11300

2015-12-29

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

Experimental non-classicality of an indivisible quantum system.

PubMed

Lapkiewicz, Radek; Li, Peizhe; Schaeff, Christoph; Langford, Nathan K; Ramelow, Sven; Wieśniak, Marcin; Zeilinger, Anton

2011-06-22

In contrast to classical physics, quantum theory demands that not all properties can be simultaneously well defined; the Heisenberg uncertainty principle is a manifestation of this fact. Alternatives have been explored--notably theories relying on joint probability distributions or non-contextual hidden-variable models, in which the properties of a system are defined independently of their own measurement and any other measurements that are made. Various deep theoretical results imply that such theories are in conflict with quantum mechanics. Simpler cases demonstrating this conflict have been found and tested experimentally with pairs of quantum bits (qubits). Recently, an inequality satisfied by non-contextual hidden-variable models and violated by quantum mechanics for all states of two qubits was introduced and tested experimentally. A single three-state system (a qutrit) is the simplest system in which such a contradiction is possible; moreover, the contradiction cannot result from entanglement between subsystems, because such a three-state system is indivisible. Here we report an experiment with single photonic qutrits which provides evidence that no joint probability distribution describing the outcomes of all possible measurements--and, therefore, no non-contextual theory--can exist. Specifically, we observe a violation of the Bell-type inequality found by Klyachko, Can, Binicioğlu and Shumovsky. Our results illustrate a deep incompatibility between quantum mechanics and classical physics that cannot in any way result from entanglement.

Erosion Performance of Gadolinium Zirconate-Based Thermal Barrier Coatings Processed by Suspension Plasma Spray

NASA Astrophysics Data System (ADS)

Mahade, Satyapal; Curry, Nicholas; Björklund, Stefan; Markocsan, Nicolaie; Nylén, Per; Vaßen, Robert

2017-01-01

7-8 wt.% Yttria-stabilized zirconia (YSZ) is the standard thermal barrier coating (TBC) material used by the gas turbines industry due to its excellent thermal and thermo-mechanical properties up to 1200 °C. The need for improvement in gas turbine efficiency has led to an increase in the turbine inlet gas temperature. However, above 1200 °C, YSZ has issues such as poor sintering resistance, poor phase stability and susceptibility to calcium magnesium alumino silicates (CMAS) degradation. Gadolinium zirconate (GZ) is considered as one of the promising top coat candidates for TBC applications at high temperatures (>1200 °C) due to its low thermal conductivity, good sintering resistance and CMAS attack resistance. Single-layer 8YSZ, double-layer GZ/YSZ and triple-layer GZdense/GZ/YSZ TBCs were deposited by suspension plasma spray (SPS) process. Microstructural analysis was carried out by scanning electron microscopy (SEM). A columnar microstructure was observed in the single-, double- and triple-layer TBCs. Phase analysis of the as-sprayed TBCs was carried out using XRD (x-ray diffraction) where a tetragonal prime phase of zirconia in the single-layer YSZ TBC and a cubic defect fluorite phase of GZ in the double and triple-layer TBCs was observed. Porosity measurements of the as-sprayed TBCs were made by water intrusion method and image analysis method. The as-sprayed GZ-based multi-layered TBCs were subjected to erosion test at room temperature, and their erosion resistance was compared with single-layer 8YSZ. It was shown that the erosion resistance of 8YSZ single-layer TBC was higher than GZ-based multi-layered TBCs. Among the multi-layered TBCs, triple-layer TBC was slightly better than double layer in terms of erosion resistance. The eroded TBCs were cold-mounted and analyzed by SEM.

Strong anisotropy and magnetostriction in the two-dimensional Stoner ferromagnet Fe 3 GeTe 2

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

Zhuang, Houlong L.; Kent, P. R. C.; Hennig, Richard G.

Comore » mputationally characterizing magnetic properies of novel two-dimensional (2D) materials serves as an important first step of exploring possible applications. Using density-functional theory, we show that single-layer Fe 3 GeTe 2 is a potential 2D material with sufficiently low formation energy to be synthesized by mechanical exfoliation from the bulk phase with a van der Waals layered structure. In addition, we calculated the phonon dispersion demonstrating that single-layer Fe 3 GeTe 2 is dynamically stable. Furthermore, we find that similar to the bulk phase, 2D Fe 3 GeTe 2 exhibits amagnetic moment that originates from a Stoner instability. In contrast to other 2D materials, we find that single-layer Fe 3 GeTe 2 exhibits a significant uniaxial magnetocrystalline anisotropy energy of 920μ eV per Fe atom originating from spin-orbit coupling. In conclusion, we show that applying biaxial tensile strains enhances the anisotropy energy, which reveals strong magnetostriction in single-layer Fe 3 GeTe 2 with a sizable magneostrictive coefficient. Our results indicate that single-layer Fe 3 GeTe 2 is potentially useful for magnetic storage applications.« less

Strong anisotropy and magnetostriction in the two-dimensional Stoner ferromagnet Fe 3 GeTe 2

DOE PAGES

Zhuang, Houlong L.; Kent, P. R. C.; Hennig, Richard G.

2016-04-06

Comore » mputationally characterizing magnetic properies of novel two-dimensional (2D) materials serves as an important first step of exploring possible applications. Using density-functional theory, we show that single-layer Fe 3 GeTe 2 is a potential 2D material with sufficiently low formation energy to be synthesized by mechanical exfoliation from the bulk phase with a van der Waals layered structure. In addition, we calculated the phonon dispersion demonstrating that single-layer Fe 3 GeTe 2 is dynamically stable. Furthermore, we find that similar to the bulk phase, 2D Fe 3 GeTe 2 exhibits amagnetic moment that originates from a Stoner instability. In contrast to other 2D materials, we find that single-layer Fe 3 GeTe 2 exhibits a significant uniaxial magnetocrystalline anisotropy energy of 920μ eV per Fe atom originating from spin-orbit coupling. In conclusion, we show that applying biaxial tensile strains enhances the anisotropy energy, which reveals strong magnetostriction in single-layer Fe 3 GeTe 2 with a sizable magneostrictive coefficient. Our results indicate that single-layer Fe 3 GeTe 2 is potentially useful for magnetic storage applications.« less

High quantum efficiency and low dark count rate in multi-layer superconducting nanowire single-photon detectors

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

Jafari Salim, A., E-mail: ajafaris@uwaterloo.ca; Eftekharian, A.; University of Waterloo, Waterloo, Ontario N2L 3G1

In this paper, we theoretically show that a multi-layer superconducting nanowire single-photon detector (SNSPD) is capable of approaching characteristics of an ideal SNSPD in terms of the quantum efficiency, dark count, and band-width. A multi-layer structure improves the performance in two ways. First, the potential barrier for thermally activated vortex crossing, which is the major source of dark counts and the reduction of the critical current in SNSPDs is elevated. In a multi-layer SNSPD, a vortex is made of 2D-pancake vortices that form a stack. It will be shown that the stack of pancake vortices effectively experiences a larger potentialmore » barrier compared to a vortex in a single-layer SNSPD. This leads to an increase in the experimental critical current as well as significant decrease in the dark count rate. In consequence, an increase in the quantum efficiency for photons of the same energy or an increase in the sensitivity to photons of lower energy is achieved. Second, a multi-layer structure improves the efficiency of single-photon absorption by increasing the effective optical thickness without compromising the single-photon sensitivity.« less

Effect of Enhanced Thermal Stability of Alumina Support Layer on Growth of Vertically Aligned Single-Walled Carbon Nanotubes and Their Application in Nanofiltration Membranes.

PubMed

In, Jung Bin; Cho, Kang Rae; Tran, Tung Xuan; Kim, Seok-Min; Wang, Yinmin; Grigoropoulos, Costas P; Noy, Aleksandr; Fornasiero, Francesco

2018-06-07

We investigate the thermal stability of alumina supporting layers sputtered at different conditions and its effect on the growth of aligned single-walled carbon nanotube arrays. Radio frequency magnetron sputtering of alumina under oxygen-argon atmosphere produces a Si-rich alumina alloy film on a silicon substrate. Atomic force microscopy on the annealed catalysts reveals that Si-rich alumina films are more stable than alumina layers with low Si content at the elevated temperatures at which the growth of single-walled carbon nanotubes is initiated. The enhanced thermal stability of the Si-rich alumina layer results in a narrower (< 2.2 nm) diameter distribution of the single-walled carbon nanotubes. Thanks to the smaller diameters of their nanotube pores, membranes fabricated with vertically aligned nanotubes grown on the stable layers display improved ion selectivity.

Effect of Enhanced Thermal Stability of Alumina Support Layer on Growth of Vertically Aligned Single-Walled Carbon Nanotubes and Their Application in Nanofiltration Membranes

NASA Astrophysics Data System (ADS)

In, Jung Bin; Cho, Kang Rae; Tran, Tung Xuan; Kim, Seok-Min; Wang, Yinmin; Grigoropoulos, Costas P.; Noy, Aleksandr; Fornasiero, Francesco

2018-06-01

We investigate the thermal stability of alumina supporting layers sputtered at different conditions and its effect on the growth of aligned single-walled carbon nanotube arrays. Radio frequency magnetron sputtering of alumina under oxygen-argon atmosphere produces a Si-rich alumina alloy film on a silicon substrate. Atomic force microscopy on the annealed catalysts reveals that Si-rich alumina films are more stable than alumina layers with low Si content at the elevated temperatures at which the growth of single-walled carbon nanotubes is initiated. The enhanced thermal stability of the Si-rich alumina layer results in a narrower (< 2.2 nm) diameter distribution of the single-walled carbon nanotubes. Thanks to the smaller diameters of their nanotube pores, membranes fabricated with vertically aligned nanotubes grown on the stable layers display improved ion selectivity.

Transmission electron microscopy study of the formation of epitaxial CoSi2/Si (111) by a room-temperature codeposition technique

NASA Technical Reports Server (NTRS)

D'Anterroches, Cecile; Yakupoglu, H. Nejat; Lin, T. L.; Fathauer, R. W.; Grunthaner, P. J.

1988-01-01

Co and Si have been codeposited on Si (111) substrates near room temperature in a stoichiometric 1:2 ratio in a molecular beam epitaxy system. Annealing of these deposits yields high-quality single-crystal CoSi2 layers. Transmission electron microscopy has been used to examine as-deposited layers and layers annealed at 300, 500, and 600 C. Single-crystal epitaxial grains of CoSi2 embedded in a matrix of amorphous Co/Si are observed in as-deposited samples, while the layer is predominantly single-crystal, inhomogeneously strained CoSi2 at 300 C. At 600 C, a homogeneously strained single-crystal layer with a high density of pinholes is observed. In contrast to other solid phase epitaxy techniques used to grow CoSi2 on Si (111), no intermediate silicide phases are observed prior to the formation of CoSi2.

THRESHOLD LOGIC SYNTHESIS OF SEQUENTIAL MACHINES.

DTIC Science & Technology

The application of threshold logic to the design of sequential machines is the subject of this research. A single layer of threshold logic units in...advantages of fewer components because of the use of threshold logic, along with very high-speed operation resulting from the use of only a single layer of...logic. In some instances, namely for asynchronous machines, the only delay need be the natural delay of the single layer of threshold elements. It is

Self-induced inverse spin-Hall effect in an iron and a cobalt single-layer films themselves under the ferromagnetic resonance

NASA Astrophysics Data System (ADS)

Kanagawa, Kazunari; Teki, Yoshio; Shikoh, Eiji

2018-05-01

The inverse spin-Hall effect (ISHE) is produced even in a "single-layer" ferromagnetic material film. Previously, the self-induced ISHE in a Ni80Fe20 film under the ferromagnetic resonance (FMR) was discovered. In this study, we observed an electromotive force (EMF) in an iron (Fe) and a cobalt (Co) single-layer films themselves under the FMR. As origins of the EMFs in the films themselves, the ISHE was main for Fe and dominant for Co, respectively 2 and 18 times larger than the anomalous Hall effect. Thus, we demonstrated the self-induced ISHE in an Fe and a Co single-layer films themselves under the FMR.

A metasurface carpet cloak for electromagnetic, acoustic and water waves.

PubMed

Yang, Yihao; Wang, Huaping; Yu, Faxin; Xu, Zhiwei; Chen, Hongsheng

2016-01-29

We propose a single low-profile skin metasurface carpet cloak to hide objects with arbitrary shape and size under three different waves, i.e., electromagnetic (EM) waves, acoustic waves and water waves. We first present a metasurface which can control the local reflection phase of these three waves. By taking advantage of this metasurface, we then design a metasurface carpet cloak which provides an additional phase to compensate the phase distortion introduced by a bump, thus restoring the reflection waves as if the incident waves impinge onto a flat mirror. The finite element simulation results demonstrate that an object can be hidden under these three kinds of waves with a single metasurface cloak.

Foundations of Quantum Mechanics: recent developments at INRIM

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

Genovese, Marco; Piacentini, Fabrizio

2011-09-23

This paper's purpose is to show some experiments performed in the 'Carlo Novero' labs of the Optics Division of the National Institute of Metrological Research (INRIM, Torino, Italy) in the last years, aiming to discriminate between Standard Quantum Mechanics and some specific, restricted class of Hidden Variable Theories (HVTs).The first experiment, realized in two different configurations, will perform the Alicki - Van Ryn non-classicality test on single particles, in our specific case heralded single photons. The second experiment instead will be on the testing of two restricted Local Realistic Theories (LRTs), properly built to describe polarization entangled photons experiments, whosemore » inequalities are not affected by the detection loophole.« less

Exascale Virtualized and Programmable Distributed Cyber Resource Control: Final Scientific Technical Report

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

Yoo, S.J.Ben; Lauer, Gregory S.

Extreme-science drives the need for distributed exascale processing and communications that are carefully, yet flexibly, managed. Exponential growth of data for scientific simulations, experimental data, collaborative data analyses, remote visualization and GRID computing requirements of scientists in fields as diverse as high energy physics, climate change, genomics, fusion, synchrotron radiation, material science, medicine, and other scientific disciplines cannot be accommodated by simply applying existing transport protocols to faster pipes. Further, scientific challenges today demand diverse research teams, heightening the need for and increasing the complexity of collaboration. To address these issues within the network layer and physical layer, we havemore » performed a number of research activities surrounding effective allocation and management of elastic optical network (EON) resources, particularly focusing on FlexGrid transponders. FlexGrid transponders support the opportunity to build Layer-1 connections at a wide range of bandwidths and to reconfigure them rapidly. The new flexibility supports complex new ways of using the physical layer that must be carefully managed and hidden from the scientist end-users. FlexGrid networks utilize flexible (or elastic) spectral bandwidths for each data link without using fixed wavelength grids. The flexibility in spectrum allocation brings many appealing features to network operations. Current networks are designed for the worst case impairments in transmission performance and the assigned spectrum is over-provisioned. In contrast, the FlexGrid networks can operate with the highest spectral efficiency and minimum bandwidth for the given traffic demand while meeting the minimum quality of transmission (QoT) requirement. Two primary focuses of our research are: (1) resource and spectrum allocation (RSA) for IP traffic over EONs, and (2) RSA for cross-domain optical networks. Previous work concentrates primarily on large file transfers within a single domain. Adding support for IP traffic changes the nature of the RSA problem: instead of choosing to accept or deny each request for network support, IP traffic is inherently elastic and thus lends itself to a bandwidth maximization formulation. We developed a number of algorithms that could be easily deployed within existing and new FlexGrid networks, leading to networks that better support scientific collaboration. Cross-domain RSA research is essential to support large-scale FlexGrid networks, since configuration information is generally not shared or coordinated across domains. The results presented here are in their early stages. They are technically feasible and practical, but still require coordination among organizations and equipment owners and a higher-layer framework for managing network requests.« less

"It's Not Always What It Seems": Exploring the Hidden Curriculum within a Doctoral Program

ERIC Educational Resources Information Center

Foot, Rachel Elizabeth

2017-01-01

The purpose of this qualitative, naturalistic study was to explore the ways in which hidden curriculum might influence doctoral student success. Two questions guided the study: (a) How do doctoral students experience the hidden curriculum? (b) What forms of hidden curricula can be identified in a PhD program? Data were collected from twelve…

Hidden Farmworker Labor Camps in North Carolina: An Indicator of Structural Vulnerability

PubMed Central

Summers, Phillip; Quandt, Sara A.; Talton, Jennifer W.; Galván, Leonardo

2015-01-01

Objectives. We used geographic information systems (GIS) to delineate whether farmworker labor camps were hidden and to determine whether hidden camps differed from visible camps in terms of physical and resident characteristics. Methods. We collected data using observation, interview, and public domain GIS data for 180 farmworker labor camps in east central North Carolina. A hidden camp was defined as one that was at least 0.15 miles from an all-weather road or located behind natural or manufactured objects. Hidden camps were compared with visible camps in terms of physical and resident characteristics. Results. More than one third (37.8%) of the farmworker labor camps were hidden. Hidden camps were significantly larger (42.7% vs 17.0% with 21 or more residents; P ≤ .001; and 29.4% vs 13.5% with 3 or more dwellings; P = .002) and were more likely to include barracks (50% vs 19.6%; P ≤ .001) than were visible camps. Conclusions. Poor housing conditions in farmworker labor camps often go unnoticed because they are hidden in the rural landscape, increasing farmworker vulnerability. Policies that promote greater community engagement with farmworker labor camp residents to reduce structural vulnerability should be considered. PMID:26469658

Organic photovoltaic devices with a single layer geometry (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kolesov, Vladimir A.; Fuentes-Hernandez, Canek; Aizawa, Naoya; Larrain, Felipe A.; Chou, Wen-Fang; Perrotta, Alberto; Graham, Samuel; Kippelen, Bernard

2016-09-01

Organic photovoltaics (OPV) can lead to a low cost and short energy payback time alternative to existing photovoltaic technologies. However, to fulfill this promise, power conversion efficiencies must be improved and simultaneously the architecture of the devices and their processing steps need to be further simplified. In the most efficient devices to date, the functions of photocurrent generation, and hole/electron collection are achieved in different layers adding complexity to the device fabrication. In this talk, we present a novel approach that yields devices in which all these functions are combined in a single layer. Specifically, we report on bulk heterojunction devices in which amine-containing polymers are first mixed in the solution together with the donor and acceptor materials that form the active layer. A single-layer coating yields a self-forming bottom electron-collection layer comprised of the amine-containing polymer (e.g. PEIE). Hole-collection is achieved by subsequent immersion of this single layer in a solution of a polyoxometalate (e.g. phosphomolybdic acid (PMA)) leading to an electrically p-doped region formed by the diffusion of the dopant molecules into the bulk. The depth of this doped region can be controlled with values up to tens of nm by varying the immersion time. Devices with a single 500 nm-thick active layer of P3HT:ICBA processed using this method yield power conversion efficiency (PCE) values of 4.8 ± 0.3% at 1 sun and demonstrate a performance level superior to that of benchmark three-layer devices with separate layers of PEIE/P3HT:ICBA/MoOx (4.1 ± 0.4%). Devices remain stable after shelf lifetime experiments carried-out at 60 °C over 280 h.

Single-Band and Dual-Band Infrared Detectors

NASA Technical Reports Server (NTRS)

Ting, David Z. (Inventor); Gunapala, Sarath D. (Inventor); Soibel, Alexander (Inventor); Nguyen, Jean (Inventor); Khoshakhlagh, Arezou (Inventor)

2015-01-01

Bias-switchable dual-band infrared detectors and methods of manufacturing such detectors are provided. The infrared detectors are based on a back-to-back heterojunction diode design, where the detector structure consists of, sequentially, a top contact layer, a unipolar hole barrier layer, an absorber layer, a unipolar electron barrier, a second absorber, a second unipolar hole barrier, and a bottom contact layer. In addition, by substantially reducing the width of one of the absorber layers, a single-band infrared detector can also be formed.

Single-Band and Dual-Band Infrared Detectors

NASA Technical Reports Server (NTRS)

Ting, David Z. (Inventor); Gunapala, Sarath D. (Inventor); Soibel, Alexander (Inventor); Nguyen, Jean (Inventor); Khoshakhlagh, Arezou (Inventor)

2017-01-01

Bias-switchable dual-band infrared detectors and methods of manufacturing such detectors are provided. The infrared detectors are based on a back-to-back heterojunction diode design, where the detector structure consists of, sequentially, a top contact layer, a unipolar hole barrier layer, an absorber layer, a unipolar electron barrier, a second absorber, a second unipolar hole barrier, and a bottom contact layer. In addition, by substantially reducing the width of one of the absorber layers, a single-band infrared detector can also be formed.

Manganese oxide nanowires, films, and membranes and methods of making

DOEpatents

Suib, Steven Lawrence [Storrs, CT; Yuan, Jikang [Storrs, CT

2008-10-21

Nanowires, films, and membranes comprising ordered porous manganese oxide-based octahedral molecular sieves, and methods of making, are disclosed. A single crystal ultra-long nanowire includes an ordered porous manganese oxide-based octahedral molecular sieve, and has an average length greater than about 10 micrometers and an average diameter of about 5 nanometers to about 100 nanometers. A film comprises a microporous network comprising a plurality of single crystal nanowires in the form of a layer, wherein a plurality of layers is stacked on a surface of a substrate, wherein the nanowires of each layer are substantially axially aligned. A free standing membrane comprises a microporous network comprising a plurality of single crystal nanowires in the form of a layer, wherein a plurality of layers is aggregately stacked, and wherein the nanowires of each layer are substantially axially aligned.

Zero velocity interval detection based on a continuous hidden Markov model in micro inertial pedestrian navigation

NASA Astrophysics Data System (ADS)

Sun, Wei; Ding, Wei; Yan, Huifang; Duan, Shunli

2018-06-01

Shoe-mounted pedestrian navigation systems based on micro inertial sensors rely on zero velocity updates to correct their positioning errors in time, which effectively makes determining the zero velocity interval play a key role during normal walking. However, as walking gaits are complicated, and vary from person to person, it is difficult to detect walking gaits with a fixed threshold method. This paper proposes a pedestrian gait classification method based on a hidden Markov model. Pedestrian gait data are collected with a micro inertial measurement unit installed at the instep. On the basis of analyzing the characteristics of the pedestrian walk, a single direction angular rate gyro output is used to classify gait features. The angular rate data are modeled into a univariate Gaussian mixture model with three components, and a four-state left–right continuous hidden Markov model (CHMM) is designed to classify the normal walking gait. The model parameters are trained and optimized using the Baum–Welch algorithm and then the sliding window Viterbi algorithm is used to decode the gait. Walking data are collected through eight subjects walking along the same route at three different speeds; the leave-one-subject-out cross validation method is conducted to test the model. Experimental results show that the proposed algorithm can accurately detect different walking gaits of zero velocity interval. The location experiment shows that the precision of CHMM-based pedestrian navigation improved by 40% when compared to the angular rate threshold method.

Zipf exponent of trajectory distribution in the hidden Markov model

NASA Astrophysics Data System (ADS)

Bochkarev, V. V.; Lerner, E. Yu

2014-03-01

This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.