Apollo

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

Beckingsal, David; Gamblin, Todd

Modern performance portability frameworks provide application developers with a flexible way to determine how to run application kernels, however, they provide no guidance as to the best configuration for a given kernel. Apollo provides a model-generation framework that, when integrated with the RAJA library, uses lightweight decision tree models to select the fastest execution configuration on a per-kernel basis

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Analysis of the cable equation with non-local and non-singular kernel fractional derivative

NASA Astrophysics Data System (ADS)

Karaagac, Berat

2018-02-01

Recently a new concept of differentiation was introduced in the literature where the kernel was converted from non-local singular to non-local and non-singular. One of the great advantages of this new kernel is its ability to portray fading memory and also well defined memory of the system under investigation. In this paper the cable equation which is used to develop mathematical models of signal decay in submarine or underwater telegraphic cables will be analysed using the Atangana-Baleanu fractional derivative due to the ability of the new fractional derivative to describe non-local fading memory. The existence and uniqueness of the more generalized model is presented in detail via the fixed point theorem. A new numerical scheme is used to solve the new equation. In addition, stability, convergence and numerical simulations are presented.

Lévy processes on a generalized fractal comb

NASA Astrophysics Data System (ADS)

Sandev, Trifce; Iomin, Alexander; Méndez, Vicenç

2016-09-01

Comb geometry, constituted of a backbone and fingers, is one of the most simple paradigm of a two-dimensional structure, where anomalous diffusion can be realized in the framework of Markov processes. However, the intrinsic properties of the structure can destroy this Markovian transport. These effects can be described by the memory and spatial kernels. In particular, the fractal structure of the fingers, which is controlled by the spatial kernel in both the real and the Fourier spaces, leads to the Lévy processes (Lévy flights) and superdiffusion. This generalization of the fractional diffusion is described by the Riesz space fractional derivative. In the framework of this generalized fractal comb model, Lévy processes are considered, and exact solutions for the probability distribution functions are obtained in terms of the Fox H-function for a variety of the memory kernels, and the rate of the superdiffusive spreading is studied by calculating the fractional moments. For a special form of the memory kernels, we also observed a competition between long rests and long jumps. Finally, we considered the fractal structure of the fingers controlled by a Weierstrass function, which leads to the power-law kernel in the Fourier space. This is a special case, when the second moment exists for superdiffusion in this competition between long rests and long jumps.

Note: A simple picture of subdiffusive polymer motion from stochastic simulations

NASA Astrophysics Data System (ADS)

Gniewek, Pawel; Kolinski, Andrzej

2011-02-01

Entangled polymer solutions and melts exhibit unusual frictional properties. In the entanglement limit self-diffusion coefficient of long flexible polymers decays with the second power of chain length and viscosity increases with 3-3.5 power of chain length.1 It is very difficult to provide detailed molecular-level explanation of the entanglement effect.2 Perhaps, the problem of many entangled polymer chains is the most complex multibody issue of classical physics. There are different approaches to polymer melt dynamics. Some of these recognize hydrodynamic interactions as a dominant term, while topological constraints for polymer chains are assumed as a secondary factor. Other theories consider the topological constraints as the most important factors controlling polymer dynamics. Herman and co-workers describe polymer dynamics in melts, as a lateral sliding of a chain along other chains until complete mutual disentanglement. Despite the success in explaining the power-laws for viscosity, the model has some limitations. First of all, memory effects are ignored, that is, polymer segments are treated independently. Also, each entanglement/obstacle is treated as a separate entity, which is certainly a simplification of the memory effect problem. In addition to that, correlated motions of segments are addressed within the framework of renormalized Rouse-chain theory,7 without calling any topological entanglements in advance. This approach leads to the generalized Langevin equation characterized by distinct memory kernels describing local and nonlocal segment correlations or to the Smoluchowski equation in which the segments' mobility is treated as a stochastic variable.11 Both models describe the polymer segments motion at a microscopic level. An interesting alternative is to solve the integrodifferential equation for the chain relaxation with a sophisticated kernel function.12 The design of the kernel function is based on a mesoscopic description of the polymer melt. These theories explain some experimental data, although the description of the crossover between the Rouse and non-Rouse behavior is not satisfactory. Obviously, within the scope of a short note we cannot review all theoretical concepts of the polymer melt dynamics. Here we focus just on the interpretation of the observed single segment autocorrelation function.

An O(N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle

2016-08-01

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.

An O( N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

DOE PAGES

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; ...

2016-08-10

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O( N 2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Methodmore » (FMM) to evaluate the integrals in O( N) operations, with O( N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. Lastly, the results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.« less

Memory behaviors of entropy production rates in heat conduction

NASA Astrophysics Data System (ADS)

Li, Shu-Nan; Cao, Bing-Yang

2018-02-01

Based on the relaxation time approximation and first-order expansion, memory behaviors in heat conduction are found between the macroscopic and Boltzmann-Gibbs-Shannon (BGS) entropy production rates with exponentially decaying memory kernels. In the frameworks of classical irreversible thermodynamics (CIT) and BGS statistical mechanics, the memory dependency on the integrated history is unidirectional, while for the extended irreversible thermodynamics (EIT) and BGS entropy production rates, the memory dependences are bidirectional and coexist with the linear terms. When macroscopic and microscopic relaxation times satisfy a specific relationship, the entropic memory dependences will be eliminated. There also exist initial effects in entropic memory behaviors, which decay exponentially. The second-order term are also discussed, which can be understood as the global non-equilibrium degree. The effects of the second-order term are consisted of three parts: memory dependency, initial value and linear term. The corresponding memory kernels are still exponential and the initial effects of the global non-equilibrium degree also decay exponentially.

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

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

Li, Zhen; Bian, Xin; Karniadakis, George Em, E-mail: george-karniadakis@brown.edu

2015-12-28

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while themore » corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model.« less

Short Message Service (SMS) Command and Control (C2) Awareness in Android-based Smartphones Using Kernel-Level Auditing

DTIC Science & Technology

2012-06-14

Display 480 x 800 pixels (3.7 inches) CPU Qualcomm QSD8250 1GHz Memory (internal) 512MB RAM / 512 MB ROM Kernel version 2.6.35.7-ge0fb012 Figure 3.5: HTC...development and writing). The 34 MSM kernel provided by the AOSP and compatible with the HTC Nexus One’s motherboard and Qualcomm chipset, is used for this...building the kernel is having the prebuilt toolchains and the right kernel for the hardware. Many HTC products use Qualcomm processors which uses the

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Experiences modeling ocean circulation problems on a 30 node commodity cluster with 3840 GPU processor cores.

NASA Astrophysics Data System (ADS)

Hill, C.

2008-12-01

Low cost graphic cards today use many, relatively simple, compute cores to deliver support for memory bandwidth of more than 100GB/s and theoretical floating point performance of more than 500 GFlop/s. Right now this performance is, however, only accessible to highly parallel algorithm implementations that, (i) can use a hundred or more, 32-bit floating point, concurrently executing cores, (ii) can work with graphics memory that resides on the graphics card side of the graphics bus and (iii) can be partially expressed in a language that can be compiled by a graphics programming tool. In this talk we describe our experiences implementing a complete, but relatively simple, time dependent shallow-water equations simulation targeting a cluster of 30 computers each hosting one graphics card. The implementation takes into account the considerations (i), (ii) and (iii) listed previously. We code our algorithm as a series of numerical kernels. Each kernel is designed to be executed by multiple threads of a single process. Kernels are passed memory blocks to compute over which can be persistent blocks of memory on a graphics card. Each kernel is individually implemented using the NVidia CUDA language but driven from a higher level supervisory code that is almost identical to a standard model driver. The supervisory code controls the overall simulation timestepping, but is written to minimize data transfer between main memory and graphics memory (a massive performance bottle-neck on current systems). Using the recipe outlined we can boost the performance of our cluster by nearly an order of magnitude, relative to the same algorithm executing only on the cluster CPU's. Achieving this performance boost requires that many threads are available to each graphics processor for execution within each numerical kernel and that the simulations working set of data can fit into the graphics card memory. As we describe, this puts interesting upper and lower bounds on the problem sizes for which this technology is currently most useful. However, many interesting problems fit within this envelope. Looking forward, we extrapolate our experience to estimate full-scale ocean model performance and applicability. Finally we describe preliminary hybrid mixed 32-bit and 64-bit experiments with graphics cards that support 64-bit arithmetic, albeit at a lower performance.

ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

NASA Astrophysics Data System (ADS)

Shao, Rui; Zhong, Sheng; Yan, Luxin

2015-12-01

Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

Bessel function expansion to reduce the calculation time and memory usage for cylindrical computer-generated holograms.

PubMed

Sando, Yusuke; Barada, Daisuke; Jackin, Boaz Jessie; Yatagai, Toyohiko

2017-07-10

This study proposes a method to reduce the calculation time and memory usage required for calculating cylindrical computer-generated holograms. The wavefront on the cylindrical observation surface is represented as a convolution integral in the 3D Fourier domain. The Fourier transformation of the kernel function involving this convolution integral is analytically performed using a Bessel function expansion. The analytical solution can drastically reduce the calculation time and the memory usage without any cost, compared with the numerical method using fast Fourier transform to Fourier transform the kernel function. In this study, we present the analytical derivation, the efficient calculation of Bessel function series, and a numerical simulation. Furthermore, we demonstrate the effectiveness of the analytical solution through comparisons of calculation time and memory usage.

Control Transfer in Operating System Kernels

DTIC Science & Technology

1994-05-13

microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating

Embedded real-time operating system micro kernel design

NASA Astrophysics Data System (ADS)

Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng

2005-12-01

Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Ling, Fan

2013-01-01

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels. PMID:23666108

An energy efficient and high speed architecture for convolution computing based on binary resistive random access memory

NASA Astrophysics Data System (ADS)

Liu, Chen; Han, Runze; Zhou, Zheng; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

2018-04-01

In this work we present a novel convolution computing architecture based on metal oxide resistive random access memory (RRAM) to process the image data stored in the RRAM arrays. The proposed image storage architecture shows performances of better speed-device consumption efficiency compared with the previous kernel storage architecture. Further we improve the architecture for a high accuracy and low power computing by utilizing the binary storage and the series resistor. For a 28 × 28 image and 10 kernels with a size of 3 × 3, compared with the previous kernel storage approach, the newly proposed architecture shows excellent performances including: 1) almost 100% accuracy within 20% LRS variation and 90% HRS variation; 2) more than 67 times speed boost; 3) 71.4% energy saving.

A high performance parallel algorithm for 1-D FFT

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

Agarwal, R.C.; Gustavson, F.G.; Zubair, M.

1994-12-31

In this paper the authors propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. They use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. They show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. They implementedmore » this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.« less

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

Lei, Huan; Baker, Nathan A.; Li, Xiantao

We present a data-driven approach to determine the memory kernel and random noise of the generalized Langevin equation. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. Further, we show that such an approximation can be constructed to arbitrarily high order. Within these approximations, the generalized Langevin dynamics can be embedded in an extended stochastic model without memory. We demonstrate how to introduce the stochastic noise so that the fluctuation-dissipation theorem is exactly satisfied.

Application of fractional derivative with exponential law to bi-fractional-order wave equation with frictional memory kernel

NASA Astrophysics Data System (ADS)

Cuahutenango-Barro, B.; Taneco-Hernández, M. A.; Gómez-Aguilar, J. F.

2017-12-01

Analytical solutions of the wave equation with bi-fractional-order and frictional memory kernel of Mittag-Leffler type are obtained via Caputo-Fabrizio fractional derivative in the Liouville-Caputo sense. Through the method of separation of variables and Laplace transform method we derive closed-form solutions and establish fundamental solutions. Special cases with homogeneous Dirichlet boundary conditions and nonhomogeneous initial conditions, as well as for the external force are considered. Numerical simulations of the special solutions were done and novel behaviors are obtained.

Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Theodoridis, Sergios

2008-12-01

Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposing a closed ball (convex set) constraint on the norm of the classifiers. This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS). To cope with the inherent memory limitations of online systems and to embed tracking capabilities to the design, an upper bound on the dimension of the linear subspaces is imposed. The underlying principle of the design is the notion of projection mappings. Classification is performed by metric projection mappings, sparsification is achieved by orthogonal projections, while the online system's memory requirements and tracking are attained by oblique projections. The resulting sparsification scheme shows strong similarities with the classical sliding window adaptive schemes. The proposed design is validated by the adaptive equalization problem of a nonlinear communication channel, and is compared with classical and recent stochastic gradient descent techniques, as well as with the APSM's solution where sparsification is performed by a closed ball constraint on the norm of the classifiers.

A survey of kernel-type estimators for copula and their applications

NASA Astrophysics Data System (ADS)

Sumarjaya, I. W.

2017-10-01

Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

Mathematical inference in one point microrheology

NASA Astrophysics Data System (ADS)

Hohenegger, Christel; McKinley, Scott

2016-11-01

Pioneered by the work of Mason and Weitz, one point passive microrheology has been successfully applied to obtaining estimates of the loss and storage modulus of viscoelastic fluids when the mean-square displacement obeys a local power law. Using numerical simulations of a fluctuating viscoelastic fluid model, we study the problem of recovering the mechanical parameters of the fluid's memory kernel using statistical inference like mean-square displacements and increment auto-correlation functions. Seeking a better understanding of the influence of the assumptions made in the inversion process, we mathematically quantify the uncertainty in traditional one point microrheology for simulated data and demonstrate that a large family of memory kernels yields the same statistical signature. We consider both simulated data obtained from a full viscoelastic fluid simulation of the unsteady Stokes equations with fluctuations and from a Generalized Langevin Equation of the particle's motion described by the same memory kernel. From the theory of inverse problems, we propose an alternative method that can be used to recover information about the loss and storage modulus and discuss its limitations and uncertainties. NSF-DMS 1412998.

Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach

NASA Astrophysics Data System (ADS)

Yan, Wen; Shelley, Michael

2018-02-01

An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions. In most popular packages such periodicities are imposed with the hierarchical repetition of periodic boxes, which may give an incorrect answer due to the conditional convergence of some kernel sums. Here we present an efficient method to properly impose periodic boundary conditions using a near-far splitting scheme. The near-field contribution is directly calculated with the KIFMM method, while the far-field contribution is calculated with a multipole-to-local (M2L) operator which is independent of the source and target point distribution. The M2L operator is constructed with the far-field portion of the kernel function to generate the far-field contribution with the downward equivalent source points in KIFMM. This method guarantees the sum of the near-field & far-field converge pointwise to results satisfying periodicity and compatibility conditions. The computational cost of the far-field calculation observes the same O (N) complexity as FMM and is designed to be small by reusing the data computed by KIFMM for the near-field. The far-field calculations require no additional control parameters, and observes the same theoretical error bound as KIFMM. We present accuracy and timing test results for the Laplace kernel in singly periodic domains and the Stokes velocity kernel in doubly and triply periodic domains.

The Impact of Software Structure and Policy on CPU and Memory System Performance

DTIC Science & Technology

1994-05-01

Mach 3.0 is that Ultrix is a monolithic or integrated system, and Mach 3.0 is a microkernel or kernelized system. In a monolithic system, all system...services are implemented in a single system context, the monolithic kernel . In a microkernel system such as Mach 3.0, primitive abstractions such as...separate protection domain as a server. Many current operating system text books discuss microkernel and monolithic kernel design. (See [17, 73, 77].) The

Data-driven parameterization of the generalized Langevin equation

DOE PAGES

Lei, Huan; Baker, Nathan A.; Li, Xiantao

2016-11-29

We present a data-driven approach to determine the memory kernel and random noise of the generalized Langevin equation. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. Further, we show that such an approximation can be constructed to arbitrarily high order. Within these approximations, the generalized Langevin dynamics can be embedded in an extended stochastic model without memory. We demonstrate how to introduce the stochastic noise so that the fluctuation-dissipation theorem is exactly satisfied.

Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics.

PubMed

van den Broek, Karina; Kuhn, Hubert; Zielesny, Achim

2018-05-21

Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems.

Lossy Wavefield Compression for Full-Waveform Inversion

NASA Astrophysics Data System (ADS)

Boehm, C.; Fichtner, A.; de la Puente, J.; Hanzich, M.

2015-12-01

We present lossy compression techniques, tailored to the inexact computation of sensitivity kernels, that significantly reduce the memory requirements of adjoint-based minimization schemes. Adjoint methods are a powerful tool to solve tomography problems in full-waveform inversion (FWI). Yet they face the challenge of massive memory requirements caused by the opposite directions of forward and adjoint simulations and the necessity to access both wavefields simultaneously during the computation of the sensitivity kernel. Thus, storage, I/O operations, and memory bandwidth become key topics in FWI. In this talk, we present strategies for the temporal and spatial compression of the forward wavefield. This comprises re-interpolation with coarse time steps and an adaptive polynomial degree of the spectral element shape functions. In addition, we predict the projection errors on a hierarchy of grids and re-quantize the residuals with an adaptive floating-point accuracy to improve the approximation. Furthermore, we use the first arrivals of adjoint waves to identify "shadow zones" that do not contribute to the sensitivity kernel at all. Updating and storing the wavefield within these shadow zones is skipped, which reduces memory requirements and computational costs at the same time. Compared to check-pointing, our approach has only a negligible computational overhead, utilizing the fact that a sufficiently accurate sensitivity kernel does not require a fully resolved forward wavefield. Furthermore, we use adaptive compression thresholds during the FWI iterations to ensure convergence. Numerical experiments on the reservoir scale and for the Western Mediterranean prove the high potential of this approach with an effective compression factor of 500-1000. Furthermore, it is computationally cheap and easy to integrate in both, finite-differences and finite-element wave propagation codes.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

An OSKit-Based Implementation of Least Privilege Separation Kernel Memory Partitioning

DTIC Science & Technology

2007-06-01

Fiasco is developed at TU Dresden. It is compatible with the x86 L4 microkernel . It is a real-time preemptive kernel written in C++. Fiasco’s...adherence to the specification of the L4 microkernel makes it a desirable choice. The downside is Fiasco is too specialized for what is needed by the

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Stochastic quantization of (λϕ4)d scalar theory: Generalized Langevin equation with memory kernel

NASA Astrophysics Data System (ADS)

Menezes, G.; Svaiter, N. F.

2007-02-01

The method of stochastic quantization for a scalar field theory is reviewed. A brief survey for the case of self-interacting scalar field, implementing the stochastic perturbation theory up to the one-loop level, is presented. Then, it is introduced a colored random noise in the Einstein's relations, a common prescription employed by one of the stochastic regularizations, to control the ultraviolet divergences of the theory. This formalism is extended to the case where a Langevin equation with a memory kernel is used. It is shown that, maintaining the Einstein's relations with a colored noise, there is convergence to a non-regularized theory.

Numerical integration of the extended variable generalized Langevin equation with a positive Prony representable memory kernel.

PubMed

Baczewski, Andrew D; Bond, Stephen D

2013-07-28

Generalized Langevin dynamics (GLD) arise in the modeling of a number of systems, ranging from structured fluids that exhibit a viscoelastic mechanical response, to biological systems, and other media that exhibit anomalous diffusive phenomena. Molecular dynamics (MD) simulations that include GLD in conjunction with external and/or pairwise forces require the development of numerical integrators that are efficient, stable, and have known convergence properties. In this article, we derive a family of extended variable integrators for the Generalized Langevin equation with a positive Prony series memory kernel. Using stability and error analysis, we identify a superlative choice of parameters and implement the corresponding numerical algorithm in the LAMMPS MD software package. Salient features of the algorithm include exact conservation of the first and second moments of the equilibrium velocity distribution in some important cases, stable behavior in the limit of conventional Langevin dynamics, and the use of a convolution-free formalism that obviates the need for explicit storage of the time history of particle velocities. Capability is demonstrated with respect to accuracy in numerous canonical examples, stability in certain limits, and an exemplary application in which the effect of a harmonic confining potential is mapped onto a memory kernel.

On the Asymptotic Behavior of the Kernel Function in the Generalized Langevin Equation: A One-Dimensional Lattice Model

NASA Astrophysics Data System (ADS)

Chu, Weiqi; Li, Xiantao

2018-01-01

We present some estimates for the memory kernel function in the generalized Langevin equation, derived using the Mori-Zwanzig formalism from a one-dimensional lattice model, in which the particles interactions are through nearest and second nearest neighbors. The kernel function can be explicitly expressed in a matrix form. The analysis focuses on the decay properties, both spatially and temporally, revealing a power-law behavior in both cases. The dependence on the level of coarse-graining is also studied.

A flexible, extendable, modular and computationally efficient approach to scattering-integral-based seismic full waveform inversion

NASA Astrophysics Data System (ADS)

Schumacher, F.; Friederich, W.; Lamara, S.

2016-02-01

We present a new conceptual approach to scattering-integral-based seismic full waveform inversion (FWI) that allows a flexible, extendable, modular and both computationally and storage-efficient numerical implementation. To achieve maximum modularity and extendability, interactions between the three fundamental steps carried out sequentially in each iteration of the inversion procedure, namely, solving the forward problem, computing waveform sensitivity kernels and deriving a model update, are kept at an absolute minimum and are implemented by dedicated interfaces. To realize storage efficiency and maximum flexibility, the spatial discretization of the inverted earth model is allowed to be completely independent of the spatial discretization employed by the forward solver. For computational efficiency reasons, the inversion is done in the frequency domain. The benefits of our approach are as follows: (1) Each of the three stages of an iteration is realized by a stand-alone software program. In this way, we avoid the monolithic, unflexible and hard-to-modify codes that have often been written for solving inverse problems. (2) The solution of the forward problem, required for kernel computation, can be obtained by any wave propagation modelling code giving users maximum flexibility in choosing the forward modelling method. Both time-domain and frequency-domain approaches can be used. (3) Forward solvers typically demand spatial discretizations that are significantly denser than actually desired for the inverted model. Exploiting this fact by pre-integrating the kernels allows a dramatic reduction of disk space and makes kernel storage feasible. No assumptions are made on the spatial discretization scheme employed by the forward solver. (4) In addition, working in the frequency domain effectively reduces the amount of data, the number of kernels to be computed and the number of equations to be solved. (5) Updating the model by solving a large equation system can be done using different mathematical approaches. Since kernels are stored on disk, it can be repeated many times for different regularization parameters without need to solve the forward problem, making the approach accessible to Occam's method. Changes of choice of misfit functional, weighting of data and selection of data subsets are still possible at this stage. We have coded our approach to FWI into a program package called ASKI (Analysis of Sensitivity and Kernel Inversion) which can be applied to inverse problems at various spatial scales in both Cartesian and spherical geometries. It is written in modern FORTRAN language using object-oriented concepts that reflect the modular structure of the inversion procedure. We validate our FWI method by a small-scale synthetic study and present first results of its application to high-quality seismological data acquired in the southern Aegean.

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

The Modularized Software Package ASKI - Full Waveform Inversion Based on Waveform Sensitivity Kernels Utilizing External Seismic Wave Propagation Codes

NASA Astrophysics Data System (ADS)

Schumacher, F.; Friederich, W.

2015-12-01

We present the modularized software package ASKI which is a flexible and extendable toolbox for seismic full waveform inversion (FWI) as well as sensitivity or resolution analysis operating on the sensitivity matrix. It utilizes established wave propagation codes for solving the forward problem and offers an alternative to the monolithic, unflexible and hard-to-modify codes that have typically been written for solving inverse problems. It is available under the GPL at www.rub.de/aski. The Gauss-Newton FWI method for 3D-heterogeneous elastic earth models is based on waveform sensitivity kernels and can be applied to inverse problems at various spatial scales in both Cartesian and spherical geometries. The kernels are derived in the frequency domain from Born scattering theory as the Fréchet derivatives of linearized full waveform data functionals, quantifying the influence of elastic earth model parameters on the particular waveform data values. As an important innovation, we keep two independent spatial descriptions of the earth model - one for solving the forward problem and one representing the inverted model updates. Thereby we account for the independent needs of spatial model resolution of forward and inverse problem, respectively. Due to pre-integration of the kernels over the (in general much coarser) inversion grid, storage requirements for the sensitivity kernels are dramatically reduced.ASKI can be flexibly extended to other forward codes by providing it with specific interface routines that contain knowledge about forward code-specific file formats and auxiliary information provided by the new forward code. In order to sustain flexibility, the ASKI tools must communicate via file output/input, thus large storage capacities need to be accessible in a convenient way. Storing the complete sensitivity matrix to file, however, permits the scientist full manual control over each step in a customized procedure of sensitivity/resolution analysis and full waveform inversion.

Efficient High Performance Collective Communication for Distributed Memory Environments

ERIC Educational Resources Information Center

Ali, Qasim

2009-01-01

Collective communication allows efficient communication and synchronization among a collection of processes, unlike point-to-point communication that only involves a pair of communicating processes. Achieving high performance for both kernels and full-scale applications running on a distributed memory system requires an efficient implementation of…

Developing infrared array controller with software real time operating system

NASA Astrophysics Data System (ADS)

Sako, Shigeyuki; Miyata, Takashi; Nakamura, Tomohiko; Motohara, Kentaro; Uchimoto, Yuka Katsuno; Onaka, Takashi; Kataza, Hirokazu

2008-07-01

Real-time capabilities are required for a controller of a large format array to reduce a dead-time attributed by readout and data transfer. The real-time processing has been achieved by dedicated processors including DSP, CPLD, and FPGA devices. However, the dedicated processors have problems with memory resources, inflexibility, and high cost. Meanwhile, a recent PC has sufficient resources of CPUs and memories to control the infrared array and to process a large amount of frame data in real-time. In this study, we have developed an infrared array controller with a software real-time operating system (RTOS) instead of the dedicated processors. A Linux PC equipped with a RTAI extension and a dual-core CPU is used as a main computer, and one of the CPU cores is allocated to the real-time processing. A digital I/O board with DMA functions is used for an I/O interface. The signal-processing cores are integrated in the OS kernel as a real-time driver module, which is composed of two virtual devices of the clock processor and the frame processor tasks. The array controller with the RTOS realizes complicated operations easily, flexibly, and at a low cost.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Experiences in autotuning matrix multiplication for energy minimization on GPUs

DOE PAGES

Anzt, Hartwig; Haugen, Blake; Kurzak, Jakub; ...

2015-05-20

In this study, we report extensive results and analysis of autotuning the computationally intensive graphics processing units kernel for dense matrix–matrix multiplication in double precision. In contrast to traditional autotuning and/or optimization for runtime performance only, we also take the energy efficiency into account. For kernels achieving equal performance, we show significant differences in their energy balance. We also identify the memory throughput as the most influential metric that trades off performance and energy efficiency. Finally, as a result, the performance optimal case ends up not being the most efficient kernel in overall resource use.

Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

NASA Astrophysics Data System (ADS)

Komatitsch, Dimitri; Xie, Zhinan; Bozdaǧ, Ebru; Sales de Andrade, Elliott; Peter, Daniel; Liu, Qinya; Tromp, Jeroen

2016-09-01

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Fusion PIC code performance analysis on the Cori KNL system

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

Koskela, Tuomas S.; Deslippe, Jack; Friesen, Brian

We study the attainable performance of Particle-In-Cell codes on the Cori KNL system by analyzing a miniature particle push application based on the fusion PIC code XGC1. We start from the most basic building blocks of a PIC code and build up the complexity to identify the kernels that cost the most in performance and focus optimization efforts there. Particle push kernels operate at high AI and are not likely to be memory bandwidth or even cache bandwidth bound on KNL. Therefore, we see only minor benefits from the high bandwidth memory available on KNL, and achieving good vectorization ismore » shown to be the most beneficial optimization path with theoretical yield of up to 8x speedup on KNL. In practice we are able to obtain up to a 4x gain from vectorization due to limitations set by the data layout and memory latency.« less

Collective Langevin dynamics of conformational motions in proteins

NASA Astrophysics Data System (ADS)

Lange, Oliver F.; Grubmüller, Helmut

2006-06-01

Functionally relevant slow conformational motions of proteins are, at present, in most cases inaccessible to molecular dynamics (MD) simulations. The main reason is that the major part of the computational effort is spend for the accurate description of a huge number of high frequency motions of the protein and the surrounding solvent. The accumulated influence of these fluctuations is crucial for a correct treatment of the conformational dynamics; however, their details can be considered irrelevant for most purposes. To accurately describe long time protein dynamics we here propose a reduced dimension approach, collective Langevin dynamics (CLD), which evolves the dynamics of the system within a small subspace of relevant collective degrees of freedom. The dynamics within the low-dimensional conformational subspace is evolved via a generalized Langevin equation which accounts for memory effects via memory kernels also extracted from short explicit MD simulations. To determine the memory kernel with differing levels of regularization, we propose and evaluate two methods. As a first test, CLD is applied to describe the conformational motion of the peptide neurotensin. A drastic dimension reduction is achieved by considering one single curved conformational coordinate. CLD yielded accurate thermodynamical and dynamical behaviors. In particular, the rate of transitions between two conformational states agreed well with a rate obtained from a 150ns reference molecular dynamics simulation, despite the fact that the time scale of the transition (˜50ns) was much longer than the 1ns molecular dynamics simulation from which the memory kernel was extracted.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

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

Agarwal, Sapan; Quach, Tu -Thach; Parekh, Ojas

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-basedmore » architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.« less

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

DOE PAGES

Agarwal, Sapan; Quach, Tu -Thach; Parekh, Ojas; ...

2016-01-06

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-basedmore » architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.« less

Combining neural networks and signed particles to simulate quantum systems more efficiently

NASA Astrophysics Data System (ADS)

Sellier, Jean Michel

2018-04-01

Recently a new formulation of quantum mechanics has been suggested which describes systems by means of ensembles of classical particles provided with a sign. This novel approach mainly consists of two steps: the computation of the Wigner kernel, a multi-dimensional function describing the effects of the potential over the system, and the field-less evolution of the particles which eventually create new signed particles in the process. Although this method has proved to be extremely advantageous in terms of computational resources - as a matter of fact it is able to simulate in a time-dependent fashion many-body systems on relatively small machines - the Wigner kernel can represent the bottleneck of simulations of certain systems. Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system. In this work, we introduce a new technique which drastically reduces the computation time and memory requirement to simulate time-dependent quantum systems which is based on the use of an appropriately tailored neural network combined with the signed particle formalism. In particular, the suggested neural network is able to compute efficiently and reliably the Wigner kernel without any training as its entire set of weights and biases is specified by analytical formulas. As a consequence, the amount of memory for quantum simulations radically drops since the kernel does not need to be stored anymore as it is now computed by the neural network itself, only on the cells of the (discretized) phase-space which are occupied by particles. As its is clearly shown in the final part of this paper, not only this novel approach drastically reduces the computational time, it also remains accurate. The author believes this work opens the way towards effective design of quantum devices, with incredible practical implications.

A Frequency-Domain Implementation of a Sliding-Window Traffic Sign Detector for Large Scale Panoramic Datasets

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; De With, P. H. N.

2013-10-01

In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detection stage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which is employed for traffic sign detection. Sliding-window object detectors often use a linear SVM to classify the features in a window. In this case, the classification can be seen as a convolution of the feature maps with the SVM kernel. It is well known that convolution can be efficiently implemented in the frequency domain, for kernels larger than a certain size. We show that by careful reordering of sliding-window operations, most of the frequency-domain transformations can be eliminated, leading to a substantial increase in efficiency. Additionally, we suggest to use the overlap-add method to keep the memory use within reasonable bounds. This allows us to keep all the transformed kernels in memory, thereby eliminating even more domain transformations, and allows all scales in a multiscale pyramid to be processed using the same set of transformed kernels. For a typical sliding-window implementation, we have found that the detector execution performance improves with a factor of 5.3. As a bonus, many of the detector improvements from literature, e.g. chi-squared kernel approximations, sub-class splitting algorithms etc., can be more easily applied at a lower performance penalty because of an improved scalability.

Gaussian process regression for geometry optimization

NASA Astrophysics Data System (ADS)

Denzel, Alexander; Kästner, Johannes

2018-03-01

We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent.

PubMed

Hu, En-Liang; Kwok, James T

2015-09-01

Nonparametric kernel learning (NPKL) is a flexible approach to learn the kernel matrix directly without assuming any parametric form. It can be naturally formulated as a semidefinite program (SDP), which, however, is not very scalable. To address this problem, we propose the combined use of low-rank approximation and block coordinate descent (BCD). Low-rank approximation avoids the expensive positive semidefinite constraint in the SDP by replacing the kernel matrix variable with V(T)V, where V is a low-rank matrix. The resultant nonlinear optimization problem is then solved by BCD, which optimizes each column of V sequentially. It can be shown that the proposed algorithm has nice convergence properties and low computational complexities. Experiments on a number of real-world data sets show that the proposed algorithm outperforms state-of-the-art NPKL solvers.

Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters

PubMed Central

Torres-Huitzil, Cesar

2013-01-01

Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with a k × k kernel requires of k 2 − 1 comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel size k. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on 1024 × 1024 images with up to 255 × 255 kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding. PMID:24288456

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

PubMed

Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

2016-11-01

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects. Copyright © 2016 Crop Science Society of America.

Does money matter in inflation forecasting?

NASA Astrophysics Data System (ADS)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Benchmarking NWP Kernels on Multi- and Many-core Processors

NASA Astrophysics Data System (ADS)

Michalakes, J.; Vachharajani, M.

2008-12-01

Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.

On the non-stationary generalized Langevin equation

NASA Astrophysics Data System (ADS)

Meyer, Hugues; Voigtmann, Thomas; Schilling, Tanja

2017-12-01

In molecular dynamics simulations and single molecule experiments, observables are usually measured along dynamic trajectories and then averaged over an ensemble ("bundle") of trajectories. Under stationary conditions, the time-evolution of such averages is described by the generalized Langevin equation. By contrast, if the dynamics is not stationary, it is not a priori clear which form the equation of motion for an averaged observable has. We employ the formalism of time-dependent projection operator techniques to derive the equation of motion for a non-equilibrium trajectory-averaged observable as well as for its non-stationary auto-correlation function. The equation is similar in structure to the generalized Langevin equation but exhibits a time-dependent memory kernel as well as a fluctuating force that implicitly depends on the initial conditions of the process. We also derive a relation between this memory kernel and the autocorrelation function of the fluctuating force that has a structure similar to a fluctuation-dissipation relation. In addition, we show how the choice of the projection operator allows us to relate the Taylor expansion of the memory kernel to data that are accessible in MD simulations and experiments, thus allowing us to construct the equation of motion. As a numerical example, the procedure is applied to Brownian motion initialized in non-equilibrium conditions and is shown to be consistent with direct measurements from simulations.

Methods for compressible fluid simulation on GPUs using high-order finite differences

NASA Astrophysics Data System (ADS)

Pekkilä, Johannes; Väisälä, Miikka S.; Käpylä, Maarit J.; Käpylä, Petri J.; Anjum, Omer

2017-08-01

We focus on implementing and optimizing a sixth-order finite-difference solver for simulating compressible fluids on a GPU using third-order Runge-Kutta integration. Since graphics processing units perform well in data-parallel tasks, this makes them an attractive platform for fluid simulation. However, high-order stencil computation is memory-intensive with respect to both main memory and the caches of the GPU. We present two approaches for simulating compressible fluids using 55-point and 19-point stencils. We seek to reduce the requirements for memory bandwidth and cache size in our methods by using cache blocking and decomposing a latency-bound kernel into several bandwidth-bound kernels. Our fastest implementation is bandwidth-bound and integrates 343 million grid points per second on a Tesla K40t GPU, achieving a 3 . 6 × speedup over a comparable hydrodynamics solver benchmarked on two Intel Xeon E5-2690v3 processors. Our alternative GPU implementation is latency-bound and achieves the rate of 168 million updates per second.

Translational Approaches Targeting Reconsolidation

PubMed Central

Kroes, Marijn C.W.; LeDoux, Joseph E.; Phelps, Elizabeth A.

2017-01-01

Maladaptive learned responses and memories contribute to psychiatric disorders that constitute a significant socio-economic burden. Primary treatment methods teach patients to inhibit maladaptive responses, but do not get rid of the memory itself, which explains why many patients experience a return of symptoms even after initially successful treatment. This highlights the need to discover more persistent and robust techniques to diminish maladaptive learned behaviours. One potentially promising approach is to alter the original memory, as opposed to inhibiting it, by targeting memory reconsolidation. Recent research shows that reactivating an old memory results in a period of memory flexibility and requires restorage, or reconsolidation, for the memory to persist. This reconsolidation period allows a window for modification of a specific old memory. Renewal of memory flexibility following reactivation holds great clinical potential as it enables targeting reconsolidation and changing of specific learned responses and memories that contribute to maladaptive mental states and behaviours. Here, we will review translational research on non-human animals, healthy human subjects, and clinical populations aimed at altering memories by targeting reconsolidation using biological treatments (electrical stimulation, noradrenergic antagonists) or behavioural interference (reactivation–extinction paradigm). Both approaches have been used successfully to modify aversive and appetitive memories, yet effectiveness in treating clinical populations has been limited. We will discuss that memory flexibility depends on the type of memory tested and the brain regions that underlie specific types of memory. Further, when and how we can most effectively reactivate a memory and induce flexibility is largely unclear. Finally, the development of drugs that can target reconsolidation and are safe for use in humans would optimize cross-species translations. Increasing the understanding of the mechanism and limitations of memory flexibility upon reactivation should help optimize efficacy of treatments for psychiatric patients. PMID:27240676

Flexible and twistable non-volatile memory cell array with all-organic one diode-one resistor architecture.

PubMed

Ji, Yongsung; Zeigler, David F; Lee, Dong Su; Choi, Hyejung; Jen, Alex K-Y; Ko, Heung Cho; Kim, Tae-Wook

2013-01-01

Flexible organic memory devices are one of the integral components for future flexible organic electronics. However, high-density all-organic memory cell arrays on malleable substrates without cross-talk have not been demonstrated because of difficulties in their fabrication and relatively poor performances to date. Here we demonstrate the first flexible all-organic 64-bit memory cell array possessing one diode-one resistor architectures. Our all-organic one diode-one resistor cell exhibits excellent rewritable switching characteristics, even during and after harsh physical stresses. The write-read-erase-read output sequence of the cells perfectly correspond to the external pulse signal regardless of substrate deformation. The one diode-one resistor cell array is clearly addressed at the specified cells and encoded letters based on the standard ASCII character code. Our study on integrated organic memory cell arrays suggests that the all-organic one diode-one resistor cell architecture is suitable for high-density flexible organic memory applications in the future.

Neural Mechanisms of Episodic Retrieval Support Divergent Creative Thinking.

PubMed

Madore, Kevin P; Thakral, Preston P; Beaty, Roger E; Addis, Donna Rose; Schacter, Daniel L

2017-11-17

Prior research has indicated that brain regions and networks that support semantic memory, top-down and bottom-up attention, and cognitive control are all involved in divergent creative thinking. Kernels of evidence suggest that neural processes supporting episodic memory-the retrieval of particular elements of prior experiences-may also be involved in divergent thinking, but such processes have typically been characterized as not very relevant for, or even a hindrance to, creative output. In the present study, we combine functional magnetic resonance imaging with an experimental manipulation to test formally, for the first time, episodic memory's involvement in divergent thinking. Following a manipulation that facilitates detailed episodic retrieval, we observed greater neural activity in the hippocampus and stronger connectivity between a core brain network linked to episodic processing and a frontoparietal brain network linked to cognitive control during divergent thinking relative to an object association control task that requires little divergent thinking. Stronger coupling following the retrieval manipulation extended to a subsequent resting-state scan. Neural effects of the episodic manipulation were consistent with behavioral effects of enhanced idea production on divergent thinking but not object association. The results indicate that conceptual frameworks should accommodate the idea that episodic retrieval can function as a component process of creative idea generation, and highlight how the brain flexibly utilizes the retrieval of episodic details for tasks beyond simple remembering. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A message passing kernel for the hypercluster parallel processing test bed

NASA Technical Reports Server (NTRS)

Blech, Richard A.; Quealy, Angela; Cole, Gary L.

1989-01-01

A Message-Passing Kernel (MPK) for the Hypercluster parallel-processing test bed is described. The Hypercluster is being developed at the NASA Lewis Research Center to support investigations of parallel algorithms and architectures for computational fluid and structural mechanics applications. The Hypercluster resembles the hypercube architecture except that each node consists of multiple processors communicating through shared memory. The MPK efficiently routes information through the Hypercluster, using a message-passing protocol when necessary and faster shared-memory communication whenever possible. The MPK also interfaces all of the processors with the Hypercluster operating system (HYCLOPS), which runs on a Front-End Processor (FEP). This approach distributes many of the I/O tasks to the Hypercluster processors and eliminates the need for a separate I/O support program on the FEP.

Analysis of Drude model using fractional derivatives without singular kernels

NASA Astrophysics Data System (ADS)

Jiménez, Leonardo Martínez; García, J. Juan Rosales; Contreras, Abraham Ortega; Baleanu, Dumitru

2017-11-01

We report study exploring the fractional Drude model in the time domain, using fractional derivatives without singular kernels, Caputo-Fabrizio (CF), and fractional derivatives with a stretched Mittag-Leffler function. It is shown that the velocity and current density of electrons moving through a metal depend on both the time and the fractional order 0 < γ ≤ 1. Due to non-singular fractional kernels, it is possible to consider complete memory effects in the model, which appear neither in the ordinary model, nor in the fractional Drude model with Caputo fractional derivative. A comparison is also made between these two representations of the fractional derivatives, resulting a considered difference when γ < 0.8.

Rapid formation and flexible expression of memories of subliminal word pairs.

PubMed

Reber, Thomas P; Henke, Katharina

2011-01-01

Our daily experiences are incidentally and rapidly encoded as episodic memories. Episodic memories consist of numerous associations (e.g., who gave what to whom where and when) that can be expressed flexibly in new situations. Key features of episodic memory are speed of encoding, its associative nature, and its representational flexibility. Another defining feature of human episodic memory has been consciousness of encoding/retrieval. Here, we show that humans can rapidly form associations between subliminal words and minutes later retrieve these associations even if retrieval words were conceptually related to, but different from encoding words. Because encoding words were presented subliminally, associative encoding, and retrieval were unconscious. Unconscious association formation and retrieval were dependent on a preceding understanding of task principles. We conclude that key computations underlying episodic memory - rapid encoding and flexible expression of associations - can operate outside consciousness.

Working Memory and Cognitive Flexibility Mediates Visuoconstructional Abilities in Older Adults with Heterogeneous Cognitive Ability.

PubMed

Ávila, Rafaela T; de Paula, Jonas J; Bicalho, Maria A; Moraes, Edgar N; Nicolato, Rodrigo; Malloy-Diniz, Leandro F; Diniz, Breno S

2015-05-01

Previous studies suggest that executive functions influence the performance on visuoconstructional tasks. This study aims to investigate whether the relationship between planning ability and the copy of complex figures is mediated by distinct components of executive functions (i.e., working memory, inhibitory control and cognitive flexibility). We included a 129 older adults with Alzheimer's disease (n=36, AD), mild cognitive impairment (MCI, n=67), and with no evidence of cognitive impairment (controls, n=26). We evaluated the mediation effect of planning abilities, working memory, cognitive flexibility and inhibitory control on visuoconstructional tasks using a multiple mediation models. We found a significant direct effect of planning on visuoconstructional abilities and a partial mediation effect of working memory and cognitive flexibility on visuoconstructional abilities. The present results indicate that the performance on visuoconstructional task is mediated by multiple interrelated executive functions components, in particular working memory and cognitive flexibility.

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

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

Jin, Zheming; Yoshii, Kazutomo; Finkel, Hal

Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication andmore » kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.« less

RTOS kernel in portable electrocardiograph

NASA Astrophysics Data System (ADS)

Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

2011-12-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

Rapid Formation and Flexible Expression of Memories of Subliminal Word Pairs

PubMed Central

Reber, Thomas P.; Henke, Katharina

2011-01-01

Our daily experiences are incidentally and rapidly encoded as episodic memories. Episodic memories consist of numerous associations (e.g., who gave what to whom where and when) that can be expressed flexibly in new situations. Key features of episodic memory are speed of encoding, its associative nature, and its representational flexibility. Another defining feature of human episodic memory has been consciousness of encoding/retrieval. Here, we show that humans can rapidly form associations between subliminal words and minutes later retrieve these associations even if retrieval words were conceptually related to, but different from encoding words. Because encoding words were presented subliminally, associative encoding, and retrieval were unconscious. Unconscious association formation and retrieval were dependent on a preceding understanding of task principles. We conclude that key computations underlying episodic memory – rapid encoding and flexible expression of associations – can operate outside consciousness. PMID:22125545

Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression

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

Edwards, Richard E; Zhang, Hao; Parker, Lynne Edwards

2013-01-01

Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-foldmore » cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.« less

Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications

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

Ma, Wenjing; Krishnamoorthy, Sriram; Agrawal, Gagan

2012-05-15

Auto-tuning has emerged as an important practical method for creating highly optimized implementations of key computational kernels and applications. However, the growing complexity of architectures and applications is creating new challenges for auto-tuning. Complex applications can involve a prohibitively large search space that precludes empirical auto-tuning. Similarly, architectures are becoming increasingly complicated, making it hard to model performance. In this paper, we focus on the challenge to auto-tuning presented by applications with a large number of kernels and kernel instantiations. While these kernels may share a somewhat similar pattern, they differ considerably in problem sizes and the exact computation performed.more » We propose and evaluate a new approach to auto-tuning which we refer to as parameterized micro-benchmarking. It is an alternative to the two existing classes of approaches to auto-tuning: analytical model-based and empirical search-based. Particularly, we argue that the former may not be able to capture all the architectural features that impact performance, whereas the latter might be too expensive for an application that has several different kernels. In our approach, different expressions in the application, different possible implementations of each expression, and the key architectural features, are used to derive a simple micro-benchmark and a small parameter space. This allows us to learn the most significant features of the architecture that can impact the choice of implementation for each kernel. We have evaluated our approach in the context of GPU implementations of tensor contraction expressions encountered in excited state calculations in quantum chemistry. We have focused on two aspects of GPUs that affect tensor contraction execution: memory access patterns and kernel consolidation. Using our parameterized micro-benchmarking approach, we obtain a speedup of up to 2 over the version that used default optimizations, but no auto-tuning. We demonstrate that observations made from microbenchmarks match the behavior seen from real expressions. In the process, we make important observations about the memory hierarchy of two of the most recent NVIDIA GPUs, which can be used in other optimization frameworks as well.« less

An Adaptive Genetic Association Test Using Double Kernel Machines.

PubMed

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Guide wire extension for shape memory polymer occlusion removal devices

DOEpatents

Maitland, Duncan J [Pleasant Hill, CA; Small, IV, Ward; Hartman, Jonathan [Sacramento, CA

2009-11-03

A flexible extension for a shape memory polymer occlusion removal device. A shape memory polymer instrument is transported through a vessel via a catheter. A flexible elongated unit is operatively connected to the distal end of the shape memory polymer instrument to enhance maneuverability through tortuous paths en route to the occlusion.

Solution processed molecular floating gate for flexible flash memories

NASA Astrophysics Data System (ADS)

Zhou, Ye; Han, Su-Ting; Yan, Yan; Huang, Long-Biao; Zhou, Li; Huang, Jing; Roy, V. A. L.

2013-10-01

Solution processed fullerene (C60) molecular floating gate layer has been employed in low voltage nonvolatile memory device on flexible substrates. We systematically studied the charge trapping mechanism of the fullerene floating gate for both p-type pentacene and n-type copper hexadecafluorophthalocyanine (F16CuPc) semiconductor in a transistor based flash memory architecture. The devices based on pentacene as semiconductor exhibited both hole and electron trapping ability, whereas devices with F16CuPc trapped electrons alone due to abundant electron density. All the devices exhibited large memory window, long charge retention time, good endurance property and excellent flexibility. The obtained results have great potential for application in large area flexible electronic devices.

Solution processed molecular floating gate for flexible flash memories

PubMed Central

Zhou, Ye; Han, Su-Ting; Yan, Yan; Huang, Long-Biao; Zhou, Li; Huang, Jing; Roy, V. A. L.

2013-01-01

Solution processed fullerene (C60) molecular floating gate layer has been employed in low voltage nonvolatile memory device on flexible substrates. We systematically studied the charge trapping mechanism of the fullerene floating gate for both p-type pentacene and n-type copper hexadecafluorophthalocyanine (F16CuPc) semiconductor in a transistor based flash memory architecture. The devices based on pentacene as semiconductor exhibited both hole and electron trapping ability, whereas devices with F16CuPc trapped electrons alone due to abundant electron density. All the devices exhibited large memory window, long charge retention time, good endurance property and excellent flexibility. The obtained results have great potential for application in large area flexible electronic devices. PMID:24172758

Exact calculation of the time convolutionless master equation generator: Application to the nonequilibrium resonant level model

NASA Astrophysics Data System (ADS)

Kidon, Lyran; Wilner, Eli Y.; Rabani, Eran

2015-12-01

The generalized quantum master equation provides a powerful tool to describe the dynamics in quantum impurity models driven away from equilibrium. Two complementary approaches, one based on Nakajima-Zwanzig-Mori time-convolution (TC) and the other on the Tokuyama-Mori time-convolutionless (TCL) formulations provide a starting point to describe the time-evolution of the reduced density matrix. A key in both approaches is to obtain the so called "memory kernel" or "generator," going beyond second or fourth order perturbation techniques. While numerically converged techniques are available for the TC memory kernel, the canonical approach to obtain the TCL generator is based on inverting a super-operator in the full Hilbert space, which is difficult to perform and thus, nearly all applications of the TCL approach rely on a perturbative scheme of some sort. Here, the TCL generator is expressed using a reduced system propagator which can be obtained from system observables alone and requires the calculation of super-operators and their inverse in the reduced Hilbert space rather than the full one. This makes the formulation amenable to quantum impurity solvers or to diagrammatic techniques, such as the nonequilibrium Green's function. We implement the TCL approach for the resonant level model driven away from equilibrium and compare the time scales for the decay of the generator with that of the memory kernel in the TC approach. Furthermore, the effects of temperature, source-drain bias, and gate potential on the TCL/TC generators are discussed.

Novel procedure for characterizing nonlinear systems with memory: 2017 update

NASA Astrophysics Data System (ADS)

Nuttall, Albert H.; Katz, Richard A.; Hughes, Derke R.; Koch, Robert M.

2017-05-01

The present article discusses novel improvements in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra or 'NWV' algorithm is named for its principal contributors [1], [2],[ 3] . The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order and alleviate the Curse of Dimensionality (COD) in order to realize practical nonlinear solutions of scientific and engineering interest.

Bandlimited computerized improvements in characterization of nonlinear systems with memory

NASA Astrophysics Data System (ADS)

Nuttall, Albert H.; Katz, Richard A.; Hughes, Derke R.; Koch, Robert M.

2016-05-01

The present article discusses some inroads in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra 'NWV' algorithm is named for its principal contributors [1], [2],[ 3] over many years of developmental research. The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms on the system are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order in order to combat and reasonably alleviate the curse of dimensionality.

The role of verbal labels on flexible memory retrieval at 12-months of age.

PubMed

Taylor, Gemma; Liu, Hao; Herbert, Jane S

2016-11-01

The provision of verbal labels enhances 12-month-old infants' memory flexibility across a form change in a puppet imitation task (Herbert, 2011), although the mechanisms for this effect remain unclear. Here we investigate whether verbal labels can scaffold flexible memory retrieval when task difficulty increases and consider the mechanism responsible for the effect of language cues on early memory flexibility. Twelve-month-old infants were provided with English, Chinese, or empty language cues during a difficult imitation task, a combined change in the puppet's colour and form at the test (Hayne et al., 1997). Imitation performance by infants in the English language condition only exceeded baseline performance after the 10-min delay. Thus, verbal labels facilitated flexible memory retrieval on this task. There were no correlations between infants' language comprehension and imitation performance. Thus, it is likely that verbal labels facilitate both attention and categorisation during encoding and retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

PERK Regulates Working Memory and Protein Synthesis-Dependent Memory Flexibility

PubMed Central

Zhu, Siying; Henninger, Keely; McGrath, Barbara C.; Cavener, Douglas R.

2016-01-01

PERK (EIF2AK3) is an ER-resident eIF2α kinase required for memory flexibility and metabotropic glutamate receptor-dependent long-term depression, processes known to be dependent on new protein synthesis. Here we investigated PERK’s role in working memory, a cognitive ability that is independent of new protein synthesis, but instead is dependent on cellular Ca2+ dynamics. We found that working memory is impaired in forebrain-specific Perk knockout and pharmacologically PERK-inhibited mice. Moreover, inhibition of PERK in wild-type mice mimics the fear extinction impairment observed in forebrain-specific Perk knockout mice. Our findings reveal a novel role of PERK in cognitive functions and suggest that PERK regulates both Ca2+ -dependent working memory and protein synthesis-dependent memory flexibility. PMID:27627766

3D CSEM inversion based on goal-oriented adaptive finite element method

NASA Astrophysics Data System (ADS)

Zhang, Y.; Key, K.

2016-12-01

We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with complex geological scenarios by applying it to the inversion of synthetic marine controlled source EM data generated for a complex 3D offshore model with significant seafloor topography.

Working memory load affects repetitive behaviour but not cognitive flexibility in adolescent autism spectrum disorder.

PubMed

Wolff, Nicole; Chmielewski, Witold X; Beste, Christian; Roessner, Veit

2017-03-16

Autism spectrum disorder (ASD) is associated with repetitive and stereotyped behaviour, suggesting that cognitive flexibility may be deficient in ASD. A central, yet not examined aspect to understand possible deficits in flexible behaviour in ASD relates (i) to the role of working memory and (ii) to neurophysiological mechanisms underlying behavioural modulations. We analysed behavioural and neurophysiological (EEG) correlates of cognitive flexibility using a task-switching paradigm with and without working memory load in adolescents with ASD and typically developing controls (TD). Adolescents with ASD versus TD show similar performance in task switching with no memory load, indicating that 'pure' cognitive flexibility is not in deficit in adolescent ASD. However performance during task repetition decreases with increasing memory load. Neurophysiological data reflect the pattern of behavioural effects, showing modulations in P2 and P3 event-related potentials. Working memory demands affect repetitive behaviour while processes of cognitive flexibility are unaffected. Effects emerge due to deficits in preparatory attentional processes and deficits in task rule activation, organisation and implementation of task sets when repetitive behaviour is concerned. It may be speculated that the habitual response mode in ASD (i.e. repetitive behaviour) is particularly vulnerable to additional demands on executive control processes.

Design and Analysis of Architectures for Structural Health Monitoring Systems

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Sixto, S. L. (Technical Monitor)

2002-01-01

During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.

PERI - Auto-tuning Memory Intensive Kernels for Multicore

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

Bailey, David H; Williams, Samuel; Datta, Kaushik

2008-06-24

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we developmore » a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.« less

A flexible nonvolatile resistive switching memory device based on ZnO film fabricated on a foldable PET substrate.

PubMed

Sun, Bai; Zhang, Xuejiao; Zhou, Guangdong; Yu, Tian; Mao, Shuangsuo; Zhu, Shouhui; Zhao, Yong; Xia, Yudong

2018-06-15

In this work, a flexible resistive switching memory device based on ZnO film was fabricated using a foldable Polyethylene terephthalate (PET) film as substrate while Ag and Ti acts top and bottom electrode. Our as-prepared device represents an outstanding nonvolatile memory behavior with good "write-read-erase-read" stability at room temperature. Finally, a physical model of Ag conductive filament is constructed to understanding the observed memory characteristics. The work provides a new way for the preparation of flexible memory devices based on ZnO films, and especially provides an experimental basis for the exploration of high-performance and portable nonvolatile resistance random memory (RRAM). Copyright © 2018 Elsevier Inc. All rights reserved.

Memory Flexibility training (MemFlex) to reduce depressive symptomatology in individuals with major depressive disorder: study protocol for a randomised controlled trial.

PubMed

Hitchcock, Caitlin; Hammond, Emily; Rees, Catrin; Panesar, Inderpal; Watson, Peter; Werner-Seidler, Aliza; Dalgleish, Tim

2015-11-03

Major depressive disorder (MDD) is associated with chronic biases in the allocation of attention and recollection of personal memories. Impaired flexibility in attention and autobiographical memory retrieval is seen to both maintain current symptoms and predict future depression. Development of innovative interventions to reduce maladaptive cognitive patterns and improve cognitive flexibility in the domain of memory may therefore advance current treatment approaches for depression. Memory specificity training and cognitive bias modification techniques have both shown some promise in improving cognitive flexibility. Here we outline plans for a trial of an innovative memory flexibility training programme, MemFlex, which advances current training techniques with the aim of improving flexibility of autobiographical memory retrieval. This trial seeks to estimate the efficacy of MemFlex, provide data on feasibility, and begin to explore mechanisms of change. We plan a single-blind, randomised, controlled, patient-level trial in which 50 individuals with MDD will complete either psychoeducation (n = 25) or MemFlex (n = 25). After completing pre-treatment measures and an orientation session, participants complete eight workbook-based sessions at home. Participants will then be assessed at post-treatment and at 3 month follow-up. The co-primary outcomes are depressive symptoms and diagnostic status at 3 month follow-up. The secondary outcomes are memory flexibility at post-treatment and number of depression free days at 3 month follow-up. Other process outcomes and mediators of any treatment effects will also be explored. This trial will establish the efficacy of MemFlex in improving memory flexibility, and reducing depressive symptoms. Any effects on process measures related to relapse may also indicate whether MemFlex may be helpful in reducing vulnerability to future depressive episodes. The low-intensity and workbook-based format of the programme may improve access to psychological therapies, and, if encouraging, the results of this study will provide a platform for later-phase trials. NCT02371291 (ClinicalTrials.gov), registered 9 February 2015.

Flexible Retrieval: When True Inferences Produce False Memories

ERIC Educational Resources Information Center

Carpenter, Alexis C.; Schacter, Daniel L.

2017-01-01

Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave…

Performance Evaluation of Remote Memory Access (RMA) Programming on Shared Memory Parallel Computers

NASA Technical Reports Server (NTRS)

Jin, Hao-Qiang; Jost, Gabriele; Biegel, Bryan A. (Technical Monitor)

2002-01-01

The purpose of this study is to evaluate the feasibility of remote memory access (RMA) programming on shared memory parallel computers. We discuss different RMA based implementations of selected CFD application benchmark kernels and compare them to corresponding message passing based codes. For the message-passing implementation we use MPI point-to-point and global communication routines. For the RMA based approach we consider two different libraries supporting this programming model. One is a shared memory parallelization library (SMPlib) developed at NASA Ames, the other is the MPI-2 extensions to the MPI Standard. We give timing comparisons for the different implementation strategies and discuss the performance.

Extending Mondrian Memory Protection

DTIC Science & Technology

2010-11-01

a kernel semaphore is locked or unlocked. In addition, we extended the system call interface to receive notifications about user-land locking...operations (such as calls to the mutex and semaphore code provided by the C library). By patching the dynamically loadable GLibC5, we are able to test... semaphores , and spinlocks. RTO-MP-IST-091 10- 9 Extending Mondrian Memory Protection to loading extension plugins. This prevents any untrusted code

Advanced Development of Certified OS Kernels

DTIC Science & Technology

2015-06-01

It provides an infrastructure to map a physical page into multiple processes’ page maps in different address spaces. Their ownership mechanism ensures...of their shared memory infrastructure . Trap module The trap module specifies the behaviors of exception handlers and mCertiKOS system calls. In...layers), 1 pm for the shared memory infrastructure (3 layers), 3.5 pm for the thread management (10 layers), 1 pm for the process management (4 layers

A study of selenium nanoparticles as charge storage element for flexible semi-transparent memory devices

NASA Astrophysics Data System (ADS)

Alotaibi, Sattam; Nama Manjunatha, Krishna; Paul, Shashi

2017-12-01

Flexible Semi-Transparent electronic memory would be useful in coming years for integrated flexible transparent electronic devices. However, attaining such flexibility and semi-transparency leads to the boundaries in material composition. Thus, impeding processing speed and device performance. In this work, we present the use of inorganic stable selenium nanoparticles (Se-NPs) as a storage element and hydrogenated amorphous carbon (a-C:H) as an insulating layer in two terminal non-volatile physically flexible and semi-transparent capacitive memory devices (2T-NMDs). Furthermore, a-C:H films can be deposited at very low temperature (<40° C) on a variety of substrates (including many kinds of plastic substrates) by an industrial technique called Plasma Enhanced Chemical Vapour Deposition (PECVD) which is available in many existing fabrication labs. Self-assembled Se-NPs has several unique features including deposition at room temperature by simple vacuum thermal evaporation process without the need for further optimisation. This facilitates the fabrication of memory on a flexible substrate. Moreover, the memory behaviour of the Se-NPs was found to be more distinct than those of the semiconductor and metal nanostructures due to higher work function compared to the commonly used semiconductor and metal species. The memory behaviour was observed from the hysteresis of current-voltage (I-V) measurements while the two distinguishable electrical conductivity states (;0; and "1") were studied by current-time (I-t) measurements.

Performance and scalability evaluation of "Big Memory" on Blue Gene Linux.

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

Yoshii, K.; Iskra, K.; Naik, H.

2011-05-01

We address memory performance issues observed in Blue Gene Linux and discuss the design and implementation of 'Big Memory' - an alternative, transparent memory space introduced to eliminate the memory performance issues. We evaluate the performance of Big Memory using custom memory benchmarks, NAS Parallel Benchmarks, and the Parallel Ocean Program, at a scale of up to 4,096 nodes. We find that Big Memory successfully resolves the performance issues normally encountered in Blue Gene Linux. For the ocean simulation program, we even find that Linux with Big Memory provides better scalability than does the lightweight compute node kernel designed solelymore » for high-performance applications. Originally intended exclusively for compute node tasks, our new memory subsystem dramatically improves the performance of certain I/O node applications as well. We demonstrate this performance using the central processor of the LOw Frequency ARray radio telescope as an example.« less

An Adaptive Genetic Association Test Using Double Kernel Machines

PubMed Central

Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis

2014-01-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602

Design of k-Space Channel Combination Kernels and Integration with Parallel Imaging

PubMed Central

Beatty, Philip J.; Chang, Shaorong; Holmes, James H.; Wang, Kang; Brau, Anja C. S.; Reeder, Scott B.; Brittain, Jean H.

2014-01-01

Purpose In this work, a new method is described for producing local k-space channel combination kernels using a small amount of low-resolution multichannel calibration data. Additionally, this work describes how these channel combination kernels can be combined with local k-space unaliasing kernels produced by the calibration phase of parallel imaging methods such as GRAPPA, PARS and ARC. Methods Experiments were conducted to evaluate both the image quality and computational efficiency of the proposed method compared to a channel-by-channel parallel imaging approach with image-space sum-of-squares channel combination. Results Results indicate comparable image quality overall, with some very minor differences seen in reduced field-of-view imaging. It was demonstrated that this method enables a speed up in computation time on the order of 3–16X for 32-channel data sets. Conclusion The proposed method enables high quality channel combination to occur earlier in the reconstruction pipeline, reducing computational and memory requirements for image reconstruction. PMID:23943602

Flexible memory retrieval in bilingual 6-month-old infants.

PubMed

Brito, Natalie; Barr, Rachel

2014-07-01

Memory flexibility is a hallmark of the human memory system. As indexed by generalization between perceptually dissimilar objects, memory flexibility develops gradually during infancy. A recent study has found a bilingual advantage in memory generalization at 18 months of age [Brito and Barr [2012] Developmental Science, 15, 812-816], and the present study examines when this advantage may first emerge. In the current study, bilingual 6-month-olds were more likely than monolinguals to generalize to a puppet that differed in two features (shape and color) than monolingual 6-month-olds. When challenged with a less complex change, two puppets that differed only in one feature--color, monolingual 6-month-olds were also able to generalize. These findings demonstrate early emerging differences in memory generalization in bilingual infants, and have important implications for our understanding of how early environmental variations shape the trajectory of memory development. © 2013 Wiley Periodicals, Inc.

OpenGeoSys-GEMS: Hybrid parallelization of a reactive transport code with MPI and threads

NASA Astrophysics Data System (ADS)

Kosakowski, G.; Kulik, D. A.; Shao, H.

2012-04-01

OpenGeoSys-GEMS is a generic purpose reactive transport code based on the operator splitting approach. The code couples the Finite-Element groundwater flow and multi-species transport modules of the OpenGeoSys (OGS) project (http://www.ufz.de/index.php?en=18345) with the GEM-Selektor research package to model thermodynamic equilibrium of aquatic (geo)chemical systems utilizing the Gibbs Energy Minimization approach (http://gems.web.psi.ch/). The combination of OGS and the GEM-Selektor kernel (GEMS3K) is highly flexible due to the object-oriented modular code structures and the well defined (memory based) data exchange modules. Like other reactive transport codes, the practical applicability of OGS-GEMS is often hampered by the long calculation time and large memory requirements. • For realistic geochemical systems which might include dozens of mineral phases and several (non-ideal) solid solutions the time needed to solve the chemical system with GEMS3K may increase exceptionally. • The codes are coupled in a sequential non-iterative loop. In order to keep the accuracy, the time step size is restricted. In combination with a fine spatial discretization the time step size may become very small which increases calculation times drastically even for small 1D problems. • The current version of OGS is not optimized for memory use and the MPI version of OGS does not distribute data between nodes. Even for moderately small 2D problems the number of MPI processes that fit into memory of up-to-date workstations or HPC hardware is limited. One strategy to overcome the above mentioned restrictions of OGS-GEMS is to parallelize the coupled code. For OGS a parallelized version already exists. It is based on a domain decomposition method implemented with MPI and provides a parallel solver for fluid and mass transport processes. In the coupled code, after solving fluid flow and solute transport, geochemical calculations are done in form of a central loop over all finite element nodes with calls to GEMS3K and consecutive calculations of changed material parameters. In a first step the existing MPI implementation was utilized to parallelize this loop. Calculations were split between the MPI processes and afterwards data was synchronized by using MPI communication routines. Furthermore, multi-threaded calculation of the loop was implemented with help of the boost thread library (http://www.boost.org). This implementation provides a flexible environment to distribute calculations between several threads. For each MPI process at least one and up to several dozens of worker threads are spawned. These threads do not replicate the complete OGS-GEM data structure and use only a limited amount of memory. Calculation of the central geochemical loop is shared between all threads. Synchronization between the threads is done by barrier commands. The overall number of local threads times MPI processes should match the number of available computing nodes. The combination of multi-threading and MPI provides an effective and flexible environment to speed up OGS-GEMS calculations while limiting the required memory use. Test calculations on different hardware show that for certain types of applications tremendous speedups are possible.

Accessing global data from accelerator devices

DOEpatents

Bertolli, Carlo; O'Brien, John K.; Sallenave, Olivier H.; Sura, Zehra N.

2016-12-06

An aspect includes a table of contents (TOC) that was generated by a compiler being received at an accelerator device. The TOC includes an address of global data in a host memory space. The global data is copied from the address in the host memory space to an address in the device memory space. The address in the host memory space is obtained from the received TOC. The received TOC is updated to indicate that global data is stored at the address in the device memory space. A kernel that accesses the global data from the address in the device memory space is executed. The address in the device memory space is obtained based on contents of the updated TOC. When the executing is completed, the global data from the address in the device memory space is copied to the address in the host memory space.

Accessing global data from accelerator devices

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

Bertolli, Carlo; O'Brien, John K.; Sallenave, Olivier H.

2016-12-06

An aspect includes a table of contents (TOC) that was generated by a compiler being received at an accelerator device. The TOC includes an address of global data in a host memory space. The global data is copied from the address in the host memory space to an address in the device memory space. The address in the host memory space is obtained from the received TOC. The received TOC is updated to indicate that global data is stored at the address in the device memory space. A kernel that accesses the global data from the address in the devicemore » memory space is executed. The address in the device memory space is obtained based on contents of the updated TOC. When the executing is completed, the global data from the address in the device memory space is copied to the address in the host memory space.« less

Underpinnings of the Costs of Flexibility in Preschool Children: The Roles of Inhibition and Working Memory

PubMed Central

Chevalier, Nicolas; Sheffield, Tiffany D.; Nelson, Jennifer Mize; Clark, Caron A. C.; Wiebe, Sandra A.; Espy, Kimberly Andrews

2012-01-01

This study addressed the respective contributions of inhibition and working memory to two underlying components of flexibility, goal representation (as assessed by mixing costs) and switch implementation (as assessed by local costs), across the preschool period. By later preschool age (4 years 6 months and 5 years 3 months), both inhibition and working-memory performance were associated with mixing costs, but not with local costs, whereas no relation was observed earlier (3 years, 9 months). The relations of inhibition and working memory to flexibility appear to emerge late in the preschool period and are mainly driven by goal representation. PMID:22339225

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

Womeldorff, Geoffrey Alan; Payne, Joshua Estes; Bergen, Benjamin Karl

These are slides for a presentation on PARTISN Research and FleCSI Updates. The following topics are covered: SNAP vs PARTISN, Background Research, Production Code (structural design and changes, kernel design and implementation, lessons learned), NuT IMC Proxy, FleCSI Update (design and lessons learned). It can all be summarized in the following manner: Kokkos was shown to be effective in FY15 in implementing a C++ version of SNAP's kernel. This same methodology was applied to a production IC code, PARTISN. This was a much more complex endeavour than in FY15 for many reasons; a C++ kernel embedded in Fortran, overloading Fortranmore » memory allocations, general language interoperability, and a fully fleshed out production code versus a simplified proxy code. Lessons learned are Legion. In no particular order: Interoperability between Fortran and C++ was really not that hard, and a useful engineering effort. Tracking down all necessary memory allocations for a kernel in a production code is pretty hard. Modifying a production code to work for more than a handful of use cases is also pretty hard. Figuring out the toolchain that will allow a successful implementation of design decisions is quite hard, if making use of "bleeding edge" design choices. In terms of performance, production code concurrency architecture can be a virtual showstopper; being too complex to easily rewrite and test in a short period of time, or depending on tool features which do not exist yet. Ultimately, while the tools used in this work were not successful in speeding up the production code, they helped to identify how work would be done, and provide requirements to tools.« less

Development of Flexible Visual Recognition Memory in Human Infants

ERIC Educational Resources Information Center

Robinson, Astri J.; Pascalis, Olivier

2004-01-01

Research using the visual paired comparison task has shown that visual recognition memory across changing contexts is dependent on the integrity of the hippocampal formation in human adults and in monkeys. The acquisition of contextual flexibility may contribute to the change in memory performance that occurs late in the first year of life. To…

KERNELHR: A program for estimating animal home ranges

USGS Publications Warehouse

Seaman, D.E.; Griffith, B.; Powell, R.A.

1998-01-01

Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

Generalized Langevin equation with tempered memory kernel

NASA Astrophysics Data System (ADS)

Liemert, André; Sandev, Trifce; Kantz, Holger

2017-01-01

We study a generalized Langevin equation for a free particle in presence of a truncated power-law and Mittag-Leffler memory kernel. It is shown that in presence of truncation, the particle from subdiffusive behavior in the short time limit, turns to normal diffusion in the long time limit. The case of harmonic oscillator is considered as well, and the relaxation functions and the normalized displacement correlation function are represented in an exact form. By considering external time-dependent periodic force we obtain resonant behavior even in case of a free particle due to the influence of the environment on the particle movement. Additionally, the double-peak phenomenon in the imaginary part of the complex susceptibility is observed. It is obtained that the truncation parameter has a huge influence on the behavior of these quantities, and it is shown how the truncation parameter changes the critical frequencies. The normalized displacement correlation function for a fractional generalized Langevin equation is investigated as well. All the results are exact and given in terms of the three parameter Mittag-Leffler function and the Prabhakar generalized integral operator, which in the kernel contains a three parameter Mittag-Leffler function. Such kind of truncated Langevin equation motion can be of high relevance for the description of lateral diffusion of lipids and proteins in cell membranes.

TIME-DOMAIN METHODS FOR DIFFUSIVE TRANSPORT IN SOFT MATTER

PubMed Central

Fricks, John; Yao, Lingxing; Elston, Timothy C.; Gregory Forest, And M.

2015-01-01

Passive microrheology [12] utilizes measurements of noisy, entropic fluctuations (i.e., diffusive properties) of micron-scale spheres in soft matter to infer bulk frequency-dependent loss and storage moduli. Here, we are concerned exclusively with diffusion of Brownian particles in viscoelastic media, for which the Mason-Weitz theoretical-experimental protocol is ideal, and the more challenging inference of bulk viscoelastic moduli is decoupled. The diffusive theory begins with a generalized Langevin equation (GLE) with a memory drag law specified by a kernel [7, 16, 22, 23]. We start with a discrete formulation of the GLE as an autoregressive stochastic process governing microbead paths measured by particle tracking. For the inverse problem (recovery of the memory kernel from experimental data) we apply time series analysis (maximum likelihood estimators via the Kalman filter) directly to bead position data, an alternative to formulas based on mean-squared displacement statistics in frequency space. For direct modeling, we present statistically exact GLE algorithms for individual particle paths as well as statistical correlations for displacement and velocity. Our time-domain methods rest upon a generalization of well-known results for a single-mode exponential kernel [1, 7, 22, 23] to an arbitrary M-mode exponential series, for which the GLE is transformed to a vector Ornstein-Uhlenbeck process. PMID:26412904

Investigations on the effects of electrode materials on the device characteristics of ferroelectric memory thin film transistors fabricated on flexible substrates

NASA Astrophysics Data System (ADS)

Yang, Ji-Hee; Yun, Da-Jeong; Seo, Gi-Ho; Kim, Seong-Min; Yoon, Myung-Han; Yoon, Sung-Min

2018-03-01

For flexible memory device applications, we propose memory thin-film transistors using an organic ferroelectric poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] gate insulator and an amorphous In-Ga-Zn-O (a-IGZO) active channel. The effects of electrode materials and their deposition methods on the characteristics of memory devices exploiting the ferroelectric field effect were investigated for the proposed ferroelectric memory thin-film transistors (Fe-MTFTs) at flat and bending states. It was found that the plasma-induced sputtering deposition and mechanical brittleness of the indium-tin oxide (ITO) markedly degraded the ferroelectric-field-effect-driven memory window and bending characteristics of the Fe-MTFTs. The replacement of ITO electrodes with metal aluminum (Al) electrodes prepared by plasma-free thermal evaporation greatly enhanced the memory device characteristics even under bending conditions owing to their mechanical ductility. Furthermore, poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) was introduced to achieve robust bending performance under extreme mechanical stress. The Fe-MTFTs using PEDOT:PSS source/drain electrodes were successfully fabricated and showed the potential for use as flexible memory devices. The suitable choice of electrode materials employed for the Fe-MTFTs is concluded to be one of the most important control parameters for highly functional flexible Fe-MTFTs.

Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for applications on Intel® Xeon Phi™ Processor

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

Koskela, Tuomas S.; Lobet, Mathieu; Deslippe, Jack

In this session we show, in two case studies, how the roofline feature of Intel Advisor has been utilized to optimize the performance of kernels of the XGC1 and PICSAR codes in preparation for Intel Knights Landing architecture. The impact of the implemented optimizations and the benefits of using the automatic roofline feature of Intel Advisor to study performance of large applications will be presented. This demonstrates an effective optimization strategy that has enabled these science applications to achieve up to 4.6 times speed-up and prepare for future exascale architectures. # Goal/Relevance of Session The roofline model [1,2] is amore » powerful tool for analyzing the performance of applications with respect to the theoretical peak achievable on a given computer architecture. It allows one to graphically represent the performance of an application in terms of operational intensity, i.e. the ratio of flops performed and bytes moved from memory in order to guide optimization efforts. Given the scale and complexity of modern science applications, it can often be a tedious task for the user to perform the analysis on the level of functions or loops to identify where performance gains can be made. With new Intel tools, it is now possible to automate this task, as well as base the estimates of peak performance on measurements rather than vendor specifications. The goal of this session is to demonstrate how the roofline feature of Intel Advisor can be used to balance memory vs. computation related optimization efforts and effectively identify performance bottlenecks. A series of typical optimization techniques: cache blocking, structure refactoring, data alignment, and vectorization illustrated by the kernel cases will be addressed. # Description of the codes ## XGC1 The XGC1 code [3] is a magnetic fusion Particle-In-Cell code that uses an unstructured mesh for its Poisson solver that allows it to accurately resolve the edge plasma of a magnetic fusion device. After recent optimizations to its collision kernel [4], most of the computing time is spent in the electron push (pushe) kernel, where these optimization efforts have been focused. The kernel code scaled well with MPI+OpenMP but had almost no automatic compiler vectorization, in part due to indirect memory addresses and in part due to low trip counts of low-level loops that would be candidates for vectorization. Particle blocking and sorting have been implemented to increase trip counts of low-level loops and improve memory locality, and OpenMP directives have been added to vectorize compute-intensive loops that were identified by Advisor. The optimizations have improved the performance of the pushe kernel 2x on Haswell processors and 1.7x on KNL. The KNL node-for-node performance has been brought to within 30% of a NERSC Cori phase I Haswell node and we expect to bridge this gap by reducing the memory footprint of compute intensive routines to improve cache reuse. ## PICSAR is a Fortran/Python high-performance Particle-In-Cell library targeting at MIC architectures first designed to be coupled with the PIC code WARP for the simulation of laser-matter interaction and particle accelerators. PICSAR also contains a FORTRAN stand-alone kernel for performance studies and benchmarks. A MPI domain decomposition is used between NUMA domains and a tile decomposition (cache-blocking) handled by OpenMP has been added for shared-memory parallelism and better cache management. The so-called current deposition and field gathering steps that compose the PIC time loop constitute major hotspots that have been rewritten to enable more efficient vectorization. Particle communications between tiles and MPI domain has been merged and parallelized. All considered, these improvements provide speedups of 3.1 for order 1 and 4.6 for order 3 interpolation shape factors on KNL configured in SNC4 quadrant flat mode. Performance is similar between a node of cori phase 1 and KNL at order 1 and better on KNL by a factor 1.6 at order 3 with the considered test case (homogeneous thermal plasma).« less

The formulation and estimation of a spatial skew-normal generalized ordered-response model.

DOT National Transportation Integrated Search

2016-06-01

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...

Study of harsh environment operation of flexible ferroelectric memory integrated with PZT and silicon fabric

NASA Astrophysics Data System (ADS)

Ghoneim, M. T.; Hussain, M. M.

2015-08-01

Flexible memory can enable industrial, automobile, space, and smart grid centered harsh/extreme environment focused electronics application(s) for enhanced operation, safety, and monitoring where bent or complex shaped infrastructures are common and state-of-the-art rigid electronics cannot be deployed. Therefore, we report on the physical-mechanical-electrical characteristics of a flexible ferroelectric memory based on lead zirconium titanate as a key memory material and flexible version of bulk mono-crystalline silicon (100). The experimented devices show a bending radius down to 1.25 cm corresponding to 0.16% nominal strain (high pressure of ˜260 MPa), and full functionality up to 225 °C high temperature in ambient gas composition (21% oxygen and 55% relative humidity). The devices showed unaltered data retention and fatigue properties under harsh conditions, still the reduced memory window (20% difference between switching and non-switching currents at 225 °C) requires sensitive sense circuitry for proper functionality and is the limiting factor preventing operation at higher temperatures.

Committing to Memory: Memory Prosthetics Show Promise in Helping Those with Neurodegenerative Disorders.

PubMed

Solis, Michele

2017-01-01

Cell phone chimes, sticky notes, even the proverbial string around a finger-these timehonored external cues help guard against our inevitable memory lapses. But some internal help to the brain itself may be on the way in the form of what's being called memory prosthetics. Once considered to be on the fringes of neuroscience, the idea of adding hardware to the brain to help with memory has gathered steam. In 2014, the U.S. Defense Advanced Research Projects Agency (DARPA) made a US$30 million investment in memory prosthetic research as part of the Obama administration's Brain Research through Advancing Innovative Neurotechnologies initiative. In August 2016, Kernel, a startup based in Los Angeles, California, announced its goal to develop a clinical memory device for those debilitated by neurodegenerative disorders such as Alzheimer's disease.

Discrete Resource Allocation in Visual Working Memory

ERIC Educational Resources Information Center

Barton, Brian; Ester, Edward F.; Awh, Edward

2009-01-01

Are resources in visual working memory allocated in a continuous or a discrete fashion? On one hand, flexible resource models suggest that capacity is determined by a central resource pool that can be flexibly divided such that items of greater complexity receive a larger share of resources. On the other hand, if capacity in working memory is…

Global solutions to the equation of thermoelasticity with fading memory

NASA Astrophysics Data System (ADS)

Okada, Mari; Kawashima, Shuichi

2017-07-01

We consider the initial-history value problem for the one-dimensional equation of thermoelasticity with fading memory. It is proved that if the data are smooth and small, then a unique smooth solution exists globally in time and converges to the constant equilibrium state as time goes to infinity. Our proof is based on a technical energy method which makes use of the strict convexity of the entropy function and the properties of strongly positive definite kernels.

Functionalized Graphitic Carbon Nitride for Metal-free, Flexible and Rewritable Nonvolatile Memory Device via Direct Laser-Writing

NASA Astrophysics Data System (ADS)

Zhao, Fei; Cheng, Huhu; Hu, Yue; Song, Long; Zhang, Zhipan; Jiang, Lan; Qu, Liangti

2014-07-01

Graphitic carbon nitride nanosheet (g-C3N4-NS) has layered structure similar with graphene nanosheet and presents unusual physicochemical properties due to the s-triazine fragments. But their electronic and electrochemical applications are limited by the relatively poor conductivity. The current work provides the first example that atomically thick g-C3N4-NSs are the ideal candidate as the active insulator layer with tunable conductivity for achieving the high performance memory devices with electrical bistability. Unlike in conventional memory diodes, the g-C3N4-NSs based devices combined with graphene layer electrodes are flexible, metal-free and low cost. The functionalized g-C3N4-NSs exhibit desirable dispersibility and dielectricity which support the all-solution fabrication and high performance of the memory diodes. Moreover, the flexible memory diodes are conveniently fabricated through the fast laser writing process on graphene oxide/g-C3N4-NSs/graphene oxide thin film. The obtained devices not only have the nonvolatile electrical bistability with great retention and endurance, but also show the rewritable memory effect with a reliable ON/OFF ratio of up to 105, which is the highest among all the metal-free flexible memory diodes reported so far, and even higher than those of metal-containing devices.

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Zheng, Yuan F.

2017-01-01

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345

Resistive switching effect in the planar structure of all-printed, flexible and rewritable memory device based on advanced 2D nanocomposite of graphene quantum dots and white graphene flakes

NASA Astrophysics Data System (ADS)

Muqeet Rehman, Muhammad; Uddin Siddiqui, Ghayas; Kim, Sowon; Choi, Kyung Hyun

2017-08-01

Pursuit of the most appropriate materials and fabrication methods is essential for developing a reliable, rewritable and flexible memory device. In this study, we have proposed an advanced 2D nanocomposite of white graphene (hBN) flakes embedded with graphene quantum dots (GQDs) as the functional layer of a flexible memory device owing to their unique electrical, chemical and mechanical properties. Unlike the typical sandwich type structure of a memory device, we developed a cost effective planar structure, to simplify device fabrication and prevent sneak current. The entire device fabrication was carried out using printing technology followed by encapsulation in an atomically thin layer of aluminum oxide (Al2O3) for protection against environmental humidity. The proposed memory device exhibited attractive bipolar switching characteristics of high switching ratio, large electrical endurance and enhanced lifetime, without any crosstalk between adjacent memory cells. The as-fabricated device showed excellent durability for several bending cycles at various bending diameters without any degradation in bistable resistive states. The memory mechanism was deduced to be conductive filamentary; this was validated by illustrating the temperature dependence of bistable resistive states. Our obtained results pave the way for the execution of promising 2D material based next generation flexible and non-volatile memory (NVM) applications.

Testing the causality of Hawkes processes with time reversal

NASA Astrophysics Data System (ADS)

Cordi, Marcus; Challet, Damien; Muni Toke, Ioane

2018-03-01

We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, tests of goodness of parametric fit unambiguously reject backward event times, which implies that inferring kernels from time-symmetric quantities, such as the autocovariance of the event rate, only rarely produce statistically significant fits. Finally, we find that fitting financial data with many-parameter kernels may yield significant fits for both arrows of time for the same event time vector, sometimes favouring the backward time direction. This goes to show that a significant fit of Hawkes processes to real data with flexible kernels does not imply a definite arrow of time unless one tests it.

A linear-RBF multikernel SVM to classify big text corpora.

PubMed

Romero, R; Iglesias, E L; Borrajo, L

2015-01-01

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

Helium: lifting high-performance stencil kernels from stripped x86 binaries to halide DSL code

DOE PAGES

Mendis, Charith; Bosboom, Jeffrey; Wu, Kevin; ...

2015-06-03

Highly optimized programs are prone to bit rot, where performance quickly becomes suboptimal in the face of new hardware and compiler techniques. In this paper we show how to automatically lift performance-critical stencil kernels from a stripped x86 binary and generate the corresponding code in the high-level domain-specific language Halide. Using Halide's state-of-the-art optimizations targeting current hardware, we show that new optimized versions of these kernels can replace the originals to rejuvenate the application for newer hardware. The original optimized code for kernels in stripped binaries is nearly impossible to analyze statically. Instead, we rely on dynamic traces to regeneratemore » the kernels. We perform buffer structure reconstruction to identify input, intermediate and output buffer shapes. Here, we abstract from a forest of concrete dependency trees which contain absolute memory addresses to symbolic trees suitable for high-level code generation. This is done by canonicalizing trees, clustering them based on structure, inferring higher-dimensional buffer accesses and finally by solving a set of linear equations based on buffer accesses to lift them up to simple, high-level expressions. Helium can handle highly optimized, complex stencil kernels with input-dependent conditionals. We lift seven kernels from Adobe Photoshop giving a 75 % performance improvement, four kernels from Irfan View, leading to 4.97 x performance, and one stencil from the mini GMG multigrid benchmark netting a 4.25 x improvement in performance. We manually rejuvenated Photoshop by replacing eleven of Photoshop's filters with our lifted implementations, giving 1.12 x speedup without affecting the user experience.« less

Mice Overexpressing Type 1 Adenylyl Cyclase Show Enhanced Spatial Memory Flexibility in the Absence of Intact Synaptic Long-Term Depression

ERIC Educational Resources Information Center

Zhang, Ming; Wang, Hongbing

2013-01-01

There is significant interest in understanding the contribution of intracellular signaling and synaptic substrates to memory flexibility, which involves new learning and suppression of obsolete memory. Here, we report that enhancement of Ca[superscript 2+]-stimulated cAMP signaling by overexpressing type 1 adenylyl cyclase (AC1) facilitated…

3D Printing of Shape Memory Polymers for Flexible Electronic Devices.

PubMed

Zarek, Matt; Layani, Michael; Cooperstein, Ido; Sachyani, Ela; Cohn, Daniel; Magdassi, Shlomo

2016-06-01

The formation of 3D objects composed of shape memory polymers for flexible electronics is described. Layer-by-layer photopolymerization of methacrylated semicrystalline molten macromonomers by a 3D digital light processing printer enables rapid fabrication of complex objects and imparts shape memory functionality for electrical circuits. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Flexibility of Event Boundaries in Autobiographical Memory

PubMed Central

Hohman, Timothy J.; Peynircioğlu, Zehra F.; Beason-Held, Lori L.

2014-01-01

Events have clear and consistent boundaries that are defined during perception in a manner that influences memory performance. The natural process of event segmentation shapes event definitions during perception, and appears to play a critical role in defining distinct episodic memories at encoding. However, the role of retrieval processes in modifying event definitions is not clear. We explored how such processes changed event boundary definitions at recall. In Experiment 1 we showed that distance from encoding is related to boundary flexibility. Participants were more likely to move self-reported event boundaries to include information reported beyond those boundaries when recalling more distant events compared to more recent events. In Experiment 2, we showed that age also influenced boundary flexibility. Older Age adults were more likely to move event boundaries than College Age adults, and the relationship between distance from encoding and boundary flexibility seen in Experiment 1 was present only in College Age and Middle Age adults. These results suggest that factors at retrieval have a direct impact on event definitions in memory and that, although episodic memories may be initially defined at encoding, these definitions are not necessarily maintained in long-term memory. PMID:22989194

Study of harsh environment operation of flexible ferroelectric memory integrated with PZT and silicon fabric

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

Ghoneim, M. T.; Hussain, M. M., E-mail: muhammadmustafa.hussain@kaust.edu.sa

Flexible memory can enable industrial, automobile, space, and smart grid centered harsh/extreme environment focused electronics application(s) for enhanced operation, safety, and monitoring where bent or complex shaped infrastructures are common and state-of-the-art rigid electronics cannot be deployed. Therefore, we report on the physical-mechanical-electrical characteristics of a flexible ferroelectric memory based on lead zirconium titanate as a key memory material and flexible version of bulk mono-crystalline silicon (100). The experimented devices show a bending radius down to 1.25 cm corresponding to 0.16% nominal strain (high pressure of ∼260 MPa), and full functionality up to 225 °C high temperature in ambient gas composition (21% oxygenmore » and 55% relative humidity). The devices showed unaltered data retention and fatigue properties under harsh conditions, still the reduced memory window (20% difference between switching and non-switching currents at 225 °C) requires sensitive sense circuitry for proper functionality and is the limiting factor preventing operation at higher temperatures.« less

Cognitive remediation therapy (CRT) benefits more to patients with schizophrenia with low initial memory performances.

PubMed

Pillet, Benoit; Morvan, Yannick; Todd, Aurelia; Franck, Nicolas; Duboc, Chloé; Grosz, Aimé; Launay, Corinne; Demily, Caroline; Gaillard, Raphaël; Krebs, Marie-Odile; Amado, Isabelle

2015-01-01

Cognitive deficits in schizophrenia mainly affect memory, attention and executive functions. Cognitive remediation is a technique derived from neuropsychology, which aims to improve or compensate for these deficits. Working memory, verbal learning, and executive functions are crucial factors for functional outcome. Our purpose was to assess the impact of the cognitive remediation therapy (CRT) program on cognitive difficulties in patients with schizophrenia, especially on working memory, verbal memory, and cognitive flexibility. We collected data from clinical and neuropsychological assessments in 24 patients suffering from schizophrenia (Diagnostic and Statistical Manual of mental Disorders-Fourth Edition, DSM-IV) who followed a 3-month (CRT) program. Verbal and visuo-spatial working memory, verbal memory, and cognitive flexibility were assessed before and after CRT. The Wilcoxon test showed significant improvements on the backward digit span, on the visual working memory span, on verbal memory and on flexibility. Cognitive improvement was substantial when baseline performance was low, independently from clinical benefit. CRT is effective on crucial cognitive domains and provides a huge benefit for patients having low baseline performance. Such cognitive amelioration appears highly promising for improving the outcome in cognitively impaired patients.

Transferable and flexible label-like macromolecular memory on arbitrary substrates with high performance and a facile methodology.

PubMed

Lai, Ying-Chih; Hsu, Fang-Chi; Chen, Jian-Yu; He, Jr-Hau; Chang, Ting-Chang; Hsieh, Ya-Ping; Lin, Tai-Yuan; Yang, Ying-Jay; Chen, Yang-Fang

2013-05-21

A newly designed transferable and flexible label-like organic memory based on a graphene electrode behaves like a sticker, and can be readily placed on desired substrates or devices for diversified purposes. The memory label reveals excellent performance despite its physical presentation. This may greatly extend the memory applications in various advanced electronics and provide a simple scheme to integrate with other electronics. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine Learning Feature Selection for Tuning Memory Page Swapping

DTIC Science & Technology

2013-09-01

environments we set up. 13 Figure 4.1 Updated Feature Vector List. Features we added to the kernel are anno - tated with “(MLVM...Feb. 1966. [2] P. J . Denning, “The working set model for program behavior,” Communications of the ACM, vol. 11, no. 5, pp. 323–333, May 1968. [3] L. A...8] R. W. Cart and J . L. Hennessy, “WSClock — A simple and effective algorithm for virtual memory management,” M.S. thesis, Dept. Computer Science

Multi-Core Processor Memory Contention Benchmark Analysis Case Study

NASA Technical Reports Server (NTRS)

Simon, Tyler; McGalliard, James

2009-01-01

Multi-core processors dominate current mainframe, server, and high performance computing (HPC) systems. This paper provides synthetic kernel and natural benchmark results from an HPC system at the NASA Goddard Space Flight Center that illustrate the performance impacts of multi-core (dual- and quad-core) vs. single core processor systems. Analysis of processor design, application source code, and synthetic and natural test results all indicate that multi-core processors can suffer from significant memory subsystem contention compared to similar single-core processors.

SMOKE TOOL FOR MODELS-3 VERSION 4.1 STRUCTURE AND OPERATION DOCUMENTATION

EPA Science Inventory

The SMOKE Tool is a part of the Models-3 system, a flexible software system designed to simplify the development and use of air quality models and other environmental decision support tools. The SMOKE Tool is an input processor for SMOKE, (Sparse Matrix Operator Kernel Emissio...

A WPS Based Architecture for Climate Data Analytic Services (CDAS) at NASA

NASA Astrophysics Data System (ADS)

Maxwell, T. P.; McInerney, M.; Duffy, D.; Carriere, L.; Potter, G. L.; Doutriaux, C.

2015-12-01

Faced with unprecedented growth in the Big Data domain of climate science, NASA has developed the Climate Data Analytic Services (CDAS) framework. This framework enables scientists to execute trusted and tested analysis operations in a high performance environment close to the massive data stores at NASA. The data is accessed in standard (NetCDF, HDF, etc.) formats in a POSIX file system and processed using trusted climate data analysis tools (ESMF, CDAT, NCO, etc.). The framework is structured as a set of interacting modules allowing maximal flexibility in deployment choices. The current set of module managers include: Staging Manager: Runs the computation locally on the WPS server or remotely using tools such as celery or SLURM. Compute Engine Manager: Runs the computation serially or distributed over nodes using a parallelization framework such as celery or spark. Decomposition Manger: Manages strategies for distributing the data over nodes. Data Manager: Handles the import of domain data from long term storage and manages the in-memory and disk-based caching architectures. Kernel manager: A kernel is an encapsulated computational unit which executes a processor's compute task. Each kernel is implemented in python exploiting existing analysis packages (e.g. CDAT) and is compatible with all CDAS compute engines and decompositions. CDAS services are accessed via a WPS API being developed in collaboration with the ESGF Compute Working Team to support server-side analytics for ESGF. The API can be executed using either direct web service calls, a python script or application, or a javascript-based web application. Client packages in python or javascript contain everything needed to make CDAS requests. The CDAS architecture brings together the tools, data storage, and high-performance computing required for timely analysis of large-scale data sets, where the data resides, to ultimately produce societal benefits. It is is currently deployed at NASA in support of the Collaborative REAnalysis Technical Environment (CREATE) project, which centralizes numerous global reanalysis datasets onto a single advanced data analytics platform. This service permits decision makers to investigate climate changes around the globe, inspect model trends, compare multiple reanalysis datasets, and variability.

Functionalized Graphitic Carbon Nitride for Metal-free, Flexible and Rewritable Nonvolatile Memory Device via Direct Laser-Writing

PubMed Central

Zhao, Fei; Cheng, Huhu; Hu, Yue; Song, Long; Zhang, Zhipan; Jiang, Lan; Qu, Liangti

2014-01-01

Graphitic carbon nitride nanosheet (g-C3N4-NS) has layered structure similar with graphene nanosheet and presents unusual physicochemical properties due to the s-triazine fragments. But their electronic and electrochemical applications are limited by the relatively poor conductivity. The current work provides the first example that atomically thick g-C3N4-NSs are the ideal candidate as the active insulator layer with tunable conductivity for achieving the high performance memory devices with electrical bistability. Unlike in conventional memory diodes, the g-C3N4-NSs based devices combined with graphene layer electrodes are flexible, metal-free and low cost. The functionalized g-C3N4-NSs exhibit desirable dispersibility and dielectricity which support the all-solution fabrication and high performance of the memory diodes. Moreover, the flexible memory diodes are conveniently fabricated through the fast laser writing process on graphene oxide/g-C3N4-NSs/graphene oxide thin film. The obtained devices not only have the nonvolatile electrical bistability with great retention and endurance, but also show the rewritable memory effect with a reliable ON/OFF ratio of up to 105, which is the highest among all the metal-free flexible memory diodes reported so far, and even higher than those of metal-containing devices. PMID:25073687

Detection of weak signals in memory thermal baths.

PubMed

Jiménez-Aquino, J I; Velasco, R M; Romero-Bastida, M

2014-11-01

The nonlinear relaxation time and the statistics of the first passage time distribution in connection with the quasideterministic approach are used to detect weak signals in the decay process of the unstable state of a Brownian particle embedded in memory thermal baths. The study is performed in the overdamped approximation of a generalized Langevin equation characterized by an exponential decay in the friction memory kernel. A detection criterion for each time scale is studied: The first one is referred to as the receiver output, which is given as a function of the nonlinear relaxation time, and the second one is related to the statistics of the first passage time distribution.

Particle-in-cell simulations on graphic processing units

NASA Astrophysics Data System (ADS)

Ren, C.; Zhou, X.; Li, J.; Huang, M. C.; Zhao, Y.

2014-10-01

We will show our recent progress in using GPU's to accelerate the PIC code OSIRIS [Fonseca et al. LNCS 2331, 342 (2002)]. The OISRIS parallel structure is retained and the computation-intensive kernels are shipped to GPU's. Algorithms for the kernels are adapted for the GPU, including high-order charge-conserving current deposition schemes with few branching and parallel particle sorting [Kong et al., JCP 230, 1676 (2011)]. These algorithms make efficient use of the GPU shared memory. This work was supported by U.S. Department of Energy under Grant No. DE-FC02-04ER54789 and by NSF under Grant No. PHY-1314734.

Analysis and Implementation of Particle-to-Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method

DTIC Science & Technology

2015-06-01

5110P and 16 dx360M4 nodes each with one NVIDIA Kepler K20M/K40M GPU. Each node contained dual Intel Xeon E5-2670 (Sandy Bridge) central processing...kernel and as such does not employ multiple processors. This work makes use of a single processing core and a single NVIDIA Kepler K40 GK110...bandwidth (2 × 16 slot), 7.877 GFloat/s; Kepler K40 peak, 4,290 × 1 billion floating-point operations (GFLOPs), and 288 GB/s Kepler K40 memory

Object-Oriented Design for Sparse Direct Solvers

NASA Technical Reports Server (NTRS)

Dobrian, Florin; Kumfert, Gary; Pothen, Alex

1999-01-01

We discuss the object-oriented design of a software package for solving sparse, symmetric systems of equations (positive definite and indefinite) by direct methods. At the highest layers, we decouple data structure classes from algorithmic classes for flexibility. We describe the important structural and algorithmic classes in our design, and discuss the trade-offs we made for high performance. The kernels at the lower layers were optimized by hand. Our results show no performance loss from our object-oriented design, while providing flexibility, case of use, and extensibility over solvers using procedural design.

Early Experiences Writing Performance Portable OpenMP 4 Codes

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

Joubert, Wayne; Hernandez, Oscar R

In this paper, we evaluate the recently available directives in OpenMP 4 to parallelize a computational kernel using both the traditional shared memory approach and the newer accelerator targeting capabilities. In addition, we explore various transformations that attempt to increase application performance portability, and examine the expressiveness and performance implications of using these approaches. For example, we want to understand if the target map directives in OpenMP 4 improve data locality when mapped to a shared memory system, as opposed to the traditional first touch policy approach in traditional OpenMP. To that end, we use recent Cray and Intel compilersmore » to measure the performance variations of a simple application kernel when executed on the OLCF s Titan supercomputer with NVIDIA GPUs and the Beacon system with Intel Xeon Phi accelerators attached. To better understand these trade-offs, we compare our results from traditional OpenMP shared memory implementations to the newer accelerator programming model when it is used to target both the CPU and an attached heterogeneous device. We believe the results and lessons learned as presented in this paper will be useful to the larger user community by providing guidelines that can assist programmers in the development of performance portable code.« less

Construction of non-Markovian coarse-grained models employing the Mori-Zwanzig formalism and iterative Boltzmann inversion

NASA Astrophysics Data System (ADS)

Yoshimoto, Yuta; Li, Zhen; Kinefuchi, Ikuya; Karniadakis, George Em

2017-12-01

We propose a new coarse-grained (CG) molecular simulation technique based on the Mori-Zwanzig (MZ) formalism along with the iterative Boltzmann inversion (IBI). Non-Markovian dissipative particle dynamics (NMDPD) taking into account memory effects is derived in a pairwise interaction form from the MZ-guided generalized Langevin equation. It is based on the introduction of auxiliary variables that allow for the replacement of a non-Markovian equation with a Markovian one in a higher dimensional space. We demonstrate that the NMDPD model exploiting MZ-guided memory kernels can successfully reproduce the dynamic properties such as the mean square displacement and velocity autocorrelation function of a Lennard-Jones system, as long as the memory kernels are appropriately evaluated based on the Volterra integral equation using the force-velocity and velocity-velocity correlations. Furthermore, we find that the IBI correction of a pair CG potential significantly improves the representation of static properties characterized by a radial distribution function and pressure, while it has little influence on the dynamic processes. Our findings suggest that combining the advantages of both the MZ formalism and IBI leads to an accurate representation of both the static and dynamic properties of microscopic systems that exhibit non-Markovian behavior.

Community detection using Kernel Spectral Clustering with memory

NASA Astrophysics Data System (ADS)

Langone, Rocco; Suykens, Johan A. K.

2013-02-01

This work is related to the problem of community detection in dynamic scenarios, which for instance arises in the segmentation of moving objects, clustering of telephone traffic data, time-series micro-array data etc. A desirable feature of a clustering model which has to capture the evolution of communities over time is the temporal smoothness between clusters in successive time-steps. In this way the model is able to track the long-term trend and in the same time it smooths out short-term variation due to noise. We use the Kernel Spectral Clustering with Memory effect (MKSC) which allows to predict cluster memberships of new nodes via out-of-sample extension and has a proper model selection scheme. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness as a valid prior knowledge. The latter, in fact, allows the model to cluster the current data well and to be consistent with the recent history. Here we propose a generalization of the MKSC model with an arbitrary memory, not only one time-step in the past. The experiments conducted on toy problems confirm our expectations: the more memory we add to the model, the smoother over time are the clustering results. We also compare with the Evolutionary Spectral Clustering (ESC) algorithm which is a state-of-the art method, and we obtain comparable or better results.

Resilient and Robust High Performance Computing Platforms for Scientific Computing Integrity

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

Jin, Yier

As technology advances, computer systems are subject to increasingly sophisticated cyber-attacks that compromise both their security and integrity. High performance computing platforms used in commercial and scientific applications involving sensitive, or even classified data, are frequently targeted by powerful adversaries. This situation is made worse by a lack of fundamental security solutions that both perform efficiently and are effective at preventing threats. Current security solutions fail to address the threat landscape and ensure the integrity of sensitive data. As challenges rise, both private and public sectors will require robust technologies to protect its computing infrastructure. The research outcomes from thismore » project try to address all these challenges. For example, we present LAZARUS, a novel technique to harden kernel Address Space Layout Randomization (KASLR) against paging-based side-channel attacks. In particular, our scheme allows for fine-grained protection of the virtual memory mappings that implement the randomization. We demonstrate the effectiveness of our approach by hardening a recent Linux kernel with LAZARUS, mitigating all of the previously presented side-channel attacks on KASLR. Our extensive evaluation shows that LAZARUS incurs only 0.943% overhead for standard benchmarks, and is therefore highly practical. We also introduced HA2lloc, a hardware-assisted allocator that is capable of leveraging an extended memory management unit to detect memory errors in the heap. We also perform testing using HA2lloc in a simulation environment and find that the approach is capable of preventing common memory vulnerabilities.« less

On the Efficacy of Source Code Optimizations for Cache-Based Systems

NASA Technical Reports Server (NTRS)

VanderWijngaart, Rob F.; Saphir, William C.

1998-01-01

Obtaining high performance without machine-specific tuning is an important goal of scientific application programmers. Since most scientific processing is done on commodity microprocessors with hierarchical memory systems, this goal of "portable performance" can be achieved if a common set of optimization principles is effective for all such systems. It is widely believed, or at least hoped, that portable performance can be realized. The rule of thumb for optimization on hierarchical memory systems is to maximize temporal and spatial locality of memory references by reusing data and minimizing memory access stride. We investigate the effects of a number of optimizations on the performance of three related kernels taken from a computational fluid dynamics application. Timing the kernels on a range of processors, we observe an inconsistent and often counterintuitive impact of the optimizations on performance. In particular, code variations that have a positive impact on one architecture can have a negative impact on another, and variations expected to be unimportant can produce large effects. Moreover, we find that cache miss rates - as reported by a cache simulation tool, and confirmed by hardware counters - only partially explain the results. By contrast, the compiler-generated assembly code provides more insight by revealing the importance of processor-specific instructions and of compiler maturity, both of which strongly, and sometimes unexpectedly, influence performance. We conclude that it is difficult to obtain performance portability on modern cache-based computers, and comment on the implications of this result.

On the Efficacy of Source Code Optimizations for Cache-Based Systems

NASA Technical Reports Server (NTRS)

VanderWijngaart, Rob F.; Saphir, William C.; Saini, Subhash (Technical Monitor)

1998-01-01

Obtaining high performance without machine-specific tuning is an important goal of scientific application programmers. Since most scientific processing is done on commodity microprocessors with hierarchical memory systems, this goal of "portable performance" can be achieved if a common set of optimization principles is effective for all such systems. It is widely believed, or at least hoped, that portable performance can be realized. The rule of thumb for optimization on hierarchical memory systems is to maximize temporal and spatial locality of memory references by reusing data and minimizing memory access stride. We investigate the effects of a number of optimizations on the performance of three related kernels taken from a computational fluid dynamics application. Timing the kernels on a range of processors, we observe an inconsistent and often counterintuitive impact of the optimizations on performance. In particular, code variations that have a positive impact on one architecture can have a negative impact on another, and variations expected to be unimportant can produce large effects. Moreover, we find that cache miss rates-as reported by a cache simulation tool, and confirmed by hardware counters-only partially explain the results. By contrast, the compiler-generated assembly code provides more insight by revealing the importance of processor-specific instructions and of compiler maturity, both of which strongly, and sometimes unexpectedly, influence performance. We conclude that it is difficult to obtain performance portability on modern cache-based computers, and comment on the implications of this result.

Locality Aware Concurrent Start for Stencil Applications

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

Shrestha, Sunil; Gao, Guang R.; Manzano Franco, Joseph B.

Stencil computations are at the heart of many physical simulations used in scientific codes. Thus, there exists a plethora of optimization efforts for this family of computations. Among these techniques, tiling techniques that allow concurrent start have proven to be very efficient in providing better performance for these critical kernels. Nevertheless, with many core designs being the norm, these optimization techniques might not be able to fully exploit locality (both spatial and temporal) on multiple levels of the memory hierarchy without compromising parallelism. It is no longer true that the machine can be seen as a homogeneous collection of nodesmore » with caches, main memory and an interconnect network. New architectural designs exhibit complex grouping of nodes, cores, threads, caches and memory connected by an ever evolving network-on-chip design. These new designs may benefit greatly from carefully crafted schedules and groupings that encourage parallel actors (i.e. threads, cores or nodes) to be aware of the computational history of other actors in close proximity. In this paper, we provide an efficient tiling technique that allows hierarchical concurrent start for memory hierarchy aware tile groups. Each execution schedule and tile shape exploit the available parallelism, load balance and locality present in the given applications. We demonstrate our technique on the Intel Xeon Phi architecture with selected and representative stencil kernels. We show improvement ranging from 5.58% to 31.17% over existing state-of-the-art techniques.« less

The development of cognitive flexibility beyond the preschool period: an investigation using a modified Flexible Item Selection Task.

PubMed

Dick, Anthony Steven

2014-09-01

We explored the development of cognitive flexibility in typically developing 6-, 8-, and 10-year-olds and adults by modifying a common cognitive flexibility task, the Flexible Item Selection Task (FIST). Although performance on the standard FIST reached ceiling by 8 years, FIST performance on other variations continued to improve until 10 years of age. Within a detailed task analysis, we also explored working memory storage and processing components of executive function and how these contribute to the development of cognitive flexibility. The findings reinforce the notion that cognitive flexibility is a multifaceted construct but that the development of working memory contributes in part to age-related change in this ability. Copyright © 2014 Elsevier Inc. All rights reserved.

Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

PubMed Central

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa; Yasuno, Yoshiaki

2017-01-01

Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) simultaneously measures optical intensity, birefringence, degree of polarization uniformity, and OCT angiography. The statistics of the optical features in a local region, such as the local mean of the OCT intensity, are frequently used for image processing and the quantitative analysis of JM-OCT. Conventionally, local statistics have been computed with fixed-size rectangular kernels. However, this results in a trade-off between image sharpness and statistical accuracy. We introduce a superpixel method to JM-OCT for generating the flexible kernels of local statistics. A superpixel is a cluster of image pixels that is formed by the pixels’ spatial and signal value proximities. An algorithm for superpixel generation specialized for JM-OCT and its optimization methods are presented in this paper. The spatial proximity is in two-dimensional cross-sectional space and the signal values are the four optical features. Hence, the superpixel method is a six-dimensional clustering technique for JM-OCT pixels. The performance of the JM-OCT superpixels and its optimization methods are evaluated in detail using JM-OCT datasets of posterior eyes. The superpixels were found to well preserve tissue structures, such as layer structures, sclera, vessels, and retinal pigment epithelium. And hence, they are more suitable for local statistics kernels than conventional uniform rectangular kernels. PMID:29082073

Active Control of Flexible Space Structures Using the Nitinol Shape Memory Actuators

DTIC Science & Technology

1987-10-01

number) FIELD !GROUP SUBGROUP I Active Control, Nitinol Actuators, Space Structures 9. ABSTRACT (Continue on reverse if necessary and identify by block...number) Summarizes research progress in the feasibility demonstration of active vibration control using Nitinol shape memory actuators. Tests on...FLEXIBLE SPACE STRUCTURES USING NITINOL SHAPE MEMORY ACTUATORS FINAL REPORT FOR PHASE I SDIO CONTRACT #F49620-87-C-0035 0 BY DR. AMR M. BAZ KARIM R

Memory dynamics under stress.

PubMed

Quaedflieg, Conny W E M; Schwabe, Lars

2018-03-01

Stressful events have a major impact on memory. They modulate memory formation in a time-dependent manner, closely linked to the temporal profile of action of major stress mediators, in particular catecholamines and glucocorticoids. Shortly after stressor onset, rapidly acting catecholamines and fast, non-genomic glucocorticoid actions direct cognitive resources to the processing and consolidation of the ongoing threat. In parallel, control of memory is biased towards rather rigid systems, promoting habitual forms of memory allowing efficient processing under stress, at the expense of "cognitive" systems supporting memory flexibility and specificity. In this review, we discuss the implications of this shift in the balance of multiple memory systems for the dynamics of the memory trace. Specifically, stress appears to hinder the incorporation of contextual details into the memory trace, to impede the integration of new information into existing knowledge structures, to impair the flexible generalisation across past experiences, and to hamper the modification of memories in light of new information. Delayed, genomic glucocorticoid actions might reverse the control of memory, thus restoring homeostasis and "cognitive" control of memory again.

Executive functioning and reading achievement in school: a study of Brazilian children assessed by their teachers as "poor readers".

PubMed

Engel de Abreu, Pascale M J; Abreu, Neander; Nikaedo, Carolina C; Puglisi, Marina L; Tourinho, Carlos J; Miranda, Mônica C; Befi-Lopes, Debora M; Bueno, Orlando F A; Martin, Romain

2014-01-01

This study examined executive functioning and reading achievement in 106 6- to 8-year-old Brazilian children from a range of social backgrounds of whom approximately half lived below the poverty line. A particular focus was to explore the executive function profile of children whose classroom reading performance was judged below standard by their teachers and who were matched to controls on chronological age, sex, school type (private or public), domicile (Salvador/BA or São Paulo/SP) and socioeconomic status. Children completed a battery of 12 executive function tasks that were conceptual tapping cognitive flexibility, working memory, inhibition and selective attention. Each executive function domain was assessed by several tasks. Principal component analysis extracted four factors that were labeled "Working Memory/Cognitive Flexibility," "Interference Suppression," "Selective Attention," and "Response Inhibition." Individual differences in executive functioning components made differential contributions to early reading achievement. The Working Memory/Cognitive Flexibility factor emerged as the best predictor of reading. Group comparisons on computed factor scores showed that struggling readers displayed limitations in Working Memory/Cognitive Flexibility, but not in other executive function components, compared to more skilled readers. These results validate the account that working memory capacity provides a crucial building block for the development of early literacy skills and extends it to a population of early readers of Portuguese from Brazil. The study suggests that deficits in working memory/cognitive flexibility might represent one contributing factor to reading difficulties in early readers. This might have important implications for how educators might intervene with children at risk of academic under achievement.

Energy-band engineering for tunable memory characteristics through controlled doping of reduced graphene oxide.

PubMed

Han, Su-Ting; Zhou, Ye; Yang, Qing Dan; Zhou, Li; Huang, Long-Biao; Yan, Yan; Lee, Chun-Sing; Roy, Vellaisamy A L

2014-02-25

Tunable memory characteristics are used in multioperational mode circuits where memory cells with various functionalities are needed in one combined device. It is always a challenge to obtain control over threshold voltage for multimode operation. On this regard, we use a strategy of shifting the work function of reduced graphene oxide (rGO) in a controlled manner through doping gold chloride (AuCl3) and obtained a gradient increase of rGO work function. By inserting doped rGO as floating gate, a controlled threshold voltage (Vth) shift has been achieved in both p- and n-type low voltage flexible memory devices with large memory window (up to 4 times for p-type and 8 times for n-type memory devices) in comparison with pristine rGO floating gate memory devices. By proper energy band engineering, we demonstrated a flexible floating gate memory device with larger memory window and controlled threshold voltage shifts.

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Direct Observation of a Carbon Filament in Water-Resistant Organic Memory.

PubMed

Lee, Byung-Hyun; Bae, Hagyoul; Seong, Hyejeong; Lee, Dong-Il; Park, Hongkeun; Choi, Young Joo; Im, Sung-Gap; Kim, Sang Ouk; Choi, Yang-Kyu

2015-07-28

The memory for the Internet of Things (IoT) requires versatile characteristics such as flexibility, wearability, and stability in outdoor environments. Resistive random access memory (RRAM) to harness a simple structure and organic material with good flexibility can be an attractive candidate for IoT memory. However, its solution-oriented process and unclear switching mechanism are critical problems. Here we demonstrate iCVD polymer-intercalated RRAM (i-RRAM). i-RRAM exhibits robust flexibility and versatile wearability on any substrate. Stable operation of i-RRAM, even in water, is demonstrated, which is the first experimental presentation of water-resistant organic memory without any waterproof protection package. Moreover, the direct observation of a carbon filament is also reported for the first time using transmission electron microscopy, which puts an end to the controversy surrounding the switching mechanism. Therefore, reproducibility is feasible through comprehensive modeling. Furthermore, a carbon filament is superior to a metal filament in terms of the design window and selection of the electrode material. These results suggest an alternative to solve the critical issues of organic RRAM and an optimized memory type suitable for the IoT era.

Transparent resistive switching memory using aluminum oxide on a flexible substrate

NASA Astrophysics Data System (ADS)

Yeom, Seung-Won; Shin, Sang-Chul; Kim, Tan-Young; Ha, Hyeon Jun; Lee, Yun-Hi; Shim, Jae Won; Ju, Byeong-Kwon

2016-02-01

Resistive switching memory (ReRAM) has attracted much attention in recent times owing to its fast switching, simple structure, and non-volatility. Flexible and transparent electronic devices have also attracted considerable attention. We therefore fabricated an Al2O3-based ReRAM with transparent indium-zinc-oxide (IZO) electrodes on a flexible substrate. The device transmittance was found to be higher than 80% in the visible region (400-800 nm). Bended states (radius = 10 mm) of the device also did not affect the memory performance because of the flexibility of the two transparent IZO electrodes and the thin Al2O3 layer. The conduction mechanism of the resistive switching of our device was explained by ohmic conduction and a Poole-Frenkel emission model. The conduction mechanism was proved by oxygen vacancies in the Al2O3 layer, as analyzed by x-ray photoelectron spectroscopy analysis. These results encourage the application of ReRAM in flexible and transparent electronic devices.

Adaptive learning in complex reproducing kernel Hilbert spaces employing Wirtinger's subgradients.

PubMed

Bouboulis, Pantelis; Slavakis, Konstantinos; Theodoridis, Sergios

2012-03-01

This paper presents a wide framework for non-linear online supervised learning tasks in the context of complex valued signal processing. The (complex) input data are mapped into a complex reproducing kernel Hilbert space (RKHS), where the learning phase is taking place. Both pure complex kernels and real kernels (via the complexification trick) can be employed. Moreover, any convex, continuous and not necessarily differentiable function can be used to measure the loss between the output of the specific system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. In order to derive analytically the subgradients, the principles of the (recently developed) Wirtinger's calculus in complex RKHS are exploited. Furthermore, both linear and widely linear (in RKHS) estimation filters are considered. To cope with the problem of increasing memory requirements, which is present in almost all online schemes in RKHS, the sparsification scheme, based on projection onto closed balls, has been adopted. We demonstrate the effectiveness of the proposed framework in a non-linear channel identification task, a non-linear channel equalization problem and a quadrature phase shift keying equalization scheme, using both circular and non circular synthetic signal sources.

Flexible graphene-PZT ferroelectric nonvolatile memory.

PubMed

Lee, Wonho; Kahya, Orhan; Toh, Chee Tat; Ozyilmaz, Barbaros; Ahn, Jong-Hyun

2013-11-29

We report the fabrication of a flexible graphene-based nonvolatile memory device using Pb(Zr0.35,Ti0.65)O3 (PZT) as the ferroelectric material. The graphene and PZT ferroelectric layers were deposited using chemical vapor deposition and sol–gel methods, respectively. Such PZT films show a high remnant polarization (Pr) of 30 μC cm−2 and a coercive voltage (Vc) of 3.5 V under a voltage loop over ±11 V. The graphene–PZT ferroelectric nonvolatile memory on a plastic substrate displayed an on/off current ratio of 6.7, a memory window of 6 V and reliable operation. In addition, the device showed one order of magnitude lower operation voltage range than organic-based ferroelectric nonvolatile memory after removing the anti-ferroelectric behavior incorporating an electrolyte solution. The devices showed robust operation in bent states of bending radii up to 9 mm and in cycling tests of 200 times. The devices exhibited remarkable mechanical properties and were readily integrated with plastic substrates for the production of flexible circuits.

Flexible Organic Tribotronic Transistor Memory for a Visible and Wearable Touch Monitoring System.

PubMed

Li, Jing; Zhang, Chi; Duan, Lian; Zhang, Li Min; Wang, Li Duo; Dong, Gui Fang; Wang, Zhong Lin

2016-01-06

A new type of flexible organic tribotronic transistor memory is proposed, which can be written and erased by externally applied touch actions as an active memory. By further coupling with an organic light-emitting diode (OLED), a visible and wearable touch monitoring system is achieved, in which touch triggering can be memorized and shown as the emission from the OLED. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Protect sensitive data with lightweight memory encryption

NASA Astrophysics Data System (ADS)

Zhou, Hongwei; Yuan, Jinhui; Xiao, Rui; Zhang, Kai; Sun, Jingyao

2018-04-01

Since current commercial processor is not able to deal with the data in the cipher text, the sensitive data have to be exposed in the memory. It leaves a window for the adversary. To protect the sensitive data, a direct idea is to encrypt the data when the processor does not access them. On the observation, we have developed a lightweight memory encryption, called LeMe, to protect the sensitive data in the application. LeMe marks the sensitive data in the memory with the page table entry, and encrypts the data in their free time. LeMe is built on the Linux with a 3.17.6 kernel, and provides four user interfaces as dynamic link library. Our evaluations show LeMe is effective to protect the sensitive data and incurs an acceptable performance overhead.

Enabling the High Level Synthesis of Data Analytics Accelerators

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

Minutoli, Marco; Castellana, Vito G.; Tumeo, Antonino

Conventional High Level Synthesis (HLS) tools mainly tar- get compute intensive kernels typical of digital signal pro- cessing applications. We are developing techniques and ar- chitectural templates to enable HLS of data analytics appli- cations. These applications are memory intensive, present fine-grained, unpredictable data accesses, and irregular, dy- namic task parallelism. We discuss an architectural tem- plate based around a distributed controller to efficiently ex- ploit thread level parallelism. We present a memory in- terface that supports parallel memory subsystems and en- ables implementing atomic memory operations. We intro- duce a dynamic task scheduling approach to efficiently ex- ecute heavilymore » unbalanced workload. The templates are val- idated by synthesizing queries from the Lehigh University Benchmark (LUBM), a well know SPARQL benchmark.« less

The strain and thermal induced tunable charging phenomenon in low power flexible memory arrays with a gold nanoparticle monolayer.

PubMed

Zhou, Ye; Han, Su-Ting; Xu, Zong-Xiang; Roy, V A L

2013-03-07

The strain and temperature dependent memory effect of organic memory transistors on plastic substrates has been investigated under ambient conditions. The gold (Au) nanoparticle monolayer was prepared and embedded in an atomic layer deposited aluminum oxide (Al(2)O(3)) as the charge trapping layer. The devices exhibited low operation voltage, reliable memory characteristics and long data retention time. Experimental analysis of the programming and erasing behavior at various bending states showed the relationship between strain and charging capacity. Thermal-induced effects on these memory devices have also been analyzed. The mobility shows ~200% rise and the memory window increases from 1.48 V to 1.8 V when the temperature rises from 20 °C to 80 °C due to thermally activated transport. The retention capability of the devices decreases with the increased working temperature. Our findings provide a better understanding of flexible organic memory transistors under various operating temperatures and validate their applications in various areas such as temperature sensors, temperature memory or advanced electronic circuits. Furthermore, the low temperature processing procedures of the key elements (Au nanoparticle monolayer and Al(2)O(3) dielectric layer) could be potentially integrated with large area flexible electronics.

Study of heterogeneous and reconfigurable architectures in the communication domain

NASA Astrophysics Data System (ADS)

Feldkaemper, H. T.; Blume, H.; Noll, T. G.

2003-05-01

One of the most challenging design issues for next generations of (mobile) communication systems is fulfilling the computational demands while finding an appropriate trade-off between flexibility and implementation aspects, especially power consumption. Flexibility of modern architectures is desirable, e.g. concerning adaptation to new standards and reduction of time-to-market of a new product. Typical target architectures for future communication systems include embedded FPGAs, dedicated macros as well as programmable digital signal and control oriented processor cores as each of these has its specific advantages. These will be integrated as a System-on-Chip (SoC). For such a heterogeneous architecture a design space exploration and an appropriate partitioning plays a crucial role. On the exemplary vehicle of a Viterbi decoder as frequently used in communication systems we show which costs in terms of ATE complexity arise implementing typical components on different types of architecture blocks. A factor of about seven orders of magnitude spans between a physically optimised implementation and an implementation on a programmable DSP kernel. An implementation on an embedded FPGA kernel is in between these two representing an attractive compromise with high flexibility and low power consumption. Extending this comparison to further components, it is shown quantitatively that the cost ratio between different implementation alternatives is closely related to the operation to be performed. This information is essential for the appropriate partitioning of heterogeneous systems.

Improving Block-level Efficiency with scsi-mq

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

Caldwell, Blake A

2015-01-01

Current generation solid-state storage devices are exposing a new bottlenecks in the SCSI and block layers of the Linux kernel, where IO throughput is limited by lock contention, inefficient interrupt handling, and poor memory locality. To address these limitations, the Linux kernel block layer underwent a major rewrite with the blk-mq project to move from a single request queue to a multi-queue model. The Linux SCSI subsystem rework to make use of this new model, known as scsi-mq, has been merged into the Linux kernel and work is underway for dm-multipath support in the upcoming Linux 4.0 kernel. These piecesmore » were necessary to make use of the multi-queue block layer in a Lustre parallel filesystem with high availability requirements. We undertook adding support of the 3.18 kernel to Lustre with scsi-mq and dm-multipath patches to evaluate the potential of these efficiency improvements. In this paper we evaluate the block-level performance of scsi-mq with backing storage hardware representative of a HPC-targerted Lustre filesystem. Our findings show that SCSI write request latency is reduced by as much as 13.6%. Additionally, when profiling the CPU usage of our prototype Lustre filesystem, we found that CPU idle time increased by a factor of 7 with Linux 3.18 and blk-mq as compared to a standard 2.6.32 Linux kernel. Our findings demonstrate increased efficiency of the multi-queue block layer even with disk-based caching storage arrays used in existing parallel filesystems.« less

Reduced multiple empirical kernel learning machine.

PubMed

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient.

Naps promote flexible memory retrieval in 12-month-old infants.

PubMed

Konrad, Carolin; Seehagen, Sabine; Schneider, Silvia; Herbert, Jane S

2016-11-01

Flexibility in applying existing knowledge to similar cues is a corner stone of memory development in infants. Here, we examine the effect of sleep on the flexibility of memory retrieval using a deferred imitation paradigm. Forty-eight 12-month-old infants were randomly assigned to either a nap or a no-nap demonstration condition (scheduled around their natural daytime sleep schedule) or to a baseline control condition. In the demonstration conditions, infants watched an experimenter perform three target actions on a hand puppet. Immediately afterwards, infants were allowed to practice the target actions three times. In a test session 4-hr later, infants were given the opportunity to reproduce the actions with a novel hand puppet differing in color from the puppet used during the demonstration session. Only infants in the nap-condition performed significantly more target actions than infants in the baseline control condition. Furthermore, they were faster to carry out the first target action than infants in the no-nap condition. We conclude that sleep had a facilitative effect on infants' flexibility of memory retrieval. © 2016 Wiley Periodicals, Inc.

Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference.

PubMed

Zeithamova, Dagmar; Dominick, April L; Preston, Alison R

2012-07-12

Memory enables flexible use of past experience to inform new behaviors. Although leading theories hypothesize that this fundamental flexibility results from the formation of integrated memory networks relating multiple experiences, the neural mechanisms that support memory integration are not well understood. Here, we demonstrate that retrieval-mediated learning, whereby prior event details are reinstated during encoding of related experiences, supports participants' ability to infer relationships between distinct events that share content. Furthermore, we show that activation changes in a functionally coupled hippocampal and ventral medial prefrontal cortical circuit track the formation of integrated memories and successful inferential memory performance. These findings characterize the respective roles of these regions in retrieval-mediated learning processes that support relational memory network formation and inferential memory in the human brain. More broadly, these data reveal fundamental mechanisms through which memory representations are constructed into prospectively useful formats. Copyright © 2012 Elsevier Inc. All rights reserved.

Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference

PubMed Central

Zeithamova, Dagmar; Dominick, April L.; Preston, Alison R.

2012-01-01

SUMMARY Memory enables flexible use of past experience to inform new behaviors. Though leading theories hypothesize that this fundamental flexibility results from the formation of integrated memory networks relating multiple experiences, the neural mechanisms that support memory integration are not well understood. Here, we demonstrate that retrieval-mediated learning, whereby prior event details are reinstated during encoding of related experiences, supports participants’ ability to infer relationships between distinct events that share content. Furthermore, we show that activation changes in a functionally coupled hippocampal and ventral medial prefrontal cortical circuit track the formation of integrated memories and successful inferential memory performance. These findings characterize the respective roles of these regions in retrieval-mediated learning processes that support relational memory network formation and inferential memory in the human brain. More broadly, these data reveal fundamental mechanisms through which memory representations are constructed into prospectively useful formats. PMID:22794270

The strain and thermal induced tunable charging phenomenon in low power flexible memory arrays with a gold nanoparticle monolayer

NASA Astrophysics Data System (ADS)

Zhou, Ye; Han, Su-Ting; Xu, Zong-Xiang; Roy, V. A. L.

2013-02-01

The strain and temperature dependent memory effect of organic memory transistors on plastic substrates has been investigated under ambient conditions. The gold (Au) nanoparticle monolayer was prepared and embedded in an atomic layer deposited aluminum oxide (Al2O3) as the charge trapping layer. The devices exhibited low operation voltage, reliable memory characteristics and long data retention time. Experimental analysis of the programming and erasing behavior at various bending states showed the relationship between strain and charging capacity. Thermal-induced effects on these memory devices have also been analyzed. The mobility shows ~200% rise and the memory window increases from 1.48 V to 1.8 V when the temperature rises from 20 °C to 80 °C due to thermally activated transport. The retention capability of the devices decreases with the increased working temperature. Our findings provide a better understanding of flexible organic memory transistors under various operating temperatures and validate their applications in various areas such as temperature sensors, temperature memory or advanced electronic circuits. Furthermore, the low temperature processing procedures of the key elements (Au nanoparticle monolayer and Al2O3 dielectric layer) could be potentially integrated with large area flexible electronics.The strain and temperature dependent memory effect of organic memory transistors on plastic substrates has been investigated under ambient conditions. The gold (Au) nanoparticle monolayer was prepared and embedded in an atomic layer deposited aluminum oxide (Al2O3) as the charge trapping layer. The devices exhibited low operation voltage, reliable memory characteristics and long data retention time. Experimental analysis of the programming and erasing behavior at various bending states showed the relationship between strain and charging capacity. Thermal-induced effects on these memory devices have also been analyzed. The mobility shows ~200% rise and the memory window increases from 1.48 V to 1.8 V when the temperature rises from 20 °C to 80 °C due to thermally activated transport. The retention capability of the devices decreases with the increased working temperature. Our findings provide a better understanding of flexible organic memory transistors under various operating temperatures and validate their applications in various areas such as temperature sensors, temperature memory or advanced electronic circuits. Furthermore, the low temperature processing procedures of the key elements (Au nanoparticle monolayer and Al2O3 dielectric layer) could be potentially integrated with large area flexible electronics. Electronic supplementary information (ESI) available: UV-vis spectrum of Au nanoparticle aqueous solution, transfer characteristics of the transistors without inserting an Au nanoparticle monolayer, AFM image of the pentacene layer, transfer characteristics at different program voltages and memory windows with respect to the P/E voltage. See DOI: 10.1039/c2nr32579a

Brownian motion of a nano-colloidal particle: the role of the solvent.

PubMed

Torres-Carbajal, Alexis; Herrera-Velarde, Salvador; Castañeda-Priego, Ramón

2015-07-15

Brownian motion is a feature of colloidal particles immersed in a liquid-like environment. Usually, it can be described by means of the generalised Langevin equation (GLE) within the framework of the Mori theory. In principle, all quantities that appear in the GLE can be calculated from the molecular information of the whole system, i.e., colloids and solvent molecules. In this work, by means of extensive Molecular Dynamics simulations, we study the effects of the microscopic details and the thermodynamic state of the solvent on the movement of a single nano-colloid. In particular, we consider a two-dimensional model system in which the mass and size of the colloid are two and one orders of magnitude, respectively, larger than the ones associated with the solvent molecules. The latter ones interact via a Lennard-Jones-type potential to tune the nature of the solvent, i.e., it can be either repulsive or attractive. We choose the linear momentum of the Brownian particle as the observable of interest in order to fully describe the Brownian motion within the Mori framework. We particularly focus on the colloid diffusion at different solvent densities and two temperature regimes: high and low (near the critical point) temperatures. To reach our goal, we have rewritten the GLE as a second kind Volterra integral in order to compute the memory kernel in real space. With this kernel, we evaluate the momentum-fluctuating force correlation function, which is of particular relevance since it allows us to establish when the stationarity condition has been reached. Our findings show that even at high temperatures, the details of the attractive interaction potential among solvent molecules induce important changes in the colloid dynamics. Additionally, near the critical point, the dynamical scenario becomes more complex; all the correlation functions decay slowly in an extended time window, however, the memory kernel seems to be only a function of the solvent density. Thus, the explicit inclusion of the solvent in the description of Brownian motion allows us to better understand the behaviour of the memory kernel at those thermodynamic states near the critical region without any further approximation. This information is useful to elaborate more realistic descriptions of Brownian motion that take into account the particular details of the host medium.

JANUS: A Compilation System for Balancing Parallelism and Performance in OpenVX

NASA Astrophysics Data System (ADS)

Omidian, Hossein; Lemieux, Guy G. F.

2018-04-01

Embedded systems typically do not have enough on-chip memory for entire an image buffer. Programming systems like OpenCV operate on entire image frames at each step, making them use excessive memory bandwidth and power. In contrast, the paradigm used by OpenVX is much more efficient; it uses image tiling, and the compilation system is allowed to analyze and optimize the operation sequence, specified as a compute graph, before doing any pixel processing. In this work, we are building a compilation system for OpenVX that can analyze and optimize the compute graph to take advantage of parallel resources in many-core systems or FPGAs. Using a database of prewritten OpenVX kernels, it automatically adjusts the image tile size as well as using kernel duplication and coalescing to meet a defined area (resource) target, or to meet a specified throughput target. This allows a single compute graph to target implementations with a wide range of performance needs or capabilities, e.g. from handheld to datacenter, that use minimal resources and power to reach the performance target.

Kernel spectral clustering with memory effect

NASA Astrophysics Data System (ADS)

Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.

2013-05-01

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.

Flexible attention allocation to visual and auditory working memory tasks: manipulating reward induces a trade-off.

PubMed

Morey, Candice Coker; Cowan, Nelson; Morey, Richard D; Rouder, Jeffery N

2011-02-01

Prominent roles for general attention resources are posited in many models of working memory, but the manner in which these can be allocated differs between models or is not sufficiently specified. We varied the payoffs for correct responses in two temporally-overlapping recognition tasks, a visual array comparison task and a tone sequence comparison task. In the critical conditions, an increase in reward for one task corresponded to a decrease in reward for the concurrent task, but memory load remained constant. Our results show patterns of interference consistent with a trade-off between the tasks, suggesting that a shared resource can be flexibly divided, rather than only fully allotted to either of the tasks. Our findings support a role for a domain-general resource in models of working memory, and furthermore suggest that this resource is flexibly divisible.

Memory flexibility training for autobiographical memory as an intervention for maintaining social and mental well-being in older adults.

PubMed

Leahy, Fiona; Ridout, Nathan; Holland, Carol

2018-05-07

Autobiographical memory specificity (AMS) reduces with increasing age and is associated with depression, social problem-solving and functional limitations. However, ability to switch between general and specific, as well as between positive and negative retrieval, may be more important for the strategic use of autobiographical information in everyday life. Ability to switch between retrieval modes is likely to rely on aspects of executive function. We propose that age-related deficits in cognitive flexibility impair AMS, but the "positivity effect" protects positively valenced memories from impaired specificity. A training programme to improve the ability to flexibly retrieve different types of memories in depressed adults (MemFlex) was examined in non-depressed older adults to determine effects on AMS, valence and the executive functions underlying cognitive flexibility. Thirty-nine participants aged 70+ (MemFlex, n = 20; control, n = 19) took part. AMS and the inhibition aspect of executive function improved in both groups, suggesting these abilities are amenable to change, although not differentially affected by this type of training. Lower baseline inhibition scores correlated with increased negative, but not positive AMS, suggesting that positive AMS is an automatic process in older adults. Changes in AMS correlated with changes in social problem-solving, emphasising the usefulness of AMs in a social environment.

Executive functioning and reading achievement in school: a study of Brazilian children assessed by their teachers as “poor readers”

PubMed Central

Engel de Abreu, Pascale M. J.; Abreu, Neander; Nikaedo, Carolina C.; Puglisi, Marina L.; Tourinho, Carlos J.; Miranda, Mônica C.; Befi-Lopes, Debora M.; Bueno, Orlando F. A.; Martin, Romain

2014-01-01

This study examined executive functioning and reading achievement in 106 6- to 8-year-old Brazilian children from a range of social backgrounds of whom approximately half lived below the poverty line. A particular focus was to explore the executive function profile of children whose classroom reading performance was judged below standard by their teachers and who were matched to controls on chronological age, sex, school type (private or public), domicile (Salvador/BA or São Paulo/SP) and socioeconomic status. Children completed a battery of 12 executive function tasks that were conceptual tapping cognitive flexibility, working memory, inhibition and selective attention. Each executive function domain was assessed by several tasks. Principal component analysis extracted four factors that were labeled “Working Memory/Cognitive Flexibility,” “Interference Suppression,” “Selective Attention,” and “Response Inhibition.” Individual differences in executive functioning components made differential contributions to early reading achievement. The Working Memory/Cognitive Flexibility factor emerged as the best predictor of reading. Group comparisons on computed factor scores showed that struggling readers displayed limitations in Working Memory/Cognitive Flexibility, but not in other executive function components, compared to more skilled readers. These results validate the account that working memory capacity provides a crucial building block for the development of early literacy skills and extends it to a population of early readers of Portuguese from Brazil. The study suggests that deficits in working memory/cognitive flexibility might represent one contributing factor to reading difficulties in early readers. This might have important implications for how educators might intervene with children at risk of academic under achievement. PMID:24959155

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

Printing an ITO-free flexible poly (4-vinylphenol) resistive switching device

NASA Astrophysics Data System (ADS)

Ali, Junaid; Rehman, Muhammad Muqeet; Siddiqui, Ghayas Uddin; Aziz, Shahid; Choi, Kyung Hyun

2018-02-01

Resistive switching in a sandwich structure of silver (Ag)/Polyvinyl phenol (PVP)/carbon nanotube (CNTs)-silver nanowires (AgNWs) coated on a flexible PET substrate is reported in this work. Densely populated networks of one dimensional nano materials (1DNM), CNTs-AgNWs have been used as the conductive bottom electrode with the prominent features of high flexibility and low sheet resistance of 90 Ω/sq. Thin, yet uniform active layer of PVP was deposited on top of the spin coated 1DNM thin film through state of the art printing technique of electrohydrodynamic atomization (EHDA) with an average thickness of 170 ± 28 nm. Ag dots with an active area of ∼0.1 mm2 were deposited through roll to plate printing system as the top electrodes to complete the device fabrication of flexible memory device. Our memory device exhibited suitable electrical characteristics with OFF/ON ratio of 100:1, retention time of 60 min and electrical endurance for 100 voltage sweeps without any noticeable decay in performance. The resistive switching characteristics at a low current compliance of 3 nA were also evaluated for the application of low power consumption. This memory device is flexible and can sustain more than 100 bending cycles at a bending diameter of 2 cm with stable HRS and LRS values. Our proposed device shows promise to be used as a future potential nonvolatile memory device in flexible electronics.

Fabrication of InGaZnO Nonvolatile Memory Devices at Low Temperature of 150 degrees C for Applications in Flexible Memory Displays and Transparency Coating on Plastic Substrates.

PubMed

Hanh, Nguyen Hong; Jang, Kyungsoo; Yi, Junsin

2016-05-01

We directly deposited amorphous InGaZnO (a-IGZO) nonvolatile memory (NVM) devices with oxynitride-oxide-dioxide (OOO) stack structures on plastic substrate by a DC pulsed magnetron sputtering and inductively coupled plasma chemical vapor deposition (ICPCVD) system, using a low-temperature of 150 degrees C. The fabricated bottom gate a-IGZO NVM devices have a wide memory window with a low operating voltage during programming and erasing, due to an effective control of the gate dielectrics. In addition, after ten years, the memory device retains a memory window of over 73%, with a programming duration of only 1 ms. Moreover, the a-IGZO films show high optical transmittance of over 85%, and good uniformity with a root mean square (RMS) roughness of 0.26 nm. This film is a promising candidate to achieve flexible displays and transparency on plastic substrates because of the possibility of low-temperature deposition, and the high transparent properties of a-IGZO films. These results demonstrate that the a-IGZO NVM devices obtained at low-temperature have a suitable programming and erasing efficiency for data storage under low-voltage conditions, in combination with excellent charge retention characteristics, and thus show great potential application in flexible memory displays.

Solution-processed flexible NiO resistive random access memory device

NASA Astrophysics Data System (ADS)

Kim, Soo-Jung; Lee, Heon; Hong, Sung-Hoon

2018-04-01

Non-volatile memories (NVMs) using nanocrystals (NCs) as active materials can be applied to soft electronic devices requiring a low-temperature process because NCs do not require a heat treatment process for crystallization. In addition, memory devices can be implemented simply by using a patterning technique using a solution process. In this study, a flexible NiO ReRAM device was fabricated using a simple NC patterning method that controls the capillary force and dewetting of a NiO NC solution at low temperature. The switching behavior of a NiO NC based memory was clearly observed by conductive atomic force microscopy (c-AFM).

Subliminal encoding and flexible retrieval of objects in scenes.

PubMed

Wuethrich, Sergej; Hannula, Deborah E; Mast, Fred W; Henke, Katharina

2018-04-27

Our episodic memory stores what happened when and where in life. Episodic memory requires the rapid formation and flexible retrieval of where things are located in space. Consciousness of the encoding scene is considered crucial for episodic memory formation. Here, we question the necessity of consciousness and hypothesize that humans can form unconscious episodic memories. Participants were presented with subliminal scenes, i.e., scenes invisible to the conscious mind. The scenes displayed objects at certain locations for participants to form unconscious object-in-space memories. Later, the same scenes were presented supraliminally, i.e., visibly, for retrieval testing. Scenes were presented absent the objects and rotated by 90°-270° in perspective to assess the representational flexibility of unconsciously formed memories. During the test phase, participants performed a forced-choice task that required them to place an object in one of two highlighted scene locations and their eye movements were recorded. Evaluation of the eye tracking data revealed that participants remembered object locations unconsciously, irrespective of changes in viewing perspective. This effect of gaze was related to correct placements of objects in scenes, and an intuitive decision style was necessary for unconscious memories to influence intentional behavior to a significant degree. We conclude that conscious perception is not mandatory for spatial episodic memory formation. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Improving Memory Error Handling Using Linux

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

Carlton, Michael Andrew; Blanchard, Sean P.; Debardeleben, Nathan A.

As supercomputers continue to get faster and more powerful in the future, they will also have more nodes. If nothing is done, then the amount of memory in supercomputer clusters will soon grow large enough that memory failures will be unmanageable to deal with by manually replacing memory DIMMs. "Improving Memory Error Handling Using Linux" is a process oriented method to solve this problem by using the Linux kernel to disable (offline) faulty memory pages containing bad addresses, preventing them from being used again by a process. The process of offlining memory pages simplifies error handling and results in reducingmore » both hardware and manpower costs required to run Los Alamos National Laboratory (LANL) clusters. This process will be necessary for the future of supercomputing to allow the development of exascale computers. It will not be feasible without memory error handling to manually replace the number of DIMMs that will fail daily on a machine consisting of 32-128 petabytes of memory. Testing reveals the process of offlining memory pages works and is relatively simple to use. As more and more testing is conducted, the entire process will be automated within the high-performance computing (HPC) monitoring software, Zenoss, at LANL.« less

Time limits during visual foraging reveal flexible working memory templates.

PubMed

Kristjánsson, Tómas; Thornton, Ian M; Kristjánsson, Árni

2018-06-01

During difficult foraging tasks, humans rarely switch between target categories, but switch frequently during easier foraging. Does this reflect fundamental limits on visual working memory (VWM) capacity or simply strategic choice due to effort? Our participants performed time-limited or unlimited foraging tasks where they tapped stimuli from 2 target categories while avoiding items from 2 distractor categories. These time limits should have no effect if capacity imposes limits on VWM representations but more flexible VWM could allow observers to use VWM according to task demands in each case. We found that with time limits, participants switched more frequently and switch-costs became much smaller than during unlimited foraging. Observers can therefore switch between complex (conjunction) target categories when needed. We propose that while maintaining many complex templates in working memory is effortful and observers avoid this, they can do so if this fits task demands, showing the flexibility of working memory representations used for visual exploration. This is in contrast with recent proposals, and we discuss the implications of these findings for theoretical accounts of working memory. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The derivation and approximation of coarse-grained dynamics from Langevin dynamics

NASA Astrophysics Data System (ADS)

Ma, Lina; Li, Xiantao; Liu, Chun

2016-11-01

We present a derivation of a coarse-grained description, in the form of a generalized Langevin equation, from the Langevin dynamics model that describes the dynamics of bio-molecules. The focus is placed on the form of the memory kernel function, the colored noise, and the second fluctuation-dissipation theorem that connects them. Also presented is a hierarchy of approximations for the memory and random noise terms, using rational approximations in the Laplace domain. These approximations offer increasing accuracy. More importantly, they eliminate the need to evaluate the integral associated with the memory term at each time step. Direct sampling of the colored noise can also be avoided within this framework. Therefore, the numerical implementation of the generalized Langevin equation is much more efficient.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Frontotemporal Functional Connectivity and Executive Functions Contribute to Episodic Memory Performance

PubMed Central

Blankenship, Tashauna L.; O'Neill, Meagan; Deater-Deckard, Kirby; Diana, Rachel A.; Bell, Martha Ann

2016-01-01

The contributions of hemispheric-specific electrophysiology (electroencephalogram or EEG) and independent executive functions (inhibitory control, working memory, cognitive flexibility) to episodic memory performance were examined using abstract paintings. Right hemisphere frontotemporal functional connectivity during encoding and retrieval, measured via EEG alpha coherence, statistically predicted performance on recency but not recognition judgments for the abstract paintings. Theta coherence, however, did not predict performance. Likewise, cognitive flexibility statistically predicted performance on recency judgments, but not recognition. These findings suggest that recognition and recency operate via separate electrophysiological and executive mechanisms. PMID:27388478

Inverse halftoning via robust nonlinear filtering

NASA Astrophysics Data System (ADS)

Shen, Mei-Yin; Kuo, C.-C. Jay

1999-10-01

A new blind inverse halftoning algorithm based on a nonlinear filtering technique of low computational complexity and low memory requirement is proposed in this research. It is called blind since we do not require the knowledge of the halftone kernel. The proposed scheme performs nonlinear filtering in conjunction with edge enhancement to improve the quality of an inverse halftoned image. Distinct features of the proposed approach include: efficiently smoothing halftone patterns in large homogeneous areas, additional edge enhancement capability to recover the edge quality and an excellent PSNR performance with only local integer operations and a small memory buffer.

Detection of the "cp4 epsps" Gene in Maize Line NK603 and Comparison of Related Protein Structures: An Advanced Undergraduate Experiment

ERIC Educational Resources Information Center

Swope, Nicole K.; Fryfogle, Patrick J.; Sivy, Tami L.

2015-01-01

A flexible, rigorous laboratory experiment for upper-level biochemistry undergraduates is described that focuses on the Roundup Ready maize line. The work is appropriate for undergraduate laboratory courses that integrate biochemistry, molecular biology, or bioinformatics. In this experiment, DNA is extracted and purified from maize kernel and…

Kernel methods and flexible inference for complex stochastic dynamics

NASA Astrophysics Data System (ADS)

Capobianco, Enrico

2008-07-01

Approximation theory suggests that series expansions and projections represent standard tools for random process applications from both numerical and statistical standpoints. Such instruments emphasize the role of both sparsity and smoothness for compression purposes, the decorrelation power achieved in the expansion coefficients space compared to the signal space, and the reproducing kernel property when some special conditions are met. We consider these three aspects central to the discussion in this paper, and attempt to analyze the characteristics of some known approximation instruments employed in a complex application domain such as financial market time series. Volatility models are often built ad hoc, parametrically and through very sophisticated methodologies. But they can hardly deal with stochastic processes with regard to non-Gaussianity, covariance non-stationarity or complex dependence without paying a big price in terms of either model mis-specification or computational efficiency. It is thus a good idea to look at other more flexible inference tools; hence the strategy of combining greedy approximation and space dimensionality reduction techniques, which are less dependent on distributional assumptions and more targeted to achieve computationally efficient performances. Advantages and limitations of their use will be evaluated by looking at algorithmic and model building strategies, and by reporting statistical diagnostics.

A fast algorithm for forward-modeling of gravitational fields in spherical coordinates with 3D Gauss-Legendre quadrature

NASA Astrophysics Data System (ADS)

Zhao, G.; Liu, J.; Chen, B.; Guo, R.; Chen, L.

2017-12-01

Forward modeling of gravitational fields at large-scale requires to consider the curvature of the Earth and to evaluate the Newton's volume integral in spherical coordinates. To acquire fast and accurate gravitational effects for subsurface structures, subsurface mass distribution is usually discretized into small spherical prisms (called tesseroids). The gravity fields of tesseroids are generally calculated numerically. One of the commonly used numerical methods is the 3D Gauss-Legendre quadrature (GLQ). However, the traditional GLQ integration suffers from low computational efficiency and relatively poor accuracy when the observation surface is close to the source region. We developed a fast and high accuracy 3D GLQ integration based on the equivalence of kernel matrix, adaptive discretization and parallelization using OpenMP. The equivalence of kernel matrix strategy increases efficiency and reduces memory consumption by calculating and storing the same matrix elements in each kernel matrix just one time. In this method, the adaptive discretization strategy is used to improve the accuracy. The numerical investigations show that the executing time of the proposed method is reduced by two orders of magnitude compared with the traditional method that without these optimized strategies. High accuracy results can also be guaranteed no matter how close the computation points to the source region. In addition, the algorithm dramatically reduces the memory requirement by N times compared with the traditional method, where N is the number of discretization of the source region in the longitudinal direction. It makes the large-scale gravity forward modeling and inversion with a fine discretization possible.

Epitaxial Growth of Thin Ferroelectric Polymer Films on Graphene Layer for Fully Transparent and Flexible Nonvolatile Memory.

PubMed

Kim, Kang Lib; Lee, Wonho; Hwang, Sun Kak; Joo, Se Hun; Cho, Suk Man; Song, Giyoung; Cho, Sung Hwan; Jeong, Beomjin; Hwang, Ihn; Ahn, Jong-Hyun; Yu, Young-Jun; Shin, Tae Joo; Kwak, Sang Kyu; Kang, Seok Ju; Park, Cheolmin

2016-01-13

Enhancing the device performance of organic memory devices while providing high optical transparency and mechanical flexibility requires an optimized combination of functional materials and smart device architecture design. However, it remains a great challenge to realize fully functional transparent and mechanically durable nonvolatile memory because of the limitations of conventional rigid, opaque metal electrodes. Here, we demonstrate ferroelectric nonvolatile memory devices that use graphene electrodes as the epitaxial growth substrate for crystalline poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) polymer. The strong crystallographic interaction between PVDF-TrFE and graphene results in the orientation of the crystals with distinct symmetry, which is favorable for polarization switching upon the electric field. The epitaxial growth of PVDF-TrFE on a graphene layer thus provides excellent ferroelectric performance with high remnant polarization in metal/ferroelectric polymer/metal devices. Furthermore, a fully transparent and flexible array of ferroelectric field effect transistors was successfully realized by adopting transparent poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] semiconducting polymer.

Large-area, flexible imaging arrays constructed by light-charge organic memories

PubMed Central

Zhang, Lei; Wu, Ti; Guo, Yunlong; Zhao, Yan; Sun, Xiangnan; Wen, Yugeng; Yu, Gui; Liu, Yunqi

2013-01-01

Existing organic imaging circuits, which offer attractive benefits of light weight, low cost and flexibility, are exclusively based on phototransistor or photodiode arrays. One shortcoming of these photo-sensors is that the light signal should keep invariant throughout the whole pixel-addressing and reading process. As a feasible solution, we synthesized a new charge storage molecule and embedded it into a device, which we call light-charge organic memory (LCOM). In LCOM, the functionalities of photo-sensor and non-volatile memory are integrated. Thanks to the deliberate engineering of electronic structure and self-organization process at the interface, 92% of the stored charges, which are linearly controlled by the quantity of light, retain after 20000 s. The stored charges can also be non-destructively read and erased by a simple voltage program. These results pave the way to large-area, flexible imaging circuits and demonstrate a bright future of small molecular materials in non-volatile memory. PMID:23326636

Custom controls

NASA Astrophysics Data System (ADS)

Butell, Bart

1996-02-01

Microsoft's Visual Basic (VB) and Borland's Delphi provide an extremely robust programming environment for delivering multimedia solutions for interactive kiosks, games and titles. Their object oriented use of standard and custom controls enable a user to build extremely powerful applications. A multipurpose, database enabled programming environment that can provide an event driven interface functions as a multimedia kernel. This kernel can provide a variety of authoring solutions (e.g. a timeline based model similar to Macromedia Director or a node authoring model similar to Icon Author). At the heart of the kernel is a set of low level multimedia components providing object oriented interfaces for graphics, audio, video and imaging. Data preparation tools (e.g., layout, palette and Sprite Editors) could be built to manage the media database. The flexible interface for VB allows the construction of an infinite number of user models. The proliferation of these models within a popular, easy to use environment will allow the vast developer segment of 'producer' types to bring their ideas to the market. This is the key to building exciting, content rich multimedia solutions. Microsoft's VB and Borland's Delphi environments combined with multimedia components enable these possibilities.

Performance Prediction Toolkit

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

Chennupati, Gopinath; Santhi, Nanadakishore; Eidenbenz, Stephen

The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end. The PPT hardware model of a compute core (such as a Haswell core) consists of a set of parameters, such as clock speed, memory hierarchy levels, their respective sizes,more » cache-lines, access times for different cache levels, average cycle counts of ALU operations, etc. These parameters are ideally read off a spec sheet or are learned using regression models learned from hardware counters (PAPI) data. The compute core model offers an API to the software model, a function called time_compute(), which takes as input a tasklist. A tasklist is an unordered set of ALU, and other CPU-type operations (in particular virtual memory loads and stores). The PPT application model mimics the loop structure of the application and replaces the computational kernels with a call to the hardware model's time_compute() function giving tasklists as input that model the compute kernel. A PPT application model thus consists of tasklists representing kernels and the high-er level loop structure that we like to think of as pseudo code. The key challenge for the hardware model's time_compute-function is to translate virtual memory accesses into actual cache hierarchy level hits and misses.PPT also contains another CPU core level hardware model, Analytical Memory Model (AMM). The AMM solves this challenge soundly, where our previous alternatives explicitly include the L1,L2,L3 hit-rates as inputs to the tasklists. Explicit hit-rates inevitably only reflect the application modeler's best guess, perhaps informed by a few small test problems using hardware counters; also, hard-coded hit-rates make the hardware model insensitive to changes in cache sizes. Alternatively, we use reuse distance distributions in the tasklists. In general, reuse profiles require the application modeler to run a very expensive trace analysis on the real code that realistically can be done at best for small examples.« less

High-Performance Flexible Organic Nano-Floating Gate Memory Devices Functionalized with Cobalt Ferrite Nanoparticles.

PubMed

Jung, Ji Hyung; Kim, Sunghwan; Kim, Hyeonjung; Park, Jongnam; Oh, Joon Hak

2015-10-07

Nano-floating gate memory (NFGM) devices are transistor-type memory devices that use nanostructured materials as charge trap sites. They have recently attracted a great deal of attention due to their excellent performance, capability for multilevel programming, and suitability as platforms for integrated circuits. Herein, novel NFGM devices have been fabricated using semiconducting cobalt ferrite (CoFe2O4) nanoparticles (NPs) as charge trap sites and pentacene as a p-type semiconductor. Monodisperse CoFe2O4 NPs with different diameters have been synthesized by thermal decomposition and embedded in NFGM devices. The particle size effects on the memory performance have been investigated in terms of energy levels and particle-particle interactions. CoFe2O4 NP-based memory devices exhibit a large memory window (≈73.84 V), a high read current on/off ratio (read I(on)/I(off)) of ≈2.98 × 10(3), and excellent data retention. Fast switching behaviors are observed due to the exceptional charge trapping/release capability of CoFe2O4 NPs surrounded by the oleate layer, which acts as an alternative tunneling dielectric layer and simplifies the device fabrication process. Furthermore, the NFGM devices show excellent thermal stability, and flexible memory devices fabricated on plastic substrates exhibit remarkable mechanical and electrical stability. This study demonstrates a viable means of fabricating highly flexible, high-performance organic memory devices. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modified kernel-based nonlinear feature extraction.

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

Ma, J.; Perkins, S. J.; Theiler, J. P.

2002-01-01

Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determinedmore » by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.« less

Structured functional additive regression in reproducing kernel Hilbert spaces.

PubMed

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2014-06-01

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Shifting Visual Perspective During Retrieval Shapes Autobiographical Memories

PubMed Central

St Jacques, Peggy L.; Szpunar, Karl K.; Schacter, Daniel L.

2016-01-01

The dynamic and flexible nature of memories is evident in our ability to adopt multiple visual perspectives. Although autobiographical memories are typically encoded from the visual perspective of our own eyes they can be retrieved from the perspective of an observer looking at our self. Here, we examined the neural mechanisms of shifting visual perspective during long-term memory retrieval and its influence on online and subsequent memories using functional magnetic resonance imaging (fMRI). Participants generated specific autobiographical memories from the last five years and rated their visual perspective. In a separate fMRI session, they were asked to retrieve the memories across three repetitions while maintaining the same visual perspective as their initial rating or by shifting to an alternative perspective. Visual perspective shifting during autobiographical memory retrieval was supported by a linear decrease in neural recruitment across repetitions in the posterior parietal cortices. Additional analyses revealed that the precuneus, in particular, contributed to both online and subsequent changes in the phenomenology of memories. Our findings show that flexibly shifting egocentric perspective during autobiographical memory retrieval is supported by the precuneus, and suggest that this manipulation of mental imagery during retrieval has consequences for how memories are retrieved and later remembered. PMID:27989780

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

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

Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less

Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

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

Gottwald, Fabian; Karsten, Sven; Ivanov, Sergei D., E-mail: sergei.ivanov@uni-rostock.de

2015-06-28

Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied,more » usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.« less

Transparent and flexible resistive switching memory devices with a very high ON/OFF ratio using gold nanoparticles embedded in a silk protein matrix

NASA Astrophysics Data System (ADS)

Gogurla, Narendar; Mondal, Suvra P.; Sinha, Arun K.; Katiyar, Ajit K.; Banerjee, Writam; Kundu, Subhas C.; Ray, Samit K.

2013-08-01

The growing demand for biomaterials for electrical and optical devices is motivated by the need to make building blocks for the next generation of printable bio-electronic devices. In this study, transparent and flexible resistive memory devices with a very high ON/OFF ratio incorporating gold nanoparticles into the Bombyx mori silk protein fibroin biopolymer are demonstrated. The novel electronic memory effect is based on filamentary switching, which leads to the occurrence of bistable states with an ON/OFF ratio larger than six orders of magnitude. The mechanism of this process is attributed to the formation of conductive filaments through silk fibroin and gold nanoparticles in the nanocomposite. The proposed hybrid bio-inorganic devices show promise for use in future flexible and transparent nanoelectronic systems.

High-performance flexible resistive memory devices based on Al2O3:GeOx composite

NASA Astrophysics Data System (ADS)

Behera, Bhagaban; Maity, Sarmistha; Katiyar, Ajit K.; Das, Samaresh

2018-05-01

In this study a resistive switching random access memory device using Al2O3:GeOx composite thin films on flexible substrate is presented. A bipolar switching characteristic was observed for the co-sputter deposited Al2O3:GeOx composite thin films. Al/Al2O3:GeOx/ITO/PET memory device shows excellent ON/OFF ratio (∼104) and endurance (>500 cycles). GeOx nanocrystals embedded in the Al2O3 matrix have been found to play a significant role in enhancing the switching characteristics by facilitating oxygen vacancy formation. Mechanical endurance was retained even after several bending. The conduction mechanism of the device was qualitatively discussed by considering Ohmic and SCLC conduction. This flexible device is a potential candidate for next-generation electronics device.

Recent Advances of Flexible Data Storage Devices Based on Organic Nanoscaled Materials.

PubMed

Zhou, Li; Mao, Jingyu; Ren, Yi; Han, Su-Ting; Roy, Vellaisamy A L; Zhou, Ye

2018-03-01

Following the trend of miniaturization as per Moore's law, and facing the strong demand of next-generation electronic devices that should be highly portable, wearable, transplantable, and lightweight, growing endeavors have been made to develop novel flexible data storage devices possessing nonvolatile ability, high-density storage, high-switching speed, and reliable endurance properties. Nonvolatile organic data storage devices including memory devices on the basis of floating-gate, charge-trapping, and ferroelectric architectures, as well as organic resistive memory are believed to be favorable candidates for future data storage applications. In this Review, typical information on device structure, memory characteristics, device operation mechanisms, mechanical properties, challenges, and recent progress of the above categories of flexible data storage devices based on organic nanoscaled materials is summarized. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

From dead leaves to sustainable organic resistive switching memory.

PubMed

Sun, Bai; Zhu, Shouhui; Mao, Shuangsuo; Zheng, Pingping; Xia, Yudong; Yang, Feng; Lei, Ming; Zhao, Yong

2018-03-01

An environmental-friendly, sustainable, pollution-free, biodegradable, flexible and wearable electronic device hold advanced potential applications. Here, an organic resistive switching memory device with Ag/Leaves/Ti/PET structure on a flexible polyethylene terephthalate (PET) substrate was fabricated for the first time. We observed an obvious resistive switching memory characteristic with large switching resistance ratio and stable cycle performance at room temperature. This work demonstrates that leaves, a useless waste, can be properly treated to make useful devices. Furthermore, the as-fabricated devices can be degraded naturally without damage to the environment. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictors of early growth in academic achievement: the head-toes-knees-shoulders task

PubMed Central

McClelland, Megan M.; Cameron, Claire E.; Duncan, Robert; Bowles, Ryan P.; Acock, Alan C.; Miao, Alicia; Pratt, Megan E.

2014-01-01

Children's behavioral self-regulation and executive function (EF; including attentional or cognitive flexibility, working memory, and inhibitory control) are strong predictors of academic achievement. The present study examined the psychometric properties of a measure of behavioral self-regulation called the Head-Toes-Knees-Shoulders (HTKS) by assessing construct validity, including relations to EF measures, and predictive validity to academic achievement growth between prekindergarten and kindergarten. In the fall and spring of prekindergarten and kindergarten, 208 children (51% enrolled in Head Start) were assessed on the HTKS, measures of cognitive flexibility, working memory (WM), and inhibitory control, and measures of emergent literacy, mathematics, and vocabulary. For construct validity, the HTKS was significantly related to cognitive flexibility, working memory, and inhibitory control in prekindergarten and kindergarten. For predictive validity in prekindergarten, a random effects model indicated that the HTKS significantly predicted growth in mathematics, whereas a cognitive flexibility task significantly predicted growth in mathematics and vocabulary. In kindergarten, the HTKS was the only measure to significantly predict growth in all academic outcomes. An alternative conservative analytical approach, a fixed effects analysis (FEA) model, also indicated that growth in both the HTKS and measures of EF significantly predicted growth in mathematics over four time points between prekindergarten and kindergarten. Results demonstrate that the HTKS involves cognitive flexibility, working memory, and inhibitory control, and is substantively implicated in early achievement, with the strongest relations found for growth in achievement during kindergarten and associations with emergent mathematics. PMID:25071619

6 DOF Nonlinear AUV Simulation Toolbox

DTIC Science & Technology

1997-01-01

is to supply a flexible 3D -simulation platform for motion visualization, in-lab debugging and testing of mission-specific strategies as well as those...Explorer are modular designed [Smith] in order to cut time and cost for vehicle recontlguration. A flexible 3D -simulation platform is desired to... 3D models. Current implemented modules include a nonlinear dynamic model for the OEX, shared memory and semaphore manager tools, shared memory monitor

Cost-effective, transfer-free, flexible resistive random access memory using laser-scribed reduced graphene oxide patterning technology.

PubMed

Tian, He; Chen, Hong-Yu; Ren, Tian-Ling; Li, Cheng; Xue, Qing-Tang; Mohammad, Mohammad Ali; Wu, Can; Yang, Yi; Wong, H-S Philip

2014-06-11

Laser scribing is an attractive reduced graphene oxide (rGO) growth and patterning technology because the process is low-cost, time-efficient, transfer-free, and flexible. Various laser-scribed rGO (LSG) components such as capacitors, gas sensors, and strain sensors have been demonstrated. However, obstacles remain toward practical application of the technology where all the components of a system are fabricated using laser scribing. Memory components, if developed, will substantially broaden the application space of low-cost, flexible electronic systems. For the first time, a low-cost approach to fabricate resistive random access memory (ReRAM) using laser-scribed rGO as the bottom electrode is experimentally demonstrated. The one-step laser scribing technology allows transfer-free rGO synthesis directly on flexible substrates or non-flat substrates. Using this time-efficient laser-scribing technology, the patterning of a memory-array area up to 100 cm(2) can be completed in 25 min. Without requiring the photoresist coating for lithography, the surface of patterned rGO remains as clean as its pristine state. Ag/HfOx/LSG ReRAM using laser-scribing technology is fabricated in this work. Comprehensive electrical characteristics are presented including forming-free behavior, stable switching, reasonable reliability performance and potential for 2-bit storage per memory cell. The results suggest that laser-scribing technology can potentially produce more cost-effective and time-effective rGO-based circuits and systems for practical applications.

Implementation and performance of parallel Prolog interpreter

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

Wei, S.; Kale, L.V.; Balkrishna, R.

1988-01-01

In this paper, the authors discuss the implementation of a parallel Prolog interpreter on different parallel machines. The implementation is based on the REDUCE--OR process model which exploits both AND and OR parallelism in logic programs. It is machine independent as it runs on top of the chare-kernel--a machine-independent parallel programming system. The authors also give the performance of the interpreter running a diverse set of benchmark pargrams on parallel machines including shared memory systems: an Alliant FX/8, Sequent and a MultiMax, and a non-shared memory systems: Intel iPSC/32 hypercube, in addition to its performance on a multiprocessor simulation system.

SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

NASA Astrophysics Data System (ADS)

Jiang, W.; Clune, T.; Vriesema, J.; Gutmann, G.

2013-12-01

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community. We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical 'kernels' that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures. In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction.

Multi-board kernel communication using socket programming for embedded applications

NASA Astrophysics Data System (ADS)

Mishra, Ashish; Girdhar, Neha; Krishnia, Nikita

2016-03-01

It is often seen in large application projects, there is a need to communicate between two different processors or two different kernels. The aim of this paper is to communicate between two different kernels and use efficient method to do so. The TCP/IP protocol is implemented to communicate between two boards via the Ethernet port and use lwIP (lightweight IP) stack, which is a smaller independent implementation of the TCP/IP stack suitable for use in embedded systems. While retaining TCP/IP functionality, lwIP stack reduces the use of memory and even size of the code. In this process of communication we made Raspberry pi as an active client and Field programmable gate array(FPGA) board as a passive server and they are allowed to communicate via Ethernet. Three applications based on TCP/IP client-server network communication have been implemented. The Echo server application is used to communicate between two different kernels of two different boards. Socket programming is used as it is independent of platform and programming language used. TCP transmit and receive throughput test applications are used to measure maximum throughput of the transmission of data. These applications are based on communication to an open source tool called iperf. It is used to measure the throughput transmission rate by sending or receiving some constant piece of data to the client or server according to the test application.

Initial Kernel Timing Using a Simple PIM Performance Model

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Block, Gary L.; Springer, Paul L.; Sterling, Thomas; Brockman, Jay B.; Callahan, David

2005-01-01

This presentation will describe some initial results of paper-and-pencil studies of 4 or 5 application kernels applied to a processor-in-memory (PIM) system roughly similar to the Cascade Lightweight Processor (LWP). The application kernels are: * Linked list traversal * Sun of leaf nodes on a tree * Bitonic sort * Vector sum * Gaussian elimination The intent of this work is to guide and validate work on the Cascade project in the areas of compilers, simulators, and languages. We will first discuss the generic PIM structure. Then, we will explain the concepts needed to program a parallel PIM system (locality, threads, parcels). Next, we will present a simple PIM performance model that will be used in the remainder of the presentation. For each kernel, we will then present a set of codes, including codes for a single PIM node, and codes for multiple PIM nodes that move data to threads and move threads to data. These codes are written at a fairly low level, between assembly and C, but much closer to C than to assembly. For each code, we will present some hand-drafted timing forecasts, based on the simple PIM performance model. Finally, we will conclude by discussing what we have learned from this work, including what programming styles seem to work best, from the point-of-view of both expressiveness and performance.

Neural correlates of reappraisal considering working memory capacity and cognitive flexibility.

PubMed

Zaehringer, Jenny; Falquez, Rosalux; Schubert, Anna-Lena; Nees, Frauke; Barnow, Sven

2018-01-09

Cognitive reappraisal of emotion is strongly related to long-term mental health. Therefore, the exploration of underlying cognitive and neural mechanisms has become an essential focus of research. Considering that reappraisal and executive functions rely on a similar brain network, the question arises whether behavioral differences in executive functions modulate neural activity during reappraisal. Using functional neuroimaging, the present study aimed to analyze the role of working memory capacity (WMC) and cognitive flexibility in brain activity during down-regulation of negative emotions by reappraisal in N = 20 healthy participants. Results suggests that WMC and cognitive flexibility were negatively correlated with prefrontal activity during reappraisal condition. Here, results also revealed a negative correlation between cognitive flexibility and amygdala activation. These findings provide first hints that (1) individuals with lower WMC and lower cognitive flexibility might need more higher-order cognitive neural resources in order to down-regulate negative emotions and (2) cognitive flexibility relates to emotional reactivity during reappraisal.

Attention-deficit/hyperactivity disorder: the impact of methylphenidate on working memory, inhibition capacity and mental flexibility.

PubMed

Bolfer, Cristiana; Pacheco, Sandra Pasquali; Tsunemi, Miriam Harumi; Carreira, Walter Souza; Casella, Beatriz Borba; Casella, Erasmo Barbante

2017-04-01

To compare children with attention-deficit/hyperactivity disorder (ADHD), before and after the use of methylphenidate, and a control group, using tests of working memory, inhibition capacity and mental flexibility. Neuropsychological tests were administrated to 53 boys, 9-12 years old: the WISC-III digit span backward, and arithmetic; Stroop Color; and Trail Making Tests. The case group included 23 boys with ADHD, who were combined type, treatment-naive, and with normal intelligence without comorbidities. The control group (n = 30) were age and gender matched. After three months on methylphenidate, the ADHD children were retested. The control group was also retested after three months. Before treatment, ADHD children had lower scores than the control group on the tests (p ≤ 0.001) and after methylphenidate had fewer test errors than before (p ≤ 0.001). Methylphenidate treatment improves the working memory, inhibitory control and mental flexibility of ADHD boys.

Executive control deficits in substance-dependent individuals: a comparison of alcohol, cocaine, and methamphetamine and of men and women.

PubMed

van der Plas, Ellen A A; Crone, Eveline A; van den Wildenberg, Wery P M; Tranel, Daniel; Bechara, Antoine

2009-08-01

Substance dependence is associated with executive function deficits, but the nature of these executive defects and the effect that different drugs and sex have on these defects have not been fully clarified. Therefore, we compared the performance of alcohol- (n = 33; 18 women), cocaine- (n = 27; 14 women), and methamphetamine-dependent individuals (n = 38; 25 women) with sex-matched healthy comparisons (n = 36; 17 women) on complex decision making as measured with the Iowa Gambling Task, working memory, cognitive flexibility, and response inhibition. Cocaine- and methamphetamine-dependent individuals were impaired on complex decision making, working memory, and cognitive flexibility, but not on response inhibition. The deficits in working memory and cognitive flexibility were milder than the decision-making deficits and did not change as a function of memory load or task switching. Interestingly, decision making was significantly more impaired in women addicted to cocaine or methamphetamine than in men addicted to these drugs. Together, these findings suggest that drug of choice and sex have different effects on executive functioning, which, if replicated, may help tailor intervention.

FastMag: Fast micromagnetic simulator for complex magnetic structures (invited)

NASA Astrophysics Data System (ADS)

Chang, R.; Li, S.; Lubarda, M. V.; Livshitz, B.; Lomakin, V.

2011-04-01

A fast micromagnetic simulator (FastMag) for general problems is presented. FastMag solves the Landau-Lifshitz-Gilbert equation and can handle multiscale problems with a high computational efficiency. The simulator derives its high performance from efficient methods for evaluating the effective field and from implementations on massively parallel graphics processing unit (GPU) architectures. FastMag discretizes the computational domain into tetrahedral elements and therefore is highly flexible for general problems. The magnetostatic field is computed via the superposition principle for both volume and surface parts of the computational domain. This is accomplished by implementing efficient quadrature rules and analytical integration for overlapping elements in which the integral kernel is singular. Thus, discretized superposition integrals are computed using a nonuniform grid interpolation method, which evaluates the field from N sources at N collocated observers in O(N) operations. This approach allows handling objects of arbitrary shape, allows easily calculating of the field outside the magnetized domains, does not require solving a linear system of equations, and requires little memory. FastMag is implemented on GPUs with ?> GPU-central processing unit speed-ups of 2 orders of magnitude. Simulations are shown of a large array of magnetic dots and a recording head fully discretized down to the exchange length, with over a hundred million tetrahedral elements on an inexpensive desktop computer.

Research and Development of Collaborative Environments for Command and Control

DTIC Science & Technology

2011-05-01

at any state of building. The viewer tool presents the designed model with 360-degree perspective views even after regeneration of the design, which...and it shows the following prompt. GUM > APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED...11 First initialize the microSD card by typing GUM > mmcinit Then erase the old Linux kernel and the root file system on the flash memory

A mechatronics platform to study prosthetic hand control using EMG signals.

PubMed

Geethanjali, P

2016-09-01

In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time. This prototype facilitated the study of various issues of pattern recognition and identified an efficient classifier, along with a feature ensemble, in the implementation of EMG controlled prosthetic hands in a laboratory setting at low-cost. This platform may help to motivate and facilitate prosthetic hand research in developing countries.

Flexible non-volatile memory devices based on organic semiconductors

NASA Astrophysics Data System (ADS)

Cosseddu, Piero; Casula, Giulia; Lai, Stefano; Bonfiglio, Annalisa

2015-09-01

The possibility of developing fully organic electronic circuits is critically dependent on the ability to realize a full set of electronic functionalities based on organic devices. In order to complete the scene, a fundamental element is still missing, i.e. reliable data storage. Over the past few years, a considerable effort has been spent on the development and optimization of organic polymer based memory elements. Among several possible solutions, transistor-based memories and resistive switching-based memories are attracting a great interest in the scientific community. In this paper, a route for the fabrication of organic semiconductor-based memory devices with performances beyond the state of the art is reported. Both the families of organic memories will be considered. A flexible resistive memory based on a novel combination of materials is presented. In particular, high retention time in ambient conditions are reported. Complementary, a low voltage transistor-based memory is presented. Low voltage operation is allowed by an hybrid, nano-sized dielectric, which is also responsible for the memory effect in the device. Thanks to the possibility of reproducibly fabricating such device on ultra-thin substrates, high mechanical stability is reported.

P- and S-wave Receiver Function Imaging with Scattering Kernels

NASA Astrophysics Data System (ADS)

Hansen, S. M.; Schmandt, B.

2017-12-01

Full waveform inversion provides a flexible approach to the seismic parameter estimation problem and can account for the full physics of wave propagation using numeric simulations. However, this approach requires significant computational resources due to the demanding nature of solving the forward and adjoint problems. This issue is particularly acute for temporary passive-source seismic experiments (e.g. PASSCAL) that have traditionally relied on teleseismic earthquakes as sources resulting in a global scale forward problem. Various approximation strategies have been proposed to reduce the computational burden such as hybrid methods that embed a heterogeneous regional scale model in a 1D global model. In this study, we focus specifically on the problem of scattered wave imaging (migration) using both P- and S-wave receiver function data. The proposed method relies on body-wave scattering kernels that are derived from the adjoint data sensitivity kernels which are typically used for full waveform inversion. The forward problem is approximated using ray theory yielding a computationally efficient imaging algorithm that can resolve dipping and discontinuous velocity interfaces in 3D. From the imaging perspective, this approach is closely related to elastic reverse time migration. An energy stable finite-difference method is used to simulate elastic wave propagation in a 2D hypothetical subduction zone model. The resulting synthetic P- and S-wave receiver function datasets are used to validate the imaging method. The kernel images are compared with those generated by the Generalized Radon Transform (GRT) and Common Conversion Point stacking (CCP) methods. These results demonstrate the potential of the kernel imaging approach to constrain lithospheric structure in complex geologic environments with sufficiently dense recordings of teleseismic data. This is demonstrated using a receiver function dataset from the Central California Seismic Experiment which shows several dipping interfaces related to the tectonic assembly of this region. Figure 1. Scattering kernel examples for three receiver function phases. A) direct P-to-s (Ps), B) direct S-to-p and C) free-surface PP-to-s (PPs).

Structured functional additive regression in reproducing kernel Hilbert spaces

PubMed Central

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2013-01-01

Summary Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application. PMID:25013362

A data transmission method for particle physics experiments based on Ethernet physical layer

NASA Astrophysics Data System (ADS)

Huang, Xi-Ru; Cao, Ping; Zheng, Jia-Jun

2015-11-01

Due to its advantages of universality, flexibility and high performance, fast Ethernet is widely used in readout system design for modern particle physics experiments. However, Ethernet is usually used together with the TCP/IP protocol stack, which makes it difficult to implement readout systems because designers have to use the operating system to process this protocol. Furthermore, TCP/IP degrades the transmission efficiency and real-time performance. To maximize the performance of Ethernet in physics experiment applications, a data readout method based on the physical layer (PHY) is proposed. In this method, TCP/IP is replaced with a customized and simple protocol, which makes it easier to implement. On each readout module, data from the front-end electronics is first fed into an FPGA for protocol processing and then sent out to a PHY chip controlled by this FPGA for transmission. This kind of data path is fully implemented by hardware. From the side of the data acquisition system (DAQ), however, the absence of a standard protocol causes problems for the network related applications. To solve this problem, in the operating system kernel space, data received by the network interface card is redirected from the traditional flow to a specified memory space by a customized program. This memory space can easily be accessed by applications in user space. For the purpose of verification, a prototype system has been designed and implemented. Preliminary test results show that this method can meet the requirements of data transmission from the readout module to the DAQ with an efficient and simple manner. Supported by National Natural Science Foundation of China (11005107) and Independent Projects of State Key Laboratory of Particle Detection and Electronics (201301)

High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors

NASA Technical Reports Server (NTRS)

NguyenKobayashi, Kayla; Zheng, Jason X.; He, Yutao; Shah, Biren N.

2011-01-01

Exponential growth in microelectronics technology such as field-programmable gate arrays (FPGAs) has enabled high-performance spaceborne instruments with increasing onboard data processing capabilities. As a commonly used digital signal processing (DSP) building block, fast Fourier transform (FFT) has been of great interest in onboard data processing applications, which needs to strike a reasonable balance between high-performance (throughput, block size, etc.) and low resource usage (power, silicon footprint, etc.). It is also desirable to be designed so that a single design can be reused and adapted into instruments with different requirements. The Multi-Pass Wide Kernel FFT (MPWK-FFT) architecture was developed, in which the high-throughput benefits of the parallel FFT structure and the low resource usage of Singleton s single butterfly method is exploited. The result is a wide-kernel, multipass, adaptive FFT architecture. The 32K-point MPWK-FFT architecture includes 32 radix-2 butterflies, 64 FIFOs to store the real inputs, 64 FIFOs to store the imaginary inputs, complex twiddle factor storage, and FIFO logic to route the outputs to the correct FIFO. The inputs are stored in sequential fashion into the FIFOs, and the outputs of each butterfly are sequentially written first into the even FIFO, then the odd FIFO. Because of the order of the outputs written into the FIFOs, the depth of the even FIFOs, which are 768 each, are 1.5 times larger than the odd FIFOs, which are 512 each. The total memory needed for data storage, assuming that each sample is 36 bits, is 2.95 Mbits. The twiddle factors are stored in internal ROM inside the FPGA for fast access time. The total memory size to store the twiddle factors is 589.9Kbits. This FFT structure combines the benefits of high throughput from the parallel FFT kernels and low resource usage from the multi-pass FFT kernels with desired adaptability. Space instrument missions that need onboard FFT capabilities such as the proposed DESDynl, SWOT (Surface Water Ocean Topography), and Europa sounding radar missions would greatly benefit from this technology with significant reductions in non-recurring cost and risk.

Real Time Linux - The RTOS for Astronomy?

NASA Astrophysics Data System (ADS)

Daly, P. N.

The BoF was attended by about 30 participants and a free CD of real time Linux-based upon RedHat 5.2-was available. There was a detailed presentation on the nature of real time Linux and the variants for hard real time: New Mexico Tech's RTL and DIAPM's RTAI. Comparison tables between standard Linux and real time Linux responses to time interval generation and interrupt response latency were presented (see elsewhere in these proceedings). The present recommendations are to use RTL for UP machines running the 2.0.x kernels and RTAI for SMP machines running the 2.2.x kernel. Support, both academically and commercially, is available. Some known limitations were presented and the solutions reported e.g., debugging and hardware support. The features of RTAI (scheduler, fifos, shared memory, semaphores, message queues and RPCs) were described. Typical performance statistics were presented: Pentium-based oneshot tasks running > 30kHz, 486-based oneshot tasks running at ~ 10 kHz, periodic timer tasks running in excess of 90 kHz with average zero jitter peaking to ~ 13 mus (UP) and ~ 30 mus (SMP). Some detail on kernel module programming, including coding examples, were presented showing a typical data acquisition system generating simulated (random) data writing to a shared memory buffer and a fifo buffer to communicate between real time Linux and user space. All coding examples were complete and tested under RTAI v0.6 and the 2.2.12 kernel. Finally, arguments were raised in support of real time Linux: it's open source, free under GPL, enables rapid prototyping, has good support and the ability to have a fully functioning workstation capable of co-existing hard real time performance. The counter weight-the negatives-of lack of platforms (x86 and PowerPC only at present), lack of board support, promiscuous root access and the danger of ignorance of real time programming issues were also discussed. See ftp://orion.tuc.noao.edu/pub/pnd/rtlbof.tgz for the StarOffice overheads for this presentation.

Balancing accuracy, efficiency, and flexibility in a radiative transfer parameterization for dynamical models

NASA Astrophysics Data System (ADS)

Pincus, R.; Mlawer, E. J.

2017-12-01

Radiation is key process in numerical models of the atmosphere. The problem is well-understood and the parameterization of radiation has seen relatively few conceptual advances in the past 15 years. It is nonthelss often the single most expensive component of all physical parameterizations despite being computed less frequently than other terms. This combination of cost and maturity suggests value in a single radiation parameterization that could be shared across models; devoting effort to a single parameterization might allow for fine tuning for efficiency. The challenge lies in the coupling of this parameterization to many disparate representations of clouds and aerosols. This talk will describe RRTMGP, a new radiation parameterization that seeks to balance efficiency and flexibility. This balance is struck by isolating computational tasks in "kernels" that expose as much fine-grained parallelism as possible. These have simple interfaces and are interoperable across programming languages so that they might be repalced by alternative implementations in domain-specific langauges. Coupling to the host model makes use of object-oriented features of Fortran 2003, minimizing branching within the kernels and the amount of data that must be transferred. We will show accuracy and efficiency results for a globally-representative set of atmospheric profiles using a relatively high-resolution spectral discretization.

Correlates of Neuropsychological Impairment in Older Adult Pain Clinic Patients

PubMed Central

Karp, Jordan F.; Reynolds, Charles F.; Butters, Meryl; Dew, Mary Amanda; Mazumdar, Sati; Begley, Amy E.; Lenze, Eric; Weiner, Debra K.

2010-01-01

Objective Persistent pain and cognitive impairment are common in older adults. Memory and mental flexibility are cognitive domains which may be vulnerable in the aging brain. We were interested in examining the effects of persistent pain and opioid use on cognition in community dwelling, non-demented older adults. Setting Older Adult Pain Management Program. Design 57 new patients (mean age 76.1) were recruited to describe 1) rates of persistent pain conditions and pain intensity, 2) cognition (memory and mental flexibility), 3) rates and severity of depression, and 4) sleep quality. All patients had non-malignant pain for at least 3 months. Pain intensity was measured with the McGill Pain Questionnaire. Diagnosis of depression was via the Patient Health Questionnaire and depression severity assessed with the Hamilton Rating Scale for Depression. Cognition was assessed with: 1) Mini Mental State Examination, 2) number-letter-switching and motor speed trail-making subtests, 3) Digit Symbol Subtest of the WAIS-R, and 4) free and paired recall of the WAIS-R. To determine which variables predicted poorer outcomes on mental flexibility tests, these variables were entered into a multiple regression. Results Pain severity was associated with impaired number-letter switching (r = −0.42, p = 0.002). Multiple regression showed pain severity was associated with impaired mental flexibility (parameter estimate = −0.29 (t = −2.00), p = 0.05). Patients taking opioids had worse memory (t = 2.17, df = 39, p = 0.04). Conclusions In community-dwelling older adults, pain severity is associated with impaired mental flexibility. In addition, opioids may increase memory problems. PMID:17014605

Effects of β-hydroxy-β-methyl butyrate on working memory and cognitive flexibility in an animal model of aging.

PubMed

Hankosky, Emily R; Sherrill, Luke K; Ruvola, Lauren A; Haake, Rachel M; Kim, Taehyeon; Hammerslag, Lindsey R; Kougias, Daniel G; Juraska, Janice M; Gulley, Joshua M

2017-09-01

Normal aging results in cognitive decline and nutritional interventions have been suggested as potential approaches for mitigating these deficits. Here, we used rats to investigate the effects of short- and long-term dietary supplementation with the leucine metabolite β-hydroxy-β-methyl butyrate (HMB) on working memory and cognitive flexibility. Beginning ∼12 months of age, male and female Long-Evans rats were given twice daily access to sipper tubes containing calcium HMB (450 mg/kg) or vehicle (285 mg/kg calcium lactate) in a sucrose solution (20% w/v). Supplementation continued for 1 or 7 months (middle- and old-age (OA) groups, respectively) before testing began. Working memory was assessed by requiring rats to respond on a previously sampled lever following various delays. Cognitive flexibility was assessed by training rats to earn food according to a visual strategy and then, once acquired, shifting to an egocentric response strategy. Treatment with HMB improved working memory performance in middle-age (MA) males and OA rats of both sexes. In the cognitive flexibility task, there was a significant age-dependent deficit in acquisition of the visual strategy that was not apparent in OA males treated with HMB. Furthermore, HMB ameliorated an apparent deficit in visual strategy acquisition in MA females. Together, these findings suggest that daily nutritional supplementation with HMB facilitates learning and improves working memory performance. As such, HMB supplementation may mitigate age-related cognitive deficits and may therefore be an effective tool to combat this undesirable feature of the aging process.

System-Level Integration of Mass Memory

NASA Technical Reports Server (NTRS)

Cox, Brian; Mellstrom, Jeffrey; Wysocky, Terry

2008-01-01

A report discusses integrating multiple memory modules on the high-speed serial interconnect (IEEE 1393) that is used by a spacecraft?s inter-module communications in order to ease data congestion and provide for a scalable, strong, flexible system that can meet new system-level mass memory requirements.

Generalized and efficient algorithm for computing multipole energies and gradients based on Cartesian tensors

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

Lin, Dejun, E-mail: dejun.lin@gmail.com

2015-09-21

Accurate representation of intermolecular forces has been the central task of classical atomic simulations, known as molecular mechanics. Recent advancements in molecular mechanics models have put forward the explicit representation of permanent and/or induced electric multipole (EMP) moments. The formulas developed so far to calculate EMP interactions tend to have complicated expressions, especially in Cartesian coordinates, which can only be applied to a specific kernel potential function. For example, one needs to develop a new formula each time a new kernel function is encountered. The complication of these formalisms arises from an intriguing and yet obscured mathematical relation between themore » kernel functions and the gradient operators. Here, I uncover this relation via rigorous derivation and find that the formula to calculate EMP interactions is basically invariant to the potential kernel functions as long as they are of the form f(r), i.e., any Green’s function that depends on inter-particle distance. I provide an algorithm for efficient evaluation of EMP interaction energies, forces, and torques for any kernel f(r) up to any arbitrary rank of EMP moments in Cartesian coordinates. The working equations of this algorithm are essentially the same for any kernel f(r). Recently, a few recursive algorithms were proposed to calculate EMP interactions. Depending on the kernel functions, the algorithm here is about 4–16 times faster than these algorithms in terms of the required number of floating point operations and is much more memory efficient. I show that it is even faster than a theoretically ideal recursion scheme, i.e., one that requires 1 floating point multiplication and 1 addition per recursion step. This algorithm has a compact vector-based expression that is optimal for computer programming. The Cartesian nature of this algorithm makes it fit easily into modern molecular simulation packages as compared with spherical coordinate-based algorithms. A software library based on this algorithm has been implemented in C++11 and has been released.« less

Functional neuroimaging correlates of thinking flexibility and knowledge structure in memory: Exploring the relationships between clinical reasoning and diagnostic thinking.

PubMed

Durning, Steven J; Costanzo, Michelle E; Beckman, Thomas J; Artino, Anthony R; Roy, Michael J; van der Vleuten, Cees; Holmboe, Eric S; Lipner, Rebecca S; Schuwirth, Lambert

2016-06-01

Diagnostic reasoning involves the thinking steps up to and including arrival at a diagnosis. Dual process theory posits that a physician's thinking is based on both non-analytic or fast, subconscious thinking and analytic thinking that is slower, more conscious, effortful and characterized by comparing and contrasting alternatives. Expertise in clinical reasoning may relate to the two dimensions measured by the diagnostic thinking inventory (DTI): memory structure and flexibility in thinking. Explored the functional magnetic resonance imaging (fMRI) correlates of these two aspects of the DTI: memory structure and flexibility of thinking. Participants answered and reflected upon multiple-choice questions (MCQs) during fMRI. A DTI was completed shortly after the scan. The brain processes associated with the two dimensions of the DTI were correlated with fMRI phases - assessing flexibility in thinking during analytical clinical reasoning, memory structure during non-analytical clinical reasoning and the total DTI during both non-analytical and analytical reasoning in experienced physicians. Each DTI component was associated with distinct functional neuroanatomic activation patterns, particularly in the prefrontal cortex. Our findings support diagnostic thinking conceptual models and indicate mechanisms through which cognitive demands may induce functional adaptation within the prefrontal cortex. This provides additional objective validity evidence for the use of the DTI in medical education and practice settings.

Memory effects on a resonate-and-fire neuron model subjected to Ornstein-Uhlenbeck noise

NASA Astrophysics Data System (ADS)

Paekivi, S.; Mankin, R.; Rekker, A.

2017-10-01

We consider a generalized Langevin equation with an exponentially decaying memory kernel as a model for the firing process of a resonate-and-fire neuron. The effect of temporally correlated random neuronal input is modeled as Ornstein-Uhlenbeck noise. In the noise-induced spiking regime of the neuron, we derive exact analytical formulas for the dependence of some statistical characteristics of the output spike train, such as the probability distribution of the interspike intervals (ISIs) and the survival probability, on the parameters of the input stimulus. Particularly, on the basis of these exact expressions, we have established sufficient conditions for the occurrence of memory-time-induced transitions between unimodal and multimodal structures of the ISI density and a critical damping coefficient which marks a dynamical transition in the behavior of the system.

Four-electron model for singlet and triplet excitation energy transfers with inclusion of coherence memory, inelastic tunneling and nuclear quantum effects

NASA Astrophysics Data System (ADS)

Suzuki, Yosuke; Ebina, Kuniyoshi; Tanaka, Shigenori

2016-08-01

A computational scheme to describe the coherent dynamics of excitation energy transfer (EET) in molecular systems is proposed on the basis of generalized master equations with memory kernels. This formalism takes into account those physical effects in electron-bath coupling system such as the spin symmetry of excitons, the inelastic electron tunneling and the quantum features of nuclear motions, thus providing a theoretical framework to perform an ab initio description of EET through molecular simulations for evaluating the spectral density and the temporal correlation function of electronic coupling. Some test calculations have then been carried out to investigate the dependence of exciton population dynamics on coherence memory, inelastic tunneling correlation time, magnitude of electronic coupling, quantum correction to temporal correlation function, reorganization energy and energy gap.

A dose assessment method for arbitrary geometries with virtual reality in the nuclear facilities decommissioning

NASA Astrophysics Data System (ADS)

Chao, Nan; Liu, Yong-kuo; Xia, Hong; Ayodeji, Abiodun; Bai, Lu

2018-03-01

During the decommissioning of nuclear facilities, a large number of cutting and demolition activities are performed, which results in a frequent change in the structure and produce many irregular objects. In order to assess dose rates during the cutting and demolition process, a flexible dose assessment method for arbitrary geometries and radiation sources was proposed based on virtual reality technology and Point-Kernel method. The initial geometry is designed with the three-dimensional computer-aided design tools. An approximate model is built automatically in the process of geometric modeling via three procedures namely: space division, rough modeling of the body and fine modeling of the surface, all in combination with collision detection of virtual reality technology. Then point kernels are generated by sampling within the approximate model, and when the material and radiometric attributes are inputted, dose rates can be calculated with the Point-Kernel method. To account for radiation scattering effects, buildup factors are calculated with the Geometric-Progression formula in the fitting function. The effectiveness and accuracy of the proposed method was verified by means of simulations using different geometries and the dose rate results were compared with that derived from CIDEC code, MCNP code and experimental measurements.

Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.

PubMed

Bobb, Jennifer F; Valeri, Linda; Claus Henn, Birgit; Christiani, David C; Wright, Robert O; Mazumdar, Maitreyi; Godleski, John J; Coull, Brent A

2015-07-01

Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Encoding Dissimilarity Data for Statistical Model Building.

PubMed

Wahba, Grace

2010-12-01

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.

Non-volatile main memory management methods based on a file system.

PubMed

Oikawa, Shuichi

2014-01-01

There are upcoming non-volatile (NV) memory technologies that provide byte addressability and high performance. PCM, MRAM, and STT-RAM are such examples. Such NV memory can be used as storage because of its data persistency without power supply while it can be used as main memory because of its high performance that matches up with DRAM. There are a number of researches that investigated its uses for main memory and storage. They were, however, conducted independently. This paper presents the methods that enables the integration of the main memory and file system management for NV memory. Such integration makes NV memory simultaneously utilized as both main memory and storage. The presented methods use a file system as their basis for the NV memory management. We implemented the proposed methods in the Linux kernel, and performed the evaluation on the QEMU system emulator. The evaluation results show that 1) the proposed methods can perform comparably to the existing DRAM memory allocator and significantly better than the page swapping, 2) their performance is affected by the internal data structures of a file system, and 3) the data structures appropriate for traditional hard disk drives do not always work effectively for byte addressable NV memory. We also performed the evaluation of the effects caused by the longer access latency of NV memory by cycle-accurate full-system simulation. The results show that the effect on page allocation cost is limited if the increase of latency is moderate.

Optimization method of superpixel analysis for multi-contrast Jones matrix tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa K.; Miura, Masahiro; Yasuno, Yoshiaki

2017-02-01

Local statistics are widely utilized for quantification and image processing of OCT. For example, local mean is used to reduce speckle, local variation of polarization state (degree-of-polarization-uniformity (DOPU)) is used to visualize melanin. Conventionally, these statistics are calculated in a rectangle kernel whose size is uniform over the image. However, the fixed size and shape of the kernel result in a tradeoff between image sharpness and statistical accuracy. Superpixel is a cluster of pixels which is generated by grouping image pixels based on the spatial proximity and similarity of signal values. Superpixels have variant size and flexible shapes which preserve the tissue structure. Here we demonstrate a new superpixel method which is tailored for multifunctional Jones matrix OCT (JM-OCT). This new method forms the superpixels by clustering image pixels in a 6-dimensional (6-D) feature space (spatial two dimensions and four dimensions of optical features). All image pixels were clustered based on their spatial proximity and optical feature similarity. The optical features are scattering, OCT-A, birefringence and DOPU. The method is applied to retinal OCT. Generated superpixels preserve the tissue structures such as retinal layers, sclera, vessels, and retinal pigment epithelium. Hence, superpixel can be utilized as a local statistics kernel which would be more suitable than a uniform rectangle kernel. Superpixelized image also can be used for further image processing and analysis. Since it reduces the number of pixels to be analyzed, it reduce the computational cost of such image processing.

Exact calculation of the time convolutionless master equation generator: Application to the nonequilibrium resonant level model

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

Kidon, Lyran; The Sackler Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 69978; Wilner, Eli Y.

2015-12-21

The generalized quantum master equation provides a powerful tool to describe the dynamics in quantum impurity models driven away from equilibrium. Two complementary approaches, one based on Nakajima–Zwanzig–Mori time-convolution (TC) and the other on the Tokuyama–Mori time-convolutionless (TCL) formulations provide a starting point to describe the time-evolution of the reduced density matrix. A key in both approaches is to obtain the so called “memory kernel” or “generator,” going beyond second or fourth order perturbation techniques. While numerically converged techniques are available for the TC memory kernel, the canonical approach to obtain the TCL generator is based on inverting a super-operatormore » in the full Hilbert space, which is difficult to perform and thus, nearly all applications of the TCL approach rely on a perturbative scheme of some sort. Here, the TCL generator is expressed using a reduced system propagator which can be obtained from system observables alone and requires the calculation of super-operators and their inverse in the reduced Hilbert space rather than the full one. This makes the formulation amenable to quantum impurity solvers or to diagrammatic techniques, such as the nonequilibrium Green’s function. We implement the TCL approach for the resonant level model driven away from equilibrium and compare the time scales for the decay of the generator with that of the memory kernel in the TC approach. Furthermore, the effects of temperature, source-drain bias, and gate potential on the TCL/TC generators are discussed.« less

The impact of intelligence on memory and executive functions of children with temporal lobe epilepsy: Methodological concerns with clinical relevance.

PubMed

Rzezak, Patricia; Guimarães, Catarina A; Guerreiro, Marilisa M; Valente, Kette D

2017-05-01

Patients with TLE are prone to have lower IQ scores than healthy controls. Nevertheless, the impact of IQ differences is not usually considered in studies that compared the cognitive functioning of children with and without epilepsy. This study aimed to determine the effect of using IQ as a covariate on memory and attentional/executive functions of children with TLE. Thirty-eight children and adolescents with TLE and 28 healthy controls paired as to age, gender, and sociodemographic factors were evaluated with a comprehensive neuropsychological battery for memory and executive functions. The authors conducted three analyses to verify the impact of IQ scores on the other cognitive domains. First, we compared performance on cognitive tests without controlling for IQ differences between groups. Second, we performed the same analyses, but we included IQ as a confounding factor. Finally, we evaluated the predictive value of IQ on cognitive functioning. Although patients had IQ score in the normal range, they showed lower IQ scores than controls (p = 0.001). When we did not consider IQ in the analyses, patients had worse performance in verbal and visual memory (short and long-term), semantic memory, sustained, divided and selective attention, mental flexibility and mental tracking for semantic information. By using IQ as a covariate, patients showed worse performance only in verbal memory (long-term), semantic memory, sustained and divided attention and in mental flexibility. IQ was a predictor factor of verbal and visual memory (immediate and delayed), working memory, mental flexibility and mental tracking for semantic information. Intelligence level had a significant impact on memory and executive functioning of children and adolescents with TLE without intellectual disability. This finding opens the discussion of whether IQ scores should be considered when interpreting the results of differences in cognitive performance of patients with epilepsy compared to healthy volunteers. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Numerically Exact Long Time Magnetization Dynamics Near the Nonequilibrium Kondo Regime

NASA Astrophysics Data System (ADS)

Cohen, Guy; Gull, Emanuel; Reichman, David; Millis, Andrew; Rabani, Eran

2013-03-01

The dynamical and steady-state spin response of the nonequilibrium Anderson impurity model to magnetic fields, bias voltages, and temperature is investigated by a numerically exact method which allows access to unprecedentedly long times. The method is based on using real, continuous time bold Monte Carlo techniques--quantum Monte Carlo sampling of diagrammatic corrections to a partial re-summation--in order to compute the kernel of a memory function, which is then used to determine the reduced density matrix. The method owes its effectiveness to the fact that the memory kernel is dominated by relatively short-time properties even when the system's dynamics are long-ranged. We make predictions regarding the non-monotonic temperature dependence of the system at high bias voltage and the oscillatory quench dynamics at high magnetic fields. We also discuss extensions of the method to the computation of transport properties and correlation functions, and its suitability as an impurity solver free from the need for analytical continuation in the context of dynamical mean field theory. This work is supported by the US Department of Energy under grant DE-SC0006613, by NSF-DMR-1006282 and by the US-Israel Binational Science Foundation. GC is grateful to the Yad Hanadiv-Rothschild Foundation for the award of a Rothschild Fellowship.

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

DOE PAGES

Carter Edwards, H.; Trott, Christian R.; Sunderland, Daniel

2014-07-22

The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. We found that a major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diversemore » manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. Furthermore, the Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.« less

Xyce Parallel Electronic Simulator : users' guide, version 2.0.

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

Hoekstra, Robert John; Waters, Lon J.; Rankin, Eric Lamont

2004-06-01

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator capable of simulating electrical circuits at a variety of abstraction levels. Primarily, Xyce has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability the current state-of-the-art in the following areas: {sm_bullet} Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers. {sm_bullet} Improved performance for allmore » numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. {sm_bullet} Device models which are specifically tailored to meet Sandia's needs, including many radiation-aware devices. {sm_bullet} A client-server or multi-tiered operating model wherein the numerical kernel can operate independently of the graphical user interface (GUI). {sm_bullet} Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing of computing platforms. These include serial, shared-memory and distributed-memory parallel implementation - which allows it to run efficiently on the widest possible number parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. One feature required by designers is the ability to add device models, many specific to the needs of Sandia, to the code. To this end, the device package in the Xyce These input formats include standard analytical models, behavioral models look-up Parallel Electronic Simulator is designed to support a variety of device model inputs. tables, and mesh-level PDE device models. Combined with this flexible interface is an architectural design that greatly simplifies the addition of circuit models. One of the most important feature of Xyce is in providing a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia now has an 'in-house' capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods) research and development can be performed. Ultimately, these capabilities are migrated to end users.« less

Fast Initialization of Bubble-Memory Systems

NASA Technical Reports Server (NTRS)

Looney, K. T.; Nichols, C. D.; Hayes, P. J.

1986-01-01

Improved scheme several orders of magnitude faster than normal initialization scheme. State-of-the-art commercial bubble-memory device used. Hardware interface designed connects controlling microprocessor to bubblememory circuitry. System software written to exercise various functions of bubble-memory system in comparison made between normal and fast techniques. Future implementations of approach utilize E2PROM (electrically-erasable programable read-only memory) to provide greater system flexibility. Fastinitialization technique applicable to all bubble-memory devices.

Flexible Retrieval: When True Inferences Produce False Memories

PubMed Central

Carpenter, Alexis C.; Schacter, Daniel L.

2016-01-01

Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave memory prone to error or distortion, such as source misattributions in which details of one event are mistakenly attributed to another related event. To determine whether the same recombination-related retrieval mechanism supports both successful inference and source memory errors, we developed a modified version of an associative inference paradigm in which participants encoded everyday scenes comprised of people, objects, and other contextual details. These scenes contained overlapping elements (AB, BC) that could later be linked to support novel inferential retrieval regarding elements that had not appeared together previously (AC). Our critical experimental manipulation concerned whether contextual details were probed before or after the associative inference test, thereby allowing us to assess whether a) false memories increased for successful versus unsuccessful inferences, and b) any such effects were specific to after as compared to before participants received the inference test. In each of four experiments that used variants of this paradigm, participants were more susceptible to false memories for contextual details after successful than unsuccessful inferential retrieval, but only when contextual details were probed after the associative inference test. These results suggest that the retrieval-mediated recombination mechanism that underlies associative inference also contributes to source misattributions that result from combining elements of distinct episodes. PMID:27918169

Weighted graph cuts without eigenvectors a multilevel approach.

PubMed

Dhillon, Inderjit S; Guan, Yuqiang; Kulis, Brian

2007-11-01

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.

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

Krueger, Jens; Micikevicius, Paulius; Williams, Samuel

Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi- and manycore processors and introduce a novel common subexpression elimination optimization formore » TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry’s demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.« less

Achieving High Performance in Parallel Applications via Kernel-Application Interaction

DTIC Science & Technology

1996-04-01

time systems include airplane autopilot or nuclear power plant control. New complex, parallel soft real-time applica- tions have been generating...to keep as many sheep on the table as possible, and the more powerful the sheep behavior-models and look-ahead, the better the results. General...fact that it provides considerable flexibility when considering the amount of processing power to allocate to a planner. In this experiment we again

Elliptic polylogarithms and iterated integrals on elliptic curves. II. An application to the sunrise integral

NASA Astrophysics Data System (ADS)

Broedel, Johannes; Duhr, Claude; Dulat, Falko; Tancredi, Lorenzo

2018-06-01

We introduce a class of iterated integrals that generalize multiple polylogarithms to elliptic curves. These elliptic multiple polylogarithms are closely related to similar functions defined in pure mathematics and string theory. We then focus on the equal-mass and non-equal-mass sunrise integrals, and we develop a formalism that enables us to compute these Feynman integrals in terms of our iterated integrals on elliptic curves. The key idea is to use integration-by-parts identities to identify a set of integral kernels, whose precise form is determined by the branch points of the integral in question. These kernels allow us to express all iterated integrals on an elliptic curve in terms of them. The flexibility of our approach leads us to expect that it will be applicable to a large variety of integrals in high-energy physics.

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Drawing a dog: The role of working memory and executive function.

PubMed

Panesi, Sabrina; Morra, Sergio

2016-12-01

Previous research suggests that young children draw animals by adapting their scheme for the human figure. This can be considered an early form of drawing flexibility. This study investigated preschoolers' ability to draw a dog that is different from the human figure. The role of working memory capacity and executive function was examined. The participants were 123 children (36-73 months old) who were required to draw both a person and a dog. The dog figure was scored on a list of features that could render it different from the human figure. Regression analyses showed that both working memory capacity and executive function predicted development in the dog drawing; the dog drawing score correlated with working memory capacity and executive function, even partialling out age, motor coordination, and drawing ability (measured with Goodenough's Draw-a-Man test). These results suggest that both working memory capacity and executive function play an important role in the early development of drawing flexibility. The implications regarding executive functions and working memory are also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of a Shape Memory Alloy Reinforced Annuloplasty Band for Minimally Invasive Mitral Valve Repair

PubMed Central

Purser, Molly F.; Richards, Andrew L.; Cook, Richard C.; Osborne, Jason A.; Cormier, Denis R.; Buckner, Gregory D.

2013-01-01

Purpose An in vitro study using explanted porcine hearts was conducted to evaluate a novel annuloplasty band, reinforced with a two-phase, shape memory alloy, designed specifically for minimally invasive mitral valve repair. Description In its rigid (austenitic) phase, this band provides the same mechanical properties as the commercial semi-rigid bands. In its compliant (martensitic) phase, this band is flexible enough to be introduced through an 8-mm trocar and is easily manipulated within the heart. Evaluation In its rigid phase, the prototype band displayed similar mechanical properties to commercially available semi-rigid rings. Dynamic flow testing demonstrated no statistical differences in the reduction of mitral valve regurgitation. In its flexible phase, the band was easily deployed through an 8-mm trocar, robotically manipulated and sutured into place. Conclusions Experimental results suggest that the shape memory alloy reinforced band could be a viable alternative to flexible and semi-rigid bands in minimally invasive mitral valve repair. PMID:19766827

ERP evidence for flexible adjustment of retrieval orientation and its influence on familiarity.

PubMed

Ecker, Ullrich K H; Zimmer, Hubert D

2009-10-01

The assumption was tested that familiarity memory as indexed by a mid-frontal ERP old-new effect is modulated by retrieval orientation. A randomly cued category-based versus exemplar-specific recognition memory test, requiring flexible adjustment of retrieval orientation, was conducted. Results show that the mid-frontal ERP old-new effect is sensitive to the manipulation of study-test congruency-that is, whether the same object is repeated identically or a different category exemplar is presented at test. Importantly, the effect pattern depends on subjects' retrieval orientation. With a specific orientation, only same items elicited an early old-new effect (same > different = new), whereas in the general condition, the old-new effect was graded (same > different > new). This supports the view that both perceptual and conceptual processes can contribute to familiarity memory and demonstrates that the rather automatic process of familiarity is not only data driven but influenced by top-down retrieval orientation, which subjects are able to adjust on a flexible basis.

Effects of high-dose ethanol intoxication and hangover on cognitive flexibility.

PubMed

Wolff, Nicole; Gussek, Philipp; Stock, Ann-Kathrin; Beste, Christian

2018-01-01

The effects of high-dose ethanol intoxication on cognitive flexibility processes are not well understood, and processes related to hangover after intoxication have remained even more elusive. Similarly, it is unknown in how far the complexity of cognitive flexibility processes is affected by intoxication and hangover effects. We performed a neurophysiological study applying high density electroencephalography (EEG) recording to analyze event-related potentials (ERPs) and perform source localization in a task switching paradigm which varied the complexity of task switching by means of memory demands. The results show that high-dose ethanol intoxication only affects task switching (i.e. cognitive flexibility processes) when memory processes are required to control task switching mechanisms, suggesting that even high doses of ethanol compromise cognitive processes when they are highly demanding. The EEG and source localization data show that these effects unfold by modulating response selection processes in the anterior cingulate cortex. Perceptual and attentional selection processes as well as working memory processes were only unspecifically modulated. In all subprocesses examined, there were no differences between the sober and hangover states, thus suggesting a fast recovery of cognitive flexibility after high-dose ethanol intoxication. We assume that the gamma-aminobutyric acid (GABAergic) system accounts for the observed effects, while they can hardly be explained by the dopaminergic system. © 2016 Society for the Study of Addiction.

Subjective Memory Impairment and Well-Being in Community-Dwelling Older Adults

PubMed Central

Zuniga, Krystle E.; Mackenzie, Michael; Kramer, Arthur; McAuley, Edward

2015-01-01

Background The relationship between subjective memory impairment (SMI), future cognitive decline and negative health status provides an opportunity for interventions to reduce memory complaints in high risk groups. This study aimed to examine the relationship between subjective memory impairment (SMI) and indicators of well-being in older adults enrolled in an exercise trial. Additionally, the study examined whether two different modes of exercise training, aerobic walking or non-aerobic flexibility, toning, and balance, differentially influenced subjective memory across the trial. Methods Community-dwelling older adults (n=179, Mage=66.4) were randomly assigned to a walking or flexibility, toning, and balance group for 12 months. Subjective memory, happiness, perceived stress, and symptom reporting were measured at baseline, 6 months and 12 months. Results A main effect of subjective memory indicated that individuals with the fewest memory complaints had lower perceived stress (P<0.001) and physical symptom reporting (P<0.001), and higher happiness (P<0.001) across all measurement occasions. Both main and interaction effects of time and group on SMI were not significant, suggesting SMI remained stable across the intervention and was not significantly impacted by participation in exercise training. Conclusions SMI was not responsive to exercise interventions, and the relationship between subjective memory impairment (SMI) and negative well- being demonstrates a need for interventions to reduce memory complaints in high risk groups. PMID:25737426

Complex Source and Radiation Behaviors of Small Elements of Linear and Matrix Flexible Ultrasonic Phased-Array Transducers

NASA Astrophysics Data System (ADS)

Amory, V.; Lhémery, A.

2008-02-01

Inspection of irregular components is problematical: maladjustment of transducer shoes to surfaces causes aberrations. Flexible phased-arrays (FPAs) designed at CEA LIST to maximize contact are driven by adapted delay laws to compensate for irregularities. Optimizing FPA requires simulation tools. The behavior of one element computed by FEM is observed at the surface and its radiation experimentally validated. Efforts for one element prevent from simulating a FPA by FEM. A model is proposed where each element behaves as nonuniform source of stresses. Exact and asymptotic formulas for Lamb problem are used as convolution kernels for longitudinal, transverse and head waves; the latter is of primary importance for angle-T-beam inspections.

Move to learn: Integrating spatial information from multiple viewpoints.

PubMed

Holmes, Corinne A; Newcombe, Nora S; Shipley, Thomas F

2018-05-11

Recalling a spatial layout from multiple orientations - spatial flexibility - is challenging, even when the global configuration can be viewed from a single vantage point, but more so when it must be viewed piecemeal. In the current study, we examined whether experiencing the transition between multiple viewpoints enhances spatial memory and flexible recall for a spatial configuration viewed simultaneously (Exp. 1) and sequentially (Exp. 2), whether the type of transition matters, and whether action provides an additional advantage over passive experience. In Experiment 1, participants viewed an array of dollhouse furniture from four viewpoints, but with all furniture simultaneously visible. In Experiment 2, participants viewed the same array piecemeal, from four partitioned viewpoints that allowed for viewing only a segment at a time. The transition between viewpoints involved rotation of the array or participant movement around it. Rotation and participant movement were passively experienced or actively generated. The control condition presented the dollhouse as a series of static views. Across both experiments, participant movement significantly enhanced spatial memory relative to array rotation or static views. However, in Exp. 2, there was a further advantage for actively walking around the array compared to being passively pushed. These findings suggest that movement around a stable environment is key to spatial memory and flexible recall, with action providing an additional boost to the integration of temporally segmented spatial events. Thus, spatial memory may be more flexible than prior data indicate, when studied under more natural acquisition conditions. Copyright © 2018 Elsevier B.V. All rights reserved.

Design of a Variational Multiscale Method for Turbulent Compressible Flows

NASA Technical Reports Server (NTRS)

Diosady, Laslo Tibor; Murman, Scott M.

2013-01-01

A spectral-element framework is presented for the simulation of subsonic compressible high-Reynolds-number flows. The focus of the work is maximizing the efficiency of the computational schemes to enable unsteady simulations with a large number of spatial and temporal degrees of freedom. A collocation scheme is combined with optimized computational kernels to provide a residual evaluation with computational cost independent of order of accuracy up to 16th order. The optimized residual routines are used to develop a low-memory implicit scheme based on a matrix-free Newton-Krylov method. A preconditioner based on the finite-difference diagonalized ADI scheme is developed which maintains the low memory of the matrix-free implicit solver, while providing improved convergence properties. Emphasis on low memory usage throughout the solver development is leveraged to implement a coupled space-time DG solver which may offer further efficiency gains through adaptivity in both space and time.

Stochastic Multiresonance for a Fractional Linear Oscillator with Quadratic Trichotomous Noise

NASA Astrophysics Data System (ADS)

Zhu, Jian-Qu; Jin, Wei-Dong; Zheng, Gao; Guo, Feng

2017-11-01

The stochastic multiresonance behavior for a fractional linear oscillator with random system frequency is investigated. The fluctuation of the system frequency is a quadratic trichotomous noise, the memory kernel of the fractional oscillator is modeled as a Mittag-Leffler function. Based on linear system theory, applying Laplace transform and the definition of fractional derivative, the expression of the system output amplitude (SPA) is obtained. Stochastic multiresonance phenomenon is found on the curves of SPA versus the memory time and the memory exponent of the fractional oscillator, as well as versus the trichotomous noise amplitude. The SPA depends non-monotonically on the stationary probability of the trichotomous noise, on the viscous damping coefficient and system characteristic frequency of the oscillator, as well as on the driving frequency of external force. Supported by National Natural Science Foundation of China under Grant No. 61134002

Resonant behavior of the generalized Langevin system with tempered Mittag–Leffler memory kernel

NASA Astrophysics Data System (ADS)

Chen, Yao; Wang, Xudong; Deng, Weihua

2018-05-01

The generalized Langevin equation describes anomalous dynamics. Noise is not only the origin of uncertainty but also plays a positive role in helping to detect signals with information, termed stochastic resonance (SR). This paper analyzes the anomalous resonant behaviors of the generalized Langevin system with a multiplicative dichotomous noise and an internal tempered Mittag–Leffler noise. For a system with a fluctuating harmonic potential, we obtain the exact expressions of several types of SR such as the first moment, the amplitude and autocorrelation function for the output signal as well as the signal–noise ratio. We analyze the influence of the tempering parameter and memory exponent on the bona fide SR and the general SR. Moreover, it is detected that the critical memory exponent changes regularly with the increase of the tempering parameter. Almost all the theoretical results are validated by numerical simulations.

Implementing an ADA Kernel on NEBULA.

DTIC Science & Technology

1983-08-01

physical address(es). No instruction supports directly semaphore operations , or spin-locks, or other entities used in the synchronisation of tasks...these operations It is found that NEBULA supports admirably the control structures oil Ada, but its Memory Mamagement system is not very suitable. Entry... operating system . With the advent of Ada, in theory at least, the whole program can be written in Ada in a manner that is independent of the computer and of

Validation Report for the EO-1 Lightweight Flexible Solar Array Experiment

NASA Technical Reports Server (NTRS)

Carpenter, Bernie; Lyons, John; Day, John (Technical Monitor)

2001-01-01

The controlled deployment of the Lightweight Flexible Solar Array (LFSA) experiment using the shape memory alloy release and deployment system has been demonstrated. Work remains to be done in increasing the efficiency of Copper Indium Diselinide (CIS) terminations to the flexible harness that carries current from the array to the I-V measurement electronics.

Flexibility in Visual Working Memory: Accurate Change Detection in the Face of Irrelevant Variations in Position

PubMed Central

Woodman, Geoffrey F.; Vogel, Edward K.; Luck, Steven J.

2012-01-01

Many recent studies of visual working memory have used change-detection tasks in which subjects view sequential displays and are asked to report whether they are identical or if one object has changed. A key question is whether the memory system used to perform this task is sufficiently flexible to detect changes in object identity independent of spatial transformations, but previous research has yielded contradictory results. To address this issue, the present study compared standard change-detection tasks with tasks in which the objects varied in size or position between successive arrays. Performance was nearly identical across the standard and transformed tasks unless the task implicitly encouraged spatial encoding. These results resolve the discrepancies in prior studies and demonstrate that the visual working memory system can detect changes in object identity across spatial transformations. PMID:22287933

Synergistic High Charge-Storage Capacity for Multi-level Flexible Organic Flash Memory

NASA Astrophysics Data System (ADS)

Kang, Minji; Khim, Dongyoon; Park, Won-Tae; Kim, Jihong; Kim, Juhwan; Noh, Yong-Young; Baeg, Kang-Jun; Kim, Dong-Yu

2015-07-01

Electret and organic floating-gate memories are next-generation flash storage mediums for printed organic complementary circuits. While each flash memory can be easily fabricated using solution processes on flexible plastic substrates, promising their potential for on-chip memory organization is limited by unreliable bit operation and high write loads. We here report that new architecture could improve the overall performance of organic memory, and especially meet high storage for multi-level operation. Our concept depends on synergistic effect of electrical characterization in combination with a polymer electret (poly(2-vinyl naphthalene) (PVN)) and metal nanoparticles (Copper). It is distinguished from mostly organic nano-floating-gate memories by using the electret dielectric instead of general tunneling dielectric for additional charge storage. The uniform stacking of organic layers including various dielectrics and poly(3-hexylthiophene) (P3HT) as an organic semiconductor, followed by thin-film coating using orthogonal solvents, greatly improve device precision despite easy and fast manufacture. Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] as high-k blocking dielectric also allows reduction of programming voltage. The reported synergistic organic memory devices represent low power consumption, high cycle endurance, high thermal stability and suitable retention time, compared to electret and organic nano-floating-gate memory devices.

Synergistic High Charge-Storage Capacity for Multi-level Flexible Organic Flash Memory.

PubMed

Kang, Minji; Khim, Dongyoon; Park, Won-Tae; Kim, Jihong; Kim, Juhwan; Noh, Yong-Young; Baeg, Kang-Jun; Kim, Dong-Yu

2015-07-23

Electret and organic floating-gate memories are next-generation flash storage mediums for printed organic complementary circuits. While each flash memory can be easily fabricated using solution processes on flexible plastic substrates, promising their potential for on-chip memory organization is limited by unreliable bit operation and high write loads. We here report that new architecture could improve the overall performance of organic memory, and especially meet high storage for multi-level operation. Our concept depends on synergistic effect of electrical characterization in combination with a polymer electret (poly(2-vinyl naphthalene) (PVN)) and metal nanoparticles (Copper). It is distinguished from mostly organic nano-floating-gate memories by using the electret dielectric instead of general tunneling dielectric for additional charge storage. The uniform stacking of organic layers including various dielectrics and poly(3-hexylthiophene) (P3HT) as an organic semiconductor, followed by thin-film coating using orthogonal solvents, greatly improve device precision despite easy and fast manufacture. Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] as high-k blocking dielectric also allows reduction of programming voltage. The reported synergistic organic memory devices represent low power consumption, high cycle endurance, high thermal stability and suitable retention time, compared to electret and organic nano-floating-gate memory devices.

Solid state engine using nitinol memory alloy

DOEpatents

Golestaneh, Ahmad A.

1981-01-01

A device for converting heat energy to mechanical energy includes a reservoir of a hot fluid and a rotor assembly mounted thereabove so a portion of it dips into the hot fluid. The rotor assembly may include a shaft having four spokes extending radially outwardly therefrom at right angles to each other, a floating ring and four flexible elements composed of a thermal memory material having a critical temperature between the temperature of the hot fluid and that of the ambient atmosphere extending between the ends of the spokes and the floating ring. Preferably, the flexible elements are attached to the floating ring through curved leaf springs. Energetic shape recovery of the flexible elements in the hot fluid causes the rotor assembly to rotate.

Solid state engine using nitinol memory alloy

DOEpatents

Golestaneh, A.A.

1980-01-21

A device for converting heat energy to mechanical energy includes a reservoir of a hot fluid and a rotor assembly mounted thereabove so a portion of it dips into the hot fluid. The rotor assembly may include a shaft having four spokes extending radially outwardly therefrom at right angles to each other, a floating ring and four flexible elements composed of a thermal memory material having a critical temperature between the temperature of the hot fluid and that of the ambient atmosphere extending between the ends of the spokes and the floating ring. Preferably, the flexible elements are attached to the floating ring through curved leaf springs. Energetic shape recovery of the flexible elements in the hot fluid causes the rotor assembly to rotate.

Efficient similarity-based data clustering by optimal object to cluster reallocation.

PubMed

Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

2018-01-01

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

Episodic and working memory deficits in alcoholic Korsakoff patients: the continuity theory revisited.

PubMed

Pitel, Anne Lise; Beaunieux, Hélène; Witkowski, Thomas; Vabret, François; de la Sayette, Vincent; Viader, Fausto; Desgranges, Béatrice; Eustache, Francis

2008-07-01

The exact nature of episodic and working memory impairments in alcoholic Korsakoff patients (KS) remains unclear, as does the specificity of these neuropsychological deficits compared with those of non-Korsakoff alcoholics (AL). The goals of the present study were therefore to (1) specify the nature of episodic and working memory impairments in KS, (2) determine the specificity of the KS neuropsychological profile compared with the AL profile, and (3) observe the distribution of individual performances within the 2 patient groups. We investigated episodic memory (encoding and retrieval abilities, contextual memory and state of consciousness associated with memories), the slave systems of working memory (phonological loop, visuospatial sketchpad and episodic buffer) and executive functions (inhibition, flexibility, updating and integration abilities) in 14 strictly selected KS, 40 AL and 55 control subjects (CS). Compared with CS, KS displayed impairments of episodic memory encoding and retrieval, contextual memory, recollection, the slave systems of working memory and executive functions. Although episodic memory was more severely impaired in KS than in AL, the single specificity of the KS profile was a disproportionately large encoding deficit. Apart from organizational and updating abilities, the slave systems of working memory and inhibition, flexibility and integration abilities were impaired to the same extent in both alcoholic groups. However, some KS were unable to complete the most difficult executive tasks. There was only a partial overlap of individual performances by KS and AL for episodic memory and a total mixture of the 2 groups for working memory. Korsakoff's syndrome encompasses impairments of the different episodic and working memory components. AL and KS displayed similar profiles of episodic and working memory deficits, in accordance with neuroimaging investigations showing similar patterns of brain damage in both alcoholic groups.

Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

PubMed Central

Meinicke, Peter; Tech, Maike; Morgenstern, Burkhard; Merkl, Rainer

2004-01-01

Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. PMID:15511290

The Genetic Components of Verbal Divergent Thinking and Short Term Memory.

ERIC Educational Resources Information Center

Pezzullo, Thomas R.; Madaus, George F.

A study of twins was conducted to determine the presence of an hereditary component in short term memory and in three aspects of verbal divergent thinking--flexibility, fluency, and originality. Results showed the existence of a significant genetic component in the trait of short term memory, while none was found in verbal divergent thinking. (AG)

On the Flexibility of Social Source Memory: A Test of the Emotional Incongruity Hypothesis

ERIC Educational Resources Information Center

Bell, Raoul; Buchner, Axel; Kroneisen, Meike; Giang, Trang

2012-01-01

A popular hypothesis in evolutionary psychology posits that reciprocal altruism is supported by a cognitive module that helps cooperative individuals to detect and remember cheaters. Consistent with this hypothesis, a source memory advantage for faces of cheaters (better memory for the cheating context in which these faces were encountered) was…

Thermocouple for heating and cooling of memory metal actuators

NASA Technical Reports Server (NTRS)

Wood, Charles (Inventor)

1988-01-01

A semiconductor thermocouple unit is provided for heating and cooling memory metal actuators. The semiconductor thermocouple unit is mounted adjacent to a memory metal actuator and has a heat sink attached to it. A flexible thermally conductive element extends between the semiconductor thermocouple and the actuator and serves as a heat transfer medium during heating and cooling operations.

Changing concepts of working memory

PubMed Central

Ma, Wei Ji; Husain, Masud; Bays, Paul M

2014-01-01

Working memory is widely considered to be limited in capacity, holding a fixed, small number of items, such as Miller's ‘magical number’ seven or Cowan's four. It has recently been proposed that working memory might better be conceptualized as a limited resource that is distributed flexibly among all items to be maintained in memory. According to this view, the quality rather than the quantity of working memory representations determines performance. Here we consider behavioral and emerging neural evidence for this proposal. PMID:24569831

Flexible retrieval: When true inferences produce false memories.

PubMed

Carpenter, Alexis C; Schacter, Daniel L

2017-03-01

Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave memory prone to error or distortion, such as source misattributions in which details of one event are mistakenly attributed to another related event. To determine whether the same recombination-related retrieval mechanism supports both successful inference and source memory errors, we developed a modified version of an associative inference paradigm in which participants encoded everyday scenes comprised of people, objects, and other contextual details. These scenes contained overlapping elements (AB, BC) that could later be linked to support novel inferential retrieval regarding elements that had not appeared together previously (AC). Our critical experimental manipulation concerned whether contextual details were probed before or after the associative inference test, thereby allowing us to assess whether (a) false memories increased for successful versus unsuccessful inferences, and (b) any such effects were specific to after compared with before participants received the inference test. In each of 4 experiments that used variants of this paradigm, participants were more susceptible to false memories for contextual details after successful than unsuccessful inferential retrieval, but only when contextual details were probed after the associative inference test. These results suggest that the retrieval-mediated recombination mechanism that underlies associative inference also contributes to source misattributions that result from combining elements of distinct episodes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

PubMed

Cao, Peng; Liu, Xiaoli; Yang, Jinzhu; Zhao, Dazhe; Huang, Min; Zhang, Jian; Zaiane, Osmar

2017-12-01

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response). To better capture the complicated but more flexible relationship, we propose a multi-kernel based dimensionality reduction and over-sampling approaches. We combined Marginal Fisher Analysis with ℓ 2,1 -norm based multi-kernel learning (MKMFA) to achieve the sparsity of region-of-interest (ROI), which leads to simultaneously selecting a subset of the relevant brain regions and learning a dimensionality transformation. Meanwhile, a multi-kernel over-sampling (MKOS) was developed to generate synthetic instances in the optimal kernel space induced by MKMFA, so as to compensate for the class imbalanced distribution. We comprehensively evaluate the proposed models for the diagnostic classification (binary class and multi-class classification) including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Nonparametric Fine Tuning of Mixtures: Application to Non-Life Insurance Claims Distribution Estimation

NASA Astrophysics Data System (ADS)

Sardet, Laure; Patilea, Valentin

When pricing a specific insurance premium, actuary needs to evaluate the claims cost distribution for the warranty. Traditional actuarial methods use parametric specifications to model claims distribution, like lognormal, Weibull and Pareto laws. Mixtures of such distributions allow to improve the flexibility of the parametric approach and seem to be quite well-adapted to capture the skewness, the long tails as well as the unobserved heterogeneity among the claims. In this paper, instead of looking for a finely tuned mixture with many components, we choose a parsimonious mixture modeling, typically a two or three-component mixture. Next, we use the mixture cumulative distribution function (CDF) to transform data into the unit interval where we apply a beta-kernel smoothing procedure. A bandwidth rule adapted to our methodology is proposed. Finally, the beta-kernel density estimate is back-transformed to recover an estimate of the original claims density. The beta-kernel smoothing provides an automatic fine-tuning of the parsimonious mixture and thus avoids inference in more complex mixture models with many parameters. We investigate the empirical performance of the new method in the estimation of the quantiles with simulated nonnegative data and the quantiles of the individual claims distribution in a non-life insurance application.

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

Haugen, Carl C.; Forget, Benoit; Smith, Kord S.

Most high performance computing systems being deployed currently and envisioned for the future are based on making use of heavy parallelism across many computational nodes and many concurrent cores. These types of heavily parallel systems often have relatively little memory per core but large amounts of computing capability. This places a significant constraint on how data storage is handled in many Monte Carlo codes. This is made even more significant in fully coupled multiphysics simulations, which requires simulations of many physical phenomena be carried out concurrently on individual processing nodes, which further reduces the amount of memory available for storagemore » of Monte Carlo data. As such, there has been a move towards on-the-fly nuclear data generation to reduce memory requirements associated with interpolation between pre-generated large nuclear data tables for a selection of system temperatures. Methods have been previously developed and implemented in MIT’s OpenMC Monte Carlo code for both the resolved resonance regime and the unresolved resonance regime, but are currently absent for the thermal energy regime. While there are many components involved in generating a thermal neutron scattering cross section on-the-fly, this work will focus on a proposed method for determining the energy and direction of a neutron after a thermal incoherent inelastic scattering event. This work proposes a rejection sampling based method using the thermal scattering kernel to determine the correct outgoing energy and angle. The goal of this project is to be able to treat the full S (a, ß) kernel for graphite, to assist in high fidelity simulations of the TREAT reactor at Idaho National Laboratory. The method is, however, sufficiently general to be applicable in other thermal scattering materials, and can be initially validated with the continuous analytic free gas model.« less

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

PubMed Central

Tian, Yin; Zhang, Huiling; Xu, Wei; Zhang, Haiyong; Yang, Li; Zheng, Shuxing; Shi, Yupan

2017-01-01

Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine classifier with linear kernel to differentiate these two groups. Receiver operating characteristics analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0 and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression prediction model with radial basis function kernel and the root-mean-square error of prediction revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject by short-time training, we found that 16 in 20 subjects’ SRT scores were clearly promoted after training and 15 in 20 subjects’ SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that spectral entropy could be a promising indicator to predict individual’s WM changes by training and further provide a novel application about WM for brain–computer interfaces. PMID:28912701

Decolonisation of fractional calculus rules: Breaking commutativity and associativity to capture more natural phenomena

NASA Astrophysics Data System (ADS)

Atangana, Abdon; Gómez-Aguilar, J. F.

2018-04-01

To answer some issues raised about the concept of fractional differentiation and integration based on the exponential and Mittag-Leffler laws, we present, in this paper, fundamental differences between the power law, exponential decay, Mittag-Leffler law and their possible applications in nature. We demonstrate the failure of the semi-group principle in modeling real-world problems. We use natural phenomena to illustrate the importance of non-commutative and non-associative operators under which the Caputo-Fabrizio and Atangana-Baleanu fractional operators fall. We present statistical properties of generator for each fractional derivative, including Riemann-Liouville, Caputo-Fabrizio and Atangana-Baleanu ones. The Atangana-Baleanu and Caputo-Fabrizio fractional derivatives show crossover properties for the mean-square displacement, while the Riemann-Liouville is scale invariant. Their probability distributions are also a Gaussian to non-Gaussian crossover, with the difference that the Caputo Fabrizio kernel has a steady state between the transition. Only the Atangana-Baleanu kernel is a crossover for the waiting time distribution from stretched exponential to power law. A new criterion was suggested, namely the Atangana-Gómez fractional bracket, that helps describe the energy needed by a fractional derivative to characterize a 2-pletic manifold. Based on these properties, we classified fractional derivatives in three categories: weak, mild and strong fractional differential and integral operators. We presented some applications of fractional differential operators to describe real-world problems and we proved, with numerical simulations, that the Riemann-Liouville power-law derivative provides a description of real-world problems with much additional information, that can be seen as noise or error due to specific memory properties of its power-law kernel. The Caputo-Fabrizio derivative is less noisy while the Atangana-Baleanu fractional derivative provides an excellent description, due to its Mittag-Leffler memory, able to distinguish between dynamical systems taking place at different scales without steady state. The study suggests that the properties of associativity and commutativity or the semi-group principle are just irrelevant in fractional calculus. Properties of classical derivatives were established for the ordinary calculus with no memory effect and it is a failure of mathematical investigation to attempt to describe more complex natural phenomena using the same notions.

Global exponential stability of bidirectional associative memory neural networks with distributed delays

NASA Astrophysics Data System (ADS)

Song, Qiankun; Cao, Jinde

2007-05-01

A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

Low-voltage operating flexible ferroelectric organic field-effect transistor nonvolatile memory with a vertical phase separation P(VDF-TrFE-CTFE)/PS dielectric

NASA Astrophysics Data System (ADS)

Xu, Meili; Xiang, Lanyi; Xu, Ting; Wang, Wei; Xie, Wenfa; Zhou, Dayu

2017-10-01

Future flexible electronic systems require memory devices combining low-power operation and mechanical bendability. However, high programming/erasing voltages, which are universally needed to switch the storage states in previously reported ferroelectric organic field-effect transistor (Fe-OFET) nonvolatile memories (NVMs), severely prevent their practical applications. In this work, we develop a route to achieve a low-voltage operating flexible Fe-OFET NVM. Utilizing vertical phase separation, an ultrathin self-organized poly(styrene) (PS) buffering layer covers the surface of the ferroelectric polymer layer by one-step spin-coating from their blending solution. The ferroelectric polymer with a low coercive field contributes to low-voltage operation in the Fe-OFET NVM. The polymer PS contributes to the improvement of mobility, attributing to screening the charge scattering and decreasing the surface roughness. As a result, a high performance flexible Fe-OFET NVM is achieved at the low P/E voltages of ±10 V, with a mobility larger than 0.2 cm2 V-1 s-1, a reliable P/E endurance over 150 cycles, stable data storage retention capability over 104 s, and excellent mechanical bending durability with a slight performance degradation after 1000 repetitive tensile bending cycles at a curvature radius of 5.5 mm.

High-performance, flexible, deployable array development for space applications

NASA Technical Reports Server (NTRS)

Gehling, Russell N.; Armstrong, Joseph H.; Misra, Mohan S.

1994-01-01

Flexible, deployable arrays are an attractive alternative to conventional solar arrays for near-term and future space power applications, particularly due to their potential for high specific power and low storage volume. Combined with low-cost flexible thin-film photovoltaics, these arrays have the potential to become an enabling or an enhancing technology for many missions. In order to expedite the acceptance of thin-film photovoltaics for space applications, however, parallel development of flexible photovoltaics and the corresponding deployable structure is essential. Many innovative technologies must be incorporated in these arrays to ensure a significant performance increase over conventional technologies. For example, innovative mechanisms which employ shape memory alloys for storage latches, deployment mechanisms, and array positioning gimbals can be incorporated into flexible array design with significant improvement in the areas of cost, weight, and reliability. This paper discusses recent activities at Martin Marietta regarding the development of flexible, deployable solar array technology. Particular emphasis is placed on the novel use of shape memory alloys for lightweight deployment elements to improve the overall specific power of the array. Array performance projections with flexible thin-film copper-indium-diselenide (CIS) are presented, and government-sponsored solar array programs recently initiated at Martin Marietta through NASA and Air Force Phillips Laboratory are discussed.

Remember Hard But Think Softly: Metaphorical Effects of Hardness/Softness on Cognitive Functions.

PubMed

Xie, Jiushu; Lu, Zhi; Wang, Ruiming; Cai, Zhenguang G

2016-01-01

Previous studies have found that bodily stimulation, such as hardness biases social judgment and evaluation via metaphorical association; however, it remains unclear whether bodily stimulation also affects cognitive functions, such as memory and creativity. The current study used metaphorical associations between "hard" and "rigid" and between "soft" and "flexible" in Chinese, to investigate whether the experience of hardness affects cognitive functions whose performance depends prospectively on rigidity (memory) and flexibility (creativity). In Experiment 1, we found that Chinese-speaking participants performed better at recalling previously memorized words while sitting on a hard-surface stool (the hard condition) than a cushioned one (the soft condition). In Experiment 2, participants sitting on a cushioned stool outperformed those sitting on a hard-surface stool on a Chinese riddle task, which required creative/flexible thinking, but not on an analogical reasoning task, which required both rigid and flexible thinking. The results suggest the hardness experience affects cognitive functions that are metaphorically associated with rigidity or flexibility. They support the embodiment proposition that cognitive functions and representations can be grounded in bodily states via metaphorical associations.

The effect of low versus high approach-motivated positive affect on the balance between maintenance and flexibility.

PubMed

Liu, Liting; Xu, Baihua

2016-05-27

Successful goal-directed behavior in a constantly changing environment requires a balance between maintenance and flexibility. Although some studies have found that positive affect influences this balance differently than neutral affect, one recent study found that motivational intensity of positive affective states influences this balance in a cognitive set-shifting paradigm. However, working memory updating and set shifting are interrelated but distinct components of cognitive control. The present study examined the effect of low versus high approach-motivated positive affect on the balance between maintenance and flexibility in working memory. A simple cuing paradigm (the AX Continuous Performance Task) was employed, and neutral affect and high and low approach-motivated positive affect were induced using affective pictures. The results revealed that, relative to neutral affect, low approach-motivated positive affect attenuated maintenance and increased flexibility, whereas high approach-motivated positive affect promoted maintenance and decreased flexibility. These findings offer further evidence that the effects of positive affect on cognitive control are modulated by approach motivational intensity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Electrophysiological evidence for flexible goal-directed cue processing during episodic retrieval.

PubMed

Herron, Jane E; Evans, Lisa H; Wilding, Edward L

2016-05-15

A widely held assumption is that memory retrieval is aided by cognitive control processes that are engaged flexibly in service of memory retrieval and memory decisions. While there is some empirical support for this view, a notable exception is the absence of evidence for the flexible use of retrieval control in functional neuroimaging experiments requiring frequent switches between tasks with different cognitive demands. This absence is troublesome in so far as frequent switches between tasks mimic some of the challenges that are typically placed on memory outside the laboratory. In this experiment we instructed participants to alternate frequently between three episodic memory tasks requiring item recognition or retrieval of one of two different kinds of contextual information encoded in a prior study phase (screen location or encoding task). Event-related potentials (ERPs) elicited by unstudied items in the two tasks requiring retrieval of study context were reliably different, demonstrating for the first time that ERPs index task-specific processing of retrieval cues when retrieval goals change frequently. The inclusion of the item recognition task was a novel and important addition in this study, because only the ERPs elicited by unstudied items in one of the two context conditions diverged from those in the item recognition condition. This outcome constrains functional interpretations of the differences that emerged between the two context conditions and emphasises the utility of this baseline in functional imaging studies of retrieval processing operations. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Electrophysiological evidence for flexible goal-directed cue processing during episodic retrieval

PubMed Central

Herron, Jane E.; Evans, Lisa H.; Wilding, Edward L.

2016-01-01

A widely held assumption is that memory retrieval is aided by cognitive control processes that are engaged flexibly in service of memory retrieval and memory decisions. While there is some empirical support for this view, a notable exception is the absence of evidence for the flexible use of retrieval control in functional neuroimaging experiments requiring frequent switches between tasks with different cognitive demands. This absence is troublesome in so far as frequent switches between tasks mimic some of the challenges that are typically placed on memory outside the laboratory. In this experiment we instructed participants to alternate frequently between three episodic memory tasks requiring item recognition or retrieval of one of two different kinds of contextual information encoded in a prior study phase (screen location or encoding task). Event-related potentials (ERPs) elicited by unstudied items in the two tasks requiring retrieval of study context were reliably different, demonstrating for the first time that ERPs index task-specific processing of retrieval cues when retrieval goals change frequently. The inclusion of the item recognition task was a novel and important addition in this study, because only the ERPs elicited by unstudied items in one of the two context conditions diverged from those in the item recognition condition. This outcome constrains functional interpretations of the differences that emerged between the two context conditions and emphasises the utility of this baseline in functional imaging studies of retrieval processing operations. PMID:26892854

Fast algorithms for evaluating the stress field of dislocation lines in anisotropic elastic media

NASA Astrophysics Data System (ADS)

Chen, C.; Aubry, S.; Oppelstrup, T.; Arsenlis, A.; Darve, E.

2018-06-01

In dislocation dynamics (DD) simulations, the most computationally intensive step is the evaluation of the elastic interaction forces among dislocation ensembles. Because the pair-wise interaction between dislocations is long-range, this force calculation step can be significantly accelerated by the fast multipole method (FMM). We implemented and compared four different methods in isotropic and anisotropic elastic media: one based on the Taylor series expansion (Taylor FMM), one based on the spherical harmonics expansion (Spherical FMM), one kernel-independent method based on the Chebyshev interpolation (Chebyshev FMM), and a new kernel-independent method that we call the Lagrange FMM. The Taylor FMM is an existing method, used in ParaDiS, one of the most popular DD simulation softwares. The Spherical FMM employs a more compact multipole representation than the Taylor FMM does and is thus more efficient. However, both the Taylor FMM and the Spherical FMM are difficult to derive in anisotropic elastic media because the interaction force is complex and has no closed analytical formula. The Chebyshev FMM requires only being able to evaluate the interaction between dislocations and thus can be applied easily in anisotropic elastic media. But it has a relatively large memory footprint, which limits its usage. The Lagrange FMM was designed to be a memory-efficient black-box method. Various numerical experiments are presented to demonstrate the convergence and the scalability of the four methods.

Rate kernel theory for pseudo-first-order kinetics of diffusion-influenced reactions and application to fluorescence quenching kinetics.

PubMed

Yang, Mino

2007-06-07

Theoretical foundation of rate kernel equation approaches for diffusion-influenced chemical reactions is presented and applied to explain the kinetics of fluorescence quenching reactions. A many-body master equation is constructed by introducing stochastic terms, which characterize the rates of chemical reactions, into the many-body Smoluchowski equation. A Langevin-type of memory equation for the density fields of reactants evolving under the influence of time-independent perturbation is derived. This equation should be useful in predicting the time evolution of reactant concentrations approaching the steady state attained by the perturbation as well as the steady-state concentrations. The dynamics of fluctuation occurring in equilibrium state can be predicted by the memory equation by turning the perturbation off and consequently may be useful in obtaining the linear response to a time-dependent perturbation. It is found that unimolecular decay processes including the time-independent perturbation can be incorporated into bimolecular reaction kinetics as a Laplace transform variable. As a result, a theory for bimolecular reactions along with the unimolecular process turned off is sufficient to predict overall reaction kinetics including the effects of unimolecular reactions and perturbation. As the present formulation is applied to steady-state kinetics of fluorescence quenching reactions, the exact relation between fluorophore concentrations and the intensity of excitation light is derived.

Increasing feasibility of the field-programmable gate array implementation of an iterative image registration using a kernel-warping algorithm

NASA Astrophysics Data System (ADS)

Nguyen, An Hung; Guillemette, Thomas; Lambert, Andrew J.; Pickering, Mark R.; Garratt, Matthew A.

2017-09-01

Image registration is a fundamental image processing technique. It is used to spatially align two or more images that have been captured at different times, from different sensors, or from different viewpoints. There have been many algorithms proposed for this task. The most common of these being the well-known Lucas-Kanade (LK) and Horn-Schunck approaches. However, the main limitation of these approaches is the computational complexity required to implement the large number of iterations necessary for successful alignment of the images. Previously, a multi-pass image interpolation algorithm (MP-I2A) was developed to considerably reduce the number of iterations required for successful registration compared with the LK algorithm. This paper develops a kernel-warping algorithm (KWA), a modified version of the MP-I2A, which requires fewer iterations to successfully register two images and less memory space for the field-programmable gate array (FPGA) implementation than the MP-I2A. These reductions increase feasibility of the implementation of the proposed algorithm on FPGAs with very limited memory space and other hardware resources. A two-FPGA system rather than single FPGA system is successfully developed to implement the KWA in order to compensate insufficiency of hardware resources supported by one FPGA, and increase parallel processing ability and scalability of the system.

Executive functioning predicts reading, mathematics, and theory of mind during the elementary years.

PubMed

Cantin, Rachelle H; Gnaedinger, Emily K; Gallaway, Kristin C; Hesson-McInnis, Matthew S; Hund, Alycia M

2016-06-01

The goal of this study was to specify how executive functioning components predict reading, mathematics, and theory of mind performance during the elementary years. A sample of 93 7- to 10-year-old children completed measures of working memory, inhibition, flexibility, reading, mathematics, and theory of mind. Path analysis revealed that all three executive functioning components (working memory, inhibition, and flexibility) mediated age differences in reading comprehension, whereas age predicted mathematics and theory of mind directly. In addition, reading mediated the influence of executive functioning components on mathematics and theory of mind, except that flexibility also predicted mathematics directly. These findings provide important details about the development of executive functioning, reading, mathematics, and theory of mind during the elementary years. Copyright © 2016 Elsevier Inc. All rights reserved.

Monkeys recall and reproduce simple shapes from memory.

PubMed

Basile, Benjamin M; Hampton, Robert R

2011-05-10

If you draw from memory a picture of the front of your childhood home, you will have demonstrated recall. You could also recognize this house upon seeing it. Unlike recognition, recall demonstrates memory for things that are not present. Recall is necessary for planning and imagining, and it can increase the flexibility of navigation, social behavior, and other cognitive skills. Without recall, memory is more limited to recognition of the immediate environment. Amnesic patients are impaired on recall tests [1, 2], and recall performance often declines with aging [3]. Despite its importance, we know relatively little about nonhuman animals' ability to recall information; we lack suitable recall tests for them and depend instead on recognition tests to measure nonhuman memory. Here we report that rhesus monkeys can recall simple shapes from memory and reproduce them on a touchscreen. As in humans [4, 5], monkeys remembered less in recall than recognition tests, and their recall performance deteriorated more slowly. Transfer tests showed that monkeys used a flexible memory mechanism rather than memorizing specific actions for each shape. Observation of recall in Old World monkeys suggests that it has been adaptive for over 30 million years [6] and does not depend on language. Copyright © 2011 Elsevier Ltd. All rights reserved.

Cognitive consequences of cannabis use: comparison with abuse of stimulants and heroin with regard to attention, memory and executive functions.

PubMed

Lundqvist, Thomas

2005-06-01

This review aims to compare cognitive consequence between cannabis, and stimulants and heroin with regards to attention, memory and executive functions. The available studies using brain imaging techniques and neuropsychological tests show that acutely, all drugs create a disharmony in the neuropsychological network, causing a decrease of activity in areas responsible for short-term memory and attention, with the possible exception of heroin. Cannabis induces loss of internal control and cognitive impairment, especially of attention and memory, for the duration of intoxication. Heavy cannabis use is associated with reduced function of the attentional/executive system, as exhibited by decreased mental flexibility, increased perserveration, and reduced learning, to shift and/or sustain attention. Recent investigations on amphetamine/methamphetamine have documented deficits in learning, delayed recall, processing speed, and working memory. MDMA users exhibit difficulties in coding information into long-term memory, display impaired verbal learning, are more easily distracted, and are less efficient at focusing attention on complex tasks. The degree of executive impairment increases with the severity of use, and the impairments are relatively lasting over time. Chronic cocaine users display impaired attention, learning, memory, reaction time and cognitive flexibility. Heroin addiction may have a negative effect on impulse control, and selective processing.

SOMKE: kernel density estimation over data streams by sequences of self-organizing maps.

PubMed

Cao, Yuan; He, Haibo; Man, Hong

2012-08-01

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.

Flexible cognitive resources: competitive content maps for attention and memory

PubMed Central

Franconeri, Steven L.; Alvarez, George A.; Cavanagh, Patrick

2013-01-01

The brain has finite processing resources so that, as tasks become harder, performance degrades. Where do the limits on these resources come from? We focus on a variety of capacity-limited buffers related to attention, recognition, and memory that we claim have a two-dimensional ‘map’ architecture, where individual items compete for cortical real estate. This competitive format leads to capacity limits that are flexible, set by the nature of the content and their locations within an anatomically delimited space. We contrast this format with the standard ‘slot’ architecture and its fixed capacity. Using visual spatial attention and visual short-term memory as case studies, we suggest that competitive maps are a concrete and plausible architecture that limits cognitive capacity across many domains. PMID:23428935

Clinical validation of three short forms of the Dutch Wechsler Memory Scale-Fourth Edition (WMS-IV-NL) in a mixed clinical sample.

PubMed

Bouman, Zita; Hendriks, Marc P H; Van Der Veld, William M; Aldenkamp, Albert P; Kessels, Roy P C

2016-06-01

The reliability and validity of three short forms of the Dutch version of the Wechsler Memory Scale-Fourth Edition (WMS-IV-NL) were evaluated in a mixed clinical sample of 235 patients. The short forms were based on the WMS-IV Flexible Approach, that is, a 3-subtest combination (Older Adult Battery for Adults) and two 2-subtest combinations (Logical Memory and Visual Reproduction and Logical Memory and Designs), which can be used to estimate the Immediate, Delayed, Auditory and Visual Memory Indices. All short forms showed good reliability coefficients. As expected, for adults (16-69 years old) the 3-subtest short form was consistently more accurate (predictive accuracy ranged from 73% to 100%) than both 2-subtest short forms (range = 61%-80%). Furthermore, for older adults (65-90 years old), the predictive accuracy of the 2-subtest short form ranged from 75% to 100%. These results suggest that caution is warranted when using the WMS-IV-NL Flexible Approach short forms to estimate all four indices. © The Author(s) 2015.

The Effect of Executive Function on Science Achievement Among Normally Developing 10-Year Olds

NASA Astrophysics Data System (ADS)

Lederman, Sheri G.

Executive function (EF) is an umbrella term used to identify a set of discrete but interrelated cognitive abilities that enable individuals to engage in goal-directed, future-oriented action in response to a novel context. Developmental studies indicate that EF is predictive of reading and math achievement in middle childhood. The purpose of this study was to identify the association between EF and science achievement among normally developing 10 year olds. A sample of fifth grade students from a Northeastern suburban community participated in tests of EF, science, and intelligence. Consistent with adult models of EF, principal components analysis identified a three-factor model of EF organization in middle childhood, including cognitive flexibility, working memory, and inhibition. Multiple regression analyses revealed that executive function processes of cognitive flexibility, working memory, and inhibition were all predictive of science performance. Post hoc analyses revealed that high-performing science students differed significantly from low-performing students in both cognitive flexibility and working memory. These findings suggest that complex academic demands specific to science achievement rely on the emergence and maturation of EF components.

The Effects of Acute Stress on Core Executive Functions: A Meta-Analysis and Comparison with Cortisol

PubMed Central

Shields, Grant S.; Sazma, Matthew A.; Yonelinas, Andrew P.

2016-01-01

Core executive functions such as working memory, inhibition, and cognitive flexibility are integral to daily life. A growing body of research has suggested that acute stress may impair core executive functions. However, there are a number of inconsistencies in the literature, leading to uncertainty about how or even if acute stress influences core executive functions. We addressed this by conducting a meta-analysis of acute stress effects on working memory, inhibition, and cognitive flexibility. We found that stress impaired working memory and cognitive flexibility, whereas it had nuanced effects on inhibition. Many of these effects were moderated by other variables, such as sex. In addition, we compared effects of acute stress on core executive functions to effects of cortisol administration and found some striking differences. Our findings indicate that stress works through mechanisms aside from or in addition to cortisol to produce a state characterized by more reactive processing of salient stimuli but greater control over actions. We conclude by highlighting some important future directions for stress and executive function research. PMID:27371161

Sympathetic arousal, but not disturbed executive functioning, mediates the impairment of cognitive flexibility under stress.

PubMed

Marko, Martin; Riečanský, Igor

2018-05-01

Cognitive flexibility emerges from an interplay of multiple cognitive systems, of which lexical-semantic and executive are thought to be the most important. Yet this has not been addressed by previous studies demonstrating that such forms of flexible thought deteriorate under stress. Motivated by these shortcomings, the present study evaluated several candidate mechanisms implied to mediate the impairing effects of stress on flexible thinking. Fifty-seven healthy adults were randomly assigned to psychosocial stress or control condition while assessed for performance on cognitive flexibility, working memory capacity, semantic fluency, and self-reported cognitive interference. Stress response was indicated by changes in skin conductance, hearth rate, and state anxiety. Our analyses showed that acute stress impaired cognitive flexibility via a concomitant increase in sympathetic arousal, while this mediator was positively associated with semantic fluency. Stress also decreased working memory capacity, which was partially mediated by elevated cognitive interference, but neither of these two measures were associated with cognitive flexibility or sympathetic arousal. Following these findings, we conclude that acute stress impairs cognitive flexibility via sympathetic arousal that modulates lexical-semantic and associative processes. In particular, the results indicate that stress-level of sympathetic activation may restrict the accessibility and integration of remote associates and bias the response competition towards prepotent and dominant ideas. Importantly, our results indicate that stress-induced impairments of cognitive flexibility and executive functions are mediated by distinct neurocognitive mechanisms. Copyright © 2018 Elsevier B.V. All rights reserved.

A flexible new method for 3D measurement based on multi-view image sequences

NASA Astrophysics Data System (ADS)

Cui, Haihua; Zhao, Zhimin; Cheng, Xiaosheng; Guo, Changye; Jia, Huayu

2016-11-01

Three-dimensional measurement is the base part for reverse engineering. The paper developed a new flexible and fast optical measurement method based on multi-view geometry theory. At first, feature points are detected and matched with improved SIFT algorithm. The Hellinger Kernel is used to estimate the histogram distance instead of traditional Euclidean distance, which is immunity to the weak texture image; then a new filter three-principle for filtering the calculation of essential matrix is designed, the essential matrix is calculated using the improved a Contrario Ransac filter method. One view point cloud is constructed accurately with two view images; after this, the overlapped features are used to eliminate the accumulated errors caused by added view images, which improved the camera's position precision. At last, the method is verified with the application of dental restoration CAD/CAM, experiment results show that the proposed method is fast, accurate and flexible for tooth 3D measurement.

Song and speech: examining the link between singing talent and speech imitation ability.

PubMed

Christiner, Markus; Reiterer, Susanne M

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of "speech" on the productive level and "music" on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory.

Song and speech: examining the link between singing talent and speech imitation ability

PubMed Central

Christiner, Markus; Reiterer, Susanne M.

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of “speech” on the productive level and “music” on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory. PMID:24319438

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

Gebis, Joseph; Oliker, Leonid; Shalf, John

The disparity between microprocessor clock frequencies and memory latency is a primary reason why many demanding applications run well below peak achievable performance. Software controlled scratchpad memories, such as the Cell local store, attempt to ameliorate this discrepancy by enabling precise control over memory movement; however, scratchpad technology confronts the programmer and compiler with an unfamiliar and difficult programming model. In this work, we present the Virtual Vector Architecture (ViVA), which combines the memory semantics of vector computers with a software-controlled scratchpad memory in order to provide a more effective and practical approach to latency hiding. ViVA requires minimal changesmore » to the core design and could thus be easily integrated with conventional processor cores. To validate our approach, we implemented ViVA on the Mambo cycle-accurate full system simulator, which was carefully calibrated to match the performance on our underlying PowerPC Apple G5 architecture. Results show that ViVA is able to deliver significant performance benefits over scalar techniques for a variety of memory access patterns as well as two important memory-bound compact kernels, corner turn and sparse matrix-vector multiplication -- achieving 2x-13x improvement compared the scalar version. Overall, our preliminary ViVA exploration points to a promising approach for improving application performance on leading microprocessors with minimal design and complexity costs, in a power efficient manner.« less

Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization.

PubMed

Zhong, Hualiang; Chetty, Indrin J

2012-05-01

Improving dose calculation accuracy is crucial in intensity-modulated radiation therapy (IMRT). We have developed a method for generating a phase-space-based dose kernel for IMRT planning of lung cancer patients. Particle transport in the linear accelerator treatment head of a 21EX, 6 MV photon beam (Varian Medical Systems, Palo Alto, CA) was simulated using the EGSnrc/BEAMnrc code system. The phase space information was recorded under the secondary jaws. Each particle in the phase space file was associated with a beamlet whose index was calculated and saved in the particle's LATCH variable. The DOSXYZnrc code was modified to accumulate the energy deposited by each particle based on its beamlet index. Furthermore, the central axis of each beamlet was calculated from the orientation of all the particles in this beamlet. A cylinder was then defined around the central axis so that only the energy deposited within the cylinder was counted. A look-up table was established for each cylinder during the tallying process. The efficiency and accuracy of the cylindrical beamlet energy deposition approach was evaluated using a treatment plan developed on a simulated lung phantom. Profile and percentage depth doses computed in a water phantom for an open, square field size were within 1.5% of measurements. Dose optimized with the cylindrical dose kernel was found to be within 0.6% of that computed with the nontruncated 3D kernel. The cylindrical truncation reduced optimization time by approximately 80%. A method for generating a phase-space-based dose kernel, using a truncated cylinder for scoring dose, in beamlet-based optimization of lung treatment planning was developed and found to be in good agreement with the standard, nontruncated scoring approach. Compared to previous techniques, our method significantly reduces computational time and memory requirements, which may be useful for Monte-Carlo-based 4D IMRT or IMAT treatment planning.

INDIRECT INTELLIGENT SLIDING MODE CONTROL OF A SHAPE MEMORY ALLOY ACTUATED FLEXIBLE BEAM USING HYSTERETIC RECURRENT NEURAL NETWORKS.

PubMed

Hannen, Jennifer C; Crews, John H; Buckner, Gregory D

2012-08-01

This paper introduces an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators, specifically a flexible beam deflected by a single offset SMA tendon. The controller manipulates applied voltage, which alters SMA tendon temperature to track reference bending angles. A hysteretic recurrent neural network (HRNN) captures the nonlinear, hysteretic relationship between SMA temperature and bending angle. The variable structure control strategy provides robustness to model uncertainties and parameter variations, while effectively compensating for system nonlinearities, achieving superior tracking compared to an optimized PI controller.

Inhibition, flexibility, working memory and planning in autism spectrum disorders with and without comorbid ADHD-symptoms

PubMed Central

Sinzig, Judith; Morsch, Dagmar; Bruning, Nicole; Schmidt, Martin H; Lehmkuhl, Gerd

2008-01-01

Background Recent studies have not paid a great deal of attention to comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms in autistic children even though it is well known that almost half of children with autism spectrum disorder (ASD) suffer from hyperactivity, inattention and impulsivity. The goal of this study was to evaluate and compare executive functioning (EF) profiles in children with ADHD and in children with ASD with and without comorbid ADHD. Methods Children aged 6 to 18 years old with ADHD (n = 20) or ASD (High-Functioning autism or Asperger syndrome) with (n = 20) and without (n = 20) comorbid ADHD and a typically developing group (n = 20) were compared on a battery of EF tasks comprising inhibition, flexibility, working memory and planning tasks. A MANOVA, effect sizes as well as correlations between ADHD-symptomatology and EF performance were calculated. Age- and IQ-corrected z scores were used. Results There was a significant effect for the factor group (F = 1.55; dF = 42; p = .02). Post-hoc analysis revealed significant differences between the ADHD and the TD group on the inhibition task for false alarms (p = .01) and between the ADHD group, the ASD+ group (p = .03), the ASD- group (p = .02) and the TD group (p = .01) for omissions. Effect sizes showed clear deficits of ADHD children in inhibition and working memory tasks. Participants with ASD were impaired in planning and flexibility abilities. The ASD+ group showed compared to the ASD- group more problems in inhibitory performance but not in the working memory task. Conclusion Our findings replicate previous results reporting impairment of ADHD children in inhibition and working memory tasks and of ASD children in planning and flexibility abilities. The ASD + group showed similarities to the ADHD group with regard to inhibitory but not to working memory deficits. Nevertheless the heterogeneity of these and previous results shows that EF assessment is not useful for differential diagnosis between ADHD and ASD. It might be useful for evaluating strengths and weaknesses in individual children. PMID:18237439

Robust retention and transfer of tool construction techniques in chimpanzees (Pan troglodytes).

PubMed

Vale, Gill L; Flynn, Emma G; Pender, Lydia; Price, Elizabeth; Whiten, Andrew; Lambeth, Susan P; Schapiro, Steven J; Kendal, Rachel L

2016-02-01

Long-term memory can be critical to a species' survival in environments with seasonal and even longer-term cycles of resource availability. The present, longitudinal study investigated whether complex tool behaviors used to gain an out-of-reach reward, following a hiatus of about 3 years and 7 months since initial experiences with a tool use task, were retained and subsequently executed more quickly by experienced than by naïve chimpanzees. Ten of the 11 retested chimpanzees displayed impressive long-term procedural memory, creating elongated tools using the same methods employed years previously, either combining 2 tools or extending a single tool. The complex tool behaviors were also transferred to a different task context, showing behavioral flexibility. This represents some of the first evidence for appreciable long-term procedural memory, and improvements in the utility of complex tool manufacture in chimpanzees. Such long-term procedural memory and behavioral flexibility have important implications for the longevity and transmission of behavioral traditions. (c) 2016 APA, all rights reserved).

Highly reliable top-gated thin-film transistor memory with semiconducting, tunneling, charge-trapping, and blocking layers all of flexible polymers.

PubMed

Wang, Wei; Hwang, Sun Kak; Kim, Kang Lib; Lee, Ju Han; Cho, Suk Man; Park, Cheolmin

2015-05-27

The core components of a floating-gate organic thin-film transistor nonvolatile memory (OTFT-NVM) include the semiconducting channel layer, tunneling layer, floating-gate layer, and blocking layer, besides three terminal electrodes. In this study, we demonstrated OTFT-NVMs with all four constituent layers made of polymers based on consecutive spin-coating. Ambipolar charges injected and trapped in a polymer electret charge-controlling layer upon gate program and erase field successfully allowed for reliable bistable channel current levels at zero gate voltage. We have observed that the memory performance, in particular the reliability of a device, significantly depends upon the thickness of both blocking and tunneling layers, and with an optimized layer thickness and materials selection, our device exhibits a memory window of 15.4 V, on/off current ratio of 2 × 10(4), read and write endurance cycles over 100, and time-dependent data retention of 10(8) s, even when fabricated on a mechanically flexible plastic substrate.

[Executive dysfunctions in adults with attention deficit hyperactivity disorder].

PubMed

Rodriguez-Jiménez, R; Cubillo, A; Jiménez-Arriero, M A; Ponce, G; Aragüés-Figuero, M; Palomo, T

Several different follow-up studies have shown that attention deficit hyperactivity disorder (ADHD) can persist into adulthood. To review the findings in adults with ADHD related to alterations in the executive functions. Research conducted among children with ADHD has revealed the existence of alterations in different tasks that evaluate the executive functions, such as the planning test, sustained attention tasks, cognitive flexibility, verbal fluency and working memory tasks, as well as several inhibition response tasks. In adults with ADHD, despite the lower number of reports in the literature and the methodological shortcomings that exist in some studies, analogous results have also been described with respect to executive functioning, namely, disorders affecting inhibition response, the capacity for planning, difficulties in cognitive flexibility and verbal fluency, and problems with working memory, which include aspects of spatial working memory, logical or visual memory. The findings we have available at present enable us to confirm the persistence of executive dysfunctions in adult patients with ADHD that are similar to those observed in children with ADHD.

Robust Retention and Transfer of Tool Construction Techniques in Chimpanzees (Pan troglodytes)

PubMed Central

Vale, Gill L.; Flynn, Emma G.; Pender, Lydia; Price, Elizabeth; Whiten, Andrew; Lambeth, Susan P.; Schapiro, Steven J.; Kendal, Rachel L.

2016-01-01

Long-term memory can be critical to a species’ survival in environments with seasonal and even longer-term cycles of resource availability. The present, longitudinal study investigated whether complex tool behaviors used to gain an out-of-reach reward, following a hiatus of about 3 years and 7 months since initial experiences with a tool use task, were retained and subsequently executed more quickly by experienced than by naïve chimpanzees. Ten of the 11 retested chimpanzees displayed impressive long-term procedural memory, creating elongated tools using the same methods employed years previously, either combining 2 tools or extending a single tool. The complex tool behaviors were also transferred to a different task context, showing behavioral flexibility. This represents some of the first evidence for appreciable long-term procedural memory, and improvements in the utility of complex tool manufacture in chimpanzees. Such long-term procedural memory and behavioral flexibility have important implications for the longevity and transmission of behavioral traditions. PMID:26881941

WE-AB-303-09: Rapid Projection Computations for On-Board Digital Tomosynthesis in Radiation Therapy

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

Iliopoulos, AS; Sun, X; Pitsianis, N

2015-06-15

Purpose: To facilitate fast and accurate iterative volumetric image reconstruction from limited-angle on-board projections. Methods: Intrafraction motion hinders the clinical applicability of modern radiotherapy techniques, such as lung stereotactic body radiation therapy (SBRT). The LIVE system may impact clinical practice by recovering volumetric information via Digital Tomosynthesis (DTS), thus entailing low time and radiation dose for image acquisition during treatment. The DTS is estimated as a deformation of prior CT via iterative registration with on-board images; this shifts the challenge to the computational domain, owing largely to repeated projection computations across iterations. We address this issue by composing efficient digitalmore » projection operators from their constituent parts. This allows us to separate the static (projection geometry) and dynamic (volume/image data) parts of projection operations by means of pre-computations, enabling fast on-board processing, while also relaxing constraints on underlying numerical models (e.g. regridding interpolation kernels). Further decoupling the projectors into simpler ones ensures the incurred memory overhead remains low, within the capacity of a single GPU. These operators depend only on the treatment plan and may be reused across iterations and patients. The dynamic processing load is kept to a minimum and maps well to the GPU computational model. Results: We have integrated efficient, pre-computable modules for volumetric ray-casting and FDK-based back-projection with the LIVE processing pipeline. Our results show a 60x acceleration of the DTS computations, compared to the previous version, using a single GPU; presently, reconstruction is attained within a couple of minutes. The present implementation allows for significant flexibility in terms of the numerical and operational projection model; we are investigating the benefit of further optimizations and accurate digital projection sub-kernels. Conclusion: Composable projection operators constitute a versatile research tool which can greatly accelerate iterative registration algorithms and may be conducive to the clinical applicability of LIVE. National Institutes of Health Grant No. R01-CA184173; GPU donation by NVIDIA Corporation.« less

Stochastic Dynamics Underlying Cognitive Stability and Flexibility

PubMed Central

Ueltzhöffer, Kai; Armbruster-Genç, Diana J. N.; Fiebach, Christian J.

2015-01-01

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences. PMID:26068119

Characterization of Nonlinear Systems with Memory by Means of Volterra Expansions with Frequency Partitioning: Application to a Cicada Mating Call

DTIC Science & Technology

2010-06-15

Partitioning Application to a Cicada Mating Call Albert H. Nuttall Adaptive Methods Inc. Derke R. Hughes NUWC Division Newport IVAVSEA WARFARE...Frequency Partitioning: Application to a Cicada Mating Call 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Albert H... cicada mating call with a distinctly non-white and non-Gaussian excitation gives good results for the estimated first- and second-order kernels and

Static Analysis Using Abstract Interpretation

NASA Technical Reports Server (NTRS)

Arthaud, Maxime

2017-01-01

Short presentation about static analysis and most particularly abstract interpretation. It starts with a brief explanation on why static analysis is used at NASA. Then, it describes the IKOS (Inference Kernel for Open Static Analyzers) tool chain. Results on NASA projects are shown. Several well known algorithms from the static analysis literature are then explained (such as pointer analyses, memory analyses, weak relational abstract domains, function summarization, etc.). It ends with interesting problems we encountered (such as C++ analysis with exception handling, or the detection of integer overflow).

Analysis of a Measured Launch

DTIC Science & Technology

2007-06-05

whether or not the module is capable of changing from a not - good to a good state. If this is true, the module is associated with a portion of main memory...the module is ca- pable of changing from a good to a not - good state. A false value reflects the ability of good software to protect itself from...qualities of the module. One exception to this rule is that the kernel is corruptible if and only if the hypervisor is not good , since a bad

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

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

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Language, cognitive flexibility, and explicit false belief understanding: longitudinal analysis in typical development and specific language impairment.

PubMed

Farrant, Brad M; Maybery, Murray T; Fletcher, Janet

2012-01-01

The hypothesis that language plays a role in theory-of-mind (ToM) development is supported by a number of lines of evidence (e.g., H. Lohmann & M. Tomasello, 2003). The current study sought to further investigate the relations between maternal language input, memory for false sentential complements, cognitive flexibility, and the development of explicit false belief understanding in 91 English-speaking typically developing children (M age = 61.3 months) and 30 children with specific language impairment (M age = 63.0 months). Concurrent and longitudinal findings converge in supporting a model in which maternal language input predicts the child's memory for false complements, which predicts cognitive flexibility, which in turn predicts explicit false belief understanding. © 2011 The Authors. Child Development © 2011 Society for Research in Child Development, Inc.

Study protocol for a randomised, controlled platform trial estimating the effect of autobiographical Memory Flexibility training (MemFlex) on relapse of recurrent major depressive disorder

PubMed Central

Gormley, Siobhan; O’Leary, Cliodhna; Rodrigues, Evangeline; Wright, Isobel; Griffiths, Kirsty; Gillard, Julia; Watson, Peter; Hammond, Emily; Werner-Seidler, Aliza; Dalgleish, Tim

2018-01-01

Introduction Major depressive disorder (MDD) is a chronic condition. Although current treatment approaches are effective in reducing acute depressive symptoms, rates of relapse are high. Chronic and inflexible retrieval of autobiographical memories, and in particular a bias towards negative and overgeneral memories, is a reliable predictor of relapse. This randomised controlled single-blind trial will determine whether a therapist-guided self-help intervention to ameliorate autobiographical memory biases using Memory Flexibility training (MemFlex) will increase the experience of depression-free days, relative to a psychoeducation control condition, in the 12 months following intervention. Methods and analysis Individuals (aged 18 and above) with a diagnosis of recurrent MDD will be recruited when remitted from a major depressive episode. Participants will be randomly allocated to complete 4 weeks of a workbook providing either MemFlex training, or psychoeducation on factors that increase risk of relapse. Assessment of diagnostic status, self-report depressive symptoms, depression-free days and cognitive risk factors for depression will be completed post-intervention, and at 6 and 12 months follow-up. The cognitive target of MemFlex will be change in memory flexibility on the Autobiographical Memory Test- Alternating Instructions. The primary clinical endpoints will be the number of depression-free days in the 12 months following workbook completion, and time to depressive relapse. Ethics and dissemination Ethics approval has been granted by the NHS National Research Ethics Committee (East of England, 11/H0305/1). Results from this study will provide a point-estimate of the effect of MemFlex on depressive relapse, which will be used to inform a fully powered trial evaluating the potential of MemFlex as an effective, low-cost and low-intensity option for reducing relapse of MDD. Trial registration number NCT02614326. PMID:29382674

Virtual reality based adaptive dose assessment method for arbitrary geometries in nuclear facility decommissioning.

PubMed

Liu, Yong-Kuo; Chao, Nan; Xia, Hong; Peng, Min-Jun; Ayodeji, Abiodun

2018-05-17

This paper presents an improved and efficient virtual reality-based adaptive dose assessment method (VRBAM) applicable to the cutting and dismantling tasks in nuclear facility decommissioning. The method combines the modeling strength of virtual reality with the flexibility of adaptive technology. The initial geometry is designed with the three-dimensional computer-aided design tools, and a hybrid model composed of cuboids and a point-cloud is generated automatically according to the virtual model of the object. In order to improve the efficiency of dose calculation while retaining accuracy, the hybrid model is converted to a weighted point-cloud model, and the point kernels are generated by adaptively simplifying the weighted point-cloud model according to the detector position, an approach that is suitable for arbitrary geometries. The dose rates are calculated with the Point-Kernel method. To account for radiation scattering effects, buildup factors are calculated with the Geometric-Progression formula in the fitting function. The geometric modeling capability of VRBAM was verified by simulating basic geometries, which included a convex surface, a concave surface, a flat surface and their combination. The simulation results show that the VRBAM is more flexible and superior to other approaches in modeling complex geometries. In this paper, the computation time and dose rate results obtained from the proposed method were also compared with those obtained using the MCNP code and an earlier virtual reality-based method (VRBM) developed by the same authors. © 2018 IOP Publishing Ltd.

The role of the hippocampus in flexible cognition and social behavior

PubMed Central

Rubin, Rachael D.; Watson, Patrick D.; Duff, Melissa C.; Cohen, Neal J.

2014-01-01

Successful behavior requires actively acquiring and representing information about the environment and people, and manipulating and using those acquired representations flexibly to optimally act in and on the world. The frontal lobes have figured prominently in most accounts of flexible or goal-directed behavior, as evidenced by often-reported behavioral inflexibility in individuals with frontal lobe dysfunction. Here, we propose that the hippocampus also plays a critical role by forming and reconstructing relational memory representations that underlie flexible cognition and social behavior. There is mounting evidence that damage to the hippocampus can produce inflexible and maladaptive behavior when such behavior places high demands on the generation, recombination, and flexible use of information. This is seen in abilities as diverse as memory, navigation, exploration, imagination, creativity, decision-making, character judgments, establishing and maintaining social bonds, empathy, social discourse, and language use. Thus, the hippocampus, together with its extensive interconnections with other neural systems, supports the flexible use of information in general. Further, we suggest that this understanding has important clinical implications. Hippocampal abnormalities can produce profound deficits in real-world situations, which typically place high demands on the flexible use of information, but are not always obvious on diagnostic tools tuned to frontal lobe function. This review documents the role of the hippocampus in supporting flexible representations and aims to expand our understanding of the dynamic networks that operate as we move through and create meaning of our world. PMID:25324753

The role of the hippocampus in flexible cognition and social behavior.

PubMed

Rubin, Rachael D; Watson, Patrick D; Duff, Melissa C; Cohen, Neal J

2014-01-01

Successful behavior requires actively acquiring and representing information about the environment and people, and manipulating and using those acquired representations flexibly to optimally act in and on the world. The frontal lobes have figured prominently in most accounts of flexible or goal-directed behavior, as evidenced by often-reported behavioral inflexibility in individuals with frontal lobe dysfunction. Here, we propose that the hippocampus also plays a critical role by forming and reconstructing relational memory representations that underlie flexible cognition and social behavior. There is mounting evidence that damage to the hippocampus can produce inflexible and maladaptive behavior when such behavior places high demands on the generation, recombination, and flexible use of information. This is seen in abilities as diverse as memory, navigation, exploration, imagination, creativity, decision-making, character judgments, establishing and maintaining social bonds, empathy, social discourse, and language use. Thus, the hippocampus, together with its extensive interconnections with other neural systems, supports the flexible use of information in general. Further, we suggest that this understanding has important clinical implications. Hippocampal abnormalities can produce profound deficits in real-world situations, which typically place high demands on the flexible use of information, but are not always obvious on diagnostic tools tuned to frontal lobe function. This review documents the role of the hippocampus in supporting flexible representations and aims to expand our understanding of the dynamic networks that operate as we move through and create meaning of our world.

Bilingualism and Working Memory Capacity: A Comprehensive Meta-Analysis

ERIC Educational Resources Information Center

Grundy, John G.; Timmer, Kalinka

2017-01-01

Bilinguals often outperform monolinguals on executive function tasks, including tasks that tap cognitive flexibility, conflict monitoring, and task-switching abilities. Some have suggested that bilinguals also have greater working memory capacity than comparable monolinguals, but evidence for this suggestion is mixed. We therefore conducted a…

Positive Affect Modulates Flexibility and Evaluative Control

ERIC Educational Resources Information Center

van Wouwe, Nelleke C.; Band, Guido P. H.; Ridderinkhof, K. Richard

2011-01-01

The ability to interact with a constantly changing environment requires a balance between maintaining the currently relevant working memory content and being sensitive to potentially relevant new information that should be given priority access to working memory. Mesocortical dopamine projections to frontal brain areas modulate working memory…

Development and characterization of a ferroelectric non-volatile memory for flexible electronics

NASA Astrophysics Data System (ADS)

Mao, Duo

Flexible electronics have received significant attention recently because of the potential applications in displays, sensors, radio frequency identification (RFID) tags and other integrated circuits. Electrically addressable non-volatile memory is a key component for these applications. The major challenges are to fabricate the memory at a low temperature compatible with plastic substrates while maintaining good device reliability, by being compatible with process as needed to integrate with other electronic components for system-on-chip applications. In this work, ferroelectric capacitors fabricated at low temperature were developed. Based on that, a ferroelectric random access memory (FRAM) for flexible electronics was developed and characterized. Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] copolymer was used as a ferroelectric material and a photolithographic process was developed to fabricate ferroelectric capacitors. Different characterization methods including atomic force microscopy, x-ray diffraction and Fourier-transform infrared reflection-absorption spectroscopy were used to study the material properties of the P(VDF-TrFE) film. The material properties were correlated with the electrical characteristics of the ferroelectric capacitors. To understand the polarization switching behavior of the P(VDF-TrFE) ferroelectric capacitors, a Nucleation-Limited-Switching (NLS) model was used to study the switching kinetics. The switching kinetics were characterized over the temperature range from -60 °C to 100 °C. Fatigue characteristics were studied at different electrical stress voltages and frequencies to evaluate the reliability of the ferroelectric capacitor. The degradation mechanism is attributed to the increase of the activation field and the suppression of the switchable polarization. To develop a FRAM circuit for flexible electronics, an n-channel thin film transistor (TFT) based on CdS as the semiconductor was integrated with a P(VDF-TrFE) ferroelectric capacitor for a one-transistor-one-capacitor (1T1C) memory cell. The 1T1C devices were fabricated at low temperature and demonstrated a memory window (DeltaVBL) of 2.3 V and 3.5 V, depending on the device dimensions. Next, FRAM arrays (4-bit, 16-bit and 64-bit) based on the two-transistor-two-capacitor (2T2C) memory cell architecture were designed and fabricated using a photolithographic process with 9 masks. The fabricated FRAM arrays were packaged in 28-pin ceramic packages. The read/write schemes were developed and the FRAM arrays show successful program and erase with a memory window of approximately 1 V at the output of the sense amplifier.

[Timing and effectiveness of Brenner's IPT cognitive training in early psychosis. A pilot study].

PubMed

Borriello, Adriana; Balbi, Andrea; Menichincheri, Renato Maria; Mirabella, Fiorino

2015-01-01

The present study evaluates the outcome of cognitive training as part of Brenner's Integrated Psychological Therapy (IPT) in two groups of individuals with a schizophrenic spectrum disorder (F20-F24 ICD-10). 28 participants were divided into either an experimental group or a control group. The experimental group was composed of 13 individuals (46%) with a mean age of 21.2 years and a mean duration of illness (since their first episode of psychosis FEP) of 15.6 months. The control group included 15 individuals (54%) with a mean age of 25.6 years and a mean duration of illness of 74.4 months (beyond the critical period). Participants underwent an assessment of cognitive functioning which focused on attention, memory, executive functioning and cognitive flexibility as measured by the WCST (Wisconsin Card Sorting Test). Each individual was tested pre- and 6-month post-intervention. The original IPT method was altered by reducing the frequency of sessions to once a week and by limiting our sessions to 2-3 individuals per group. Cognitive flexibility (p<0.01) and long-term memory (p<0.01) improved only in the experimental group. These former skills worsened in the control group (p<0.01). Selective attention, short-term memory and verbal fluency improved in both groups (from p<0.05 to p<0.01). IPT cognitive training, when delivered in the early stages of psychosis (within 18 months from FEP), seems to be particularly effective in improving cognitive flexibility and long-term memory. We did not see improvements in those who had a longer duration of illness who also underwent the same treatment. Cognitive flexibility is linked to clinical insight and social cognition. Therefore, improving this function may lead to a better outcome for patients.

Power and Performance Trade-offs for Space Time Adaptive Processing

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

Gawande, Nitin A.; Manzano Franco, Joseph B.; Tumeo, Antonino

Computational efficiency – performance relative to power or energy – is one of the most important concerns when designing RADAR processing systems. This paper analyzes power and performance trade-offs for a typical Space Time Adaptive Processing (STAP) application. We study STAP implementations for CUDA and OpenMP on two computationally efficient architectures, Intel Haswell Core I7-4770TE and NVIDIA Kayla with a GK208 GPU. We analyze the power and performance of STAP’s computationally intensive kernels across the two hardware testbeds. We also show the impact and trade-offs of GPU optimization techniques. We show that data parallelism can be exploited for efficient implementationmore » on the Haswell CPU architecture. The GPU architecture is able to process large size data sets without increase in power requirement. The use of shared memory has a significant impact on the power requirement for the GPU. A balance between the use of shared memory and main memory access leads to an improved performance in a typical STAP application.« less

Attenuation of the NMR signal in a field gradient due to stochastic dynamics with memory

NASA Astrophysics Data System (ADS)

Lisý, Vladimír; Tóthová, Jana

2017-03-01

The attenuation function S(t) for an ensemble of spins in a magnetic-field gradient is calculated by accumulation of the phase shifts in the rotating frame resulting from the displacements of spin-bearing particles. The found S(t), expressed through the particle mean square displacement, is applicable for any kind of stationary stochastic motion of spins, including their non-markovian dynamics with memory. The known expressions valid for normal and anomalous diffusion are obtained as special cases in the long time approximation. The method is also applicable to the NMR pulse sequences based on the refocusing principle. This is demonstrated by describing the Hahn spin echo experiment. The attenuation of the NMR signal is also evaluated providing that the random motion of particle is modeled by the generalized Langevin equation with the memory kernel exponentially decaying in time. The models considered in our paper assume massive particles driven by much smaller particles.

Proceedings of the second SISAL users` conference

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

Feo, J T; Frerking, C; Miller, P J

1992-12-01

This report contains papers on the following topics: A sisal code for computing the fourier transform on S{sub N}; five ways to fill your knapsack; simulating material dislocation motion in sisal; candis as an interface for sisal; parallelisation and performance of the burg algorithm on a shared-memory multiprocessor; use of genetic algorithm in sisal to solve the file design problem; implementing FFT`s in sisal; programming and evaluating the performance of signal processing applications in the sisal programming environment; sisal and Von Neumann-based languages: translation and intercommunication; an IF2 code generator for ADAM architecture; program partitioning for NUMA multiprocessor computer systems;more » mapping functional parallelism on distributed memory machines; implicit array copying: prevention is better than cure ; mathematical syntax for sisal; an approach for optimizing recursive functions; implementing arrays in sisal 2.0; Fol: an object oriented extension to the sisal language; twine: a portable, extensible sisal execution kernel; and investigating the memory performance of the optimizing sisal compiler.« less

Wide memory window in graphene oxide charge storage nodes

NASA Astrophysics Data System (ADS)

Wang, Shuai; Pu, Jing; Chan, Daniel S. H.; Cho, Byung Jin; Loh, Kian Ping

2010-04-01

Solution-processable, isolated graphene oxide (GO) monolayers have been used as a charge trapping dielectric in TaN gate/Al2O3/isolated GO sheets/SiO2/p-Si memory device (TANOS). The TANOS type structure serves as memory device with the threshold voltage controlled by the amount of charge trapped in the GO sheet. Capacitance-Voltage hysteresis curves reveal a 7.5 V memory window using the sweep voltage of -5-14 V. Thermal reduction in the GO to graphene reduces the memory window to 1.4 V. The unique charge trapping properties of GO points to the potential applications in flexible organic memory devices.

Role of attentional tags in working memory-driven attentional capture.

PubMed

Kuo, Chun-Yu; Chao, Hsuan-Fu

2014-08-01

Recent studies have demonstrated that the contents of working memory capture attention when performing a visual search task. However, it remains an intriguing and unresolved question whether all kinds of items stored in working memory capture attention. The present study investigated this issue by manipulating the attentional tags (target or distractor) associated with information maintained in working memory. The results showed that working memory-driven attentional capture is a flexible process, and that attentional tags associated with items stored in working memory do modulate attentional capture. When items were tagged as a target, they automatically captured attention; however, when items were tagged as a distractor, attentional capture was reduced.

On-chip photonic memory elements employing phase-change materials.

PubMed

Rios, Carlos; Hosseini, Peiman; Wright, C David; Bhaskaran, Harish; Pernice, Wolfram H P

2014-03-05

Phase-change materials integrated into nanophotonic circuits provide a flexible way to realize tunable optical components. Relying on the enormous refractive-index contrast between the amorphous and crystalline states, such materials are promising candidates for on-chip photonic memories. Nonvolatile memory operation employing arrays of microring resonators is demonstrated as a route toward all-photonic chipscale information processing. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Naïve and the Distrustful: state dependency of hippocampal computations in manipulative memory distortion.

PubMed

Ludmer, Rachel; Edelson, Micah G; Dudai, Yadin

2015-02-01

Flexible mnemonic mechanisms that adjust to different internal mental states can provide a major adaptive advantage. However, little is known regarding how this flexibility is achieved in the human brain. We examined brain activity during retrieval of false memories of a movie, generated by exposing participants to misleading information. Half of the participants suspected the memory manipulation (Distrustful), whereas the other half did not (Naïve). Distrustful displayed more accurate memory performance and a brain signature different than that of Naïve. In Distrustful, the ability to differentiate true from false information was driven by a qualitatively distinct hippocampal activity for endorsed items, consistent with the view that hippocampal encoding allows recollection of a specific source. Conversely, in Naïve, BOLD differences between true and false memories were linearly correlated with accuracy across participants, suggesting that Naïve subjects needed to reinstate and evaluate stored information to discern true from false. We propose that our results lend support to models suggesting that hippocampal activity can exhibit different computational schemes, depending on memorandum attributes. Furthermore, we show that trust, considered as a subjective state of mind, may alter basic hippocampal strategies, influencing the ability to separate real from false memory. © 2014 Wiley Periodicals, Inc.

A Tradeoff Between Accuracy and Flexibility in a Working Memory Circuit Endowed with Slow Feedback Mechanisms

PubMed Central

Pereira, Jacinto; Wang, Xiao-Jing

2015-01-01

Recent studies have shown that reverberation underlying mnemonic persistent activity must be slow, to ensure the stability of a working memory system and to give rise to long neural transients capable of accumulation of information over time. Is the slower the underlying process, the better? To address this question, we investigated 3 slow biophysical mechanisms that are activity-dependent and prominently present in the prefrontal cortex: Depolarization-induced suppression of inhibition (DSI), calcium-dependent nonspecific cationic current (ICAN), and short-term facilitation. Using a spiking network model for spatial working memory, we found that these processes enhance the memory accuracy by counteracting noise-induced drifts, heterogeneity-induced biases, and distractors. Furthermore, the incorporation of DSI and ICAN enlarges the range of network's parameter values required for working memory function. However, when a progressively slower process dominates the network, it becomes increasingly more difficult to erase a memory trace. We demonstrate this accuracy–flexibility tradeoff quantitatively and interpret it using a state-space analysis. Our results supports the scenario where N-methyl-d-aspartate receptor-dependent recurrent excitation is the workhorse for the maintenance of persistent activity, whereas slow synaptic or cellular processes contribute to the robustness of mnemonic function in a tradeoff that potentially can be adjusted according to behavioral demands. PMID:25253801

Identification of subsurface structures using electromagnetic data and shape priors

NASA Astrophysics Data System (ADS)

Tveit, Svenn; Bakr, Shaaban A.; Lien, Martha; Mannseth, Trond

2015-03-01

We consider the inverse problem of identifying large-scale subsurface structures using the controlled source electromagnetic method. To identify structures in the subsurface where the contrast in electric conductivity can be small, regularization is needed to bias the solution towards preserving structural information. We propose to combine two approaches for regularization of the inverse problem. In the first approach we utilize a model-based, reduced, composite representation of the electric conductivity that is highly flexible, even for a moderate number of degrees of freedom. With a low number of parameters, the inverse problem is efficiently solved using a standard, second-order gradient-based optimization algorithm. Further regularization is obtained using structural prior information, available, e.g., from interpreted seismic data. The reduced conductivity representation is suitable for incorporation of structural prior information. Such prior information cannot, however, be accurately modeled with a gaussian distribution. To alleviate this, we incorporate the structural information using shape priors. The shape prior technique requires the choice of kernel function, which is application dependent. We argue for using the conditionally positive definite kernel which is shown to have computational advantages over the commonly applied gaussian kernel for our problem. Numerical experiments on various test cases show that the methodology is able to identify fairly complex subsurface electric conductivity distributions while preserving structural prior information during the inversion.

CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

PubMed Central

Porto, William F.; Pires, Állan S.; Franco, Octavio L.

2012-01-01

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023

Stress and multiple memory systems: from 'thinking' to 'doing'.

PubMed

Schwabe, Lars; Wolf, Oliver T

2013-02-01

Although it has been known for decades that stress influences memory performance, it was only recently shown that stress may alter the contribution of multiple, anatomically and functionally distinct memory systems to behavior. Here, we review recent animal and human studies demonstrating that stress promotes a shift from flexible 'cognitive' to rather rigid 'habit' memory systems and discuss, based on recent neuroimaging data in humans, the underlying brain mechanisms. We argue that, despite being generally adaptive, this stress-induced shift towards 'habit' memory may, in vulnerable individuals, be a risk factor for psychopathology. Copyright © 2012 Elsevier Ltd. All rights reserved.

Status and Prospects of ZnO-Based Resistive Switching Memory Devices

NASA Astrophysics Data System (ADS)

Simanjuntak, Firman Mangasa; Panda, Debashis; Wei, Kung-Hwa; Tseng, Tseung-Yuen

2016-08-01

In the advancement of the semiconductor device technology, ZnO could be a prospective alternative than the other metal oxides for its versatility and huge applications in different aspects. In this review, a thorough overview on ZnO for the application of resistive switching memory (RRAM) devices has been conducted. Various efforts that have been made to investigate and modulate the switching characteristics of ZnO-based switching memory devices are discussed. The use of ZnO layer in different structure, the different types of filament formation, and the different types of switching including complementary switching are reported. By considering the huge interest of transparent devices, this review gives the concrete overview of the present status and prospects of transparent RRAM devices based on ZnO. ZnO-based RRAM can be used for flexible memory devices, which is also covered here. Another challenge in ZnO-based RRAM is that the realization of ultra-thin and low power devices. Nevertheless, ZnO not only offers decent memory properties but also has a unique potential to be used as multifunctional nonvolatile memory devices. The impact of electrode materials, metal doping, stack structures, transparency, and flexibility on resistive switching properties and switching parameters of ZnO-based resistive switching memory devices are briefly compared. This review also covers the different nanostructured-based emerging resistive switching memory devices for low power scalable devices. It may give a valuable insight on developing ZnO-based RRAM and also should encourage researchers to overcome the challenges.

Stepping into a Map: Initial Heading Direction Influences Spatial Memory Flexibility

ERIC Educational Resources Information Center

Gagnon, Stephanie A.; Brunyé, Tad T.; Gardony, Aaron; Noordzij, Matthijs L.; Mahoney, Caroline R.; Taylor, Holly A.

2014-01-01

Learning a novel environment involves integrating first-person perceptual and motoric experiences with developing knowledge about the overall structure of the surroundings. The present experiments provide insights into the parallel development of these egocentric and allocentric memories by intentionally conflicting body- and world-centered frames…

Thinking about the Future Early in Life: The Role of Relational Memory

ERIC Educational Resources Information Center

Richmond, Jenny L.; Pan, Rose

2013-01-01

The constructive episodic simulation hypothesis suggests that we imagine possible future events by flexibly recombining details of past experiences to produce novel scenarios. Here we tested this hypothesis by determining whether episodic future thinking is related to relational memory ability during the preschool years. Children (3- to…

A new analysis of the Fornberg-Whitham equation pertaining to a fractional derivative with Mittag-Leffler-type kernel

NASA Astrophysics Data System (ADS)

Kumar, Devendra; Singh, Jagdev; Baleanu, Dumitru

2018-02-01

The mathematical model of breaking of non-linear dispersive water waves with memory effect is very important in mathematical physics. In the present article, we examine a novel fractional extension of the non-linear Fornberg-Whitham equation occurring in wave breaking. We consider the most recent theory of differentiation involving the non-singular kernel based on the extended Mittag-Leffler-type function to modify the Fornberg-Whitham equation. We examine the existence of the solution of the non-linear Fornberg-Whitham equation of fractional order. Further, we show the uniqueness of the solution. We obtain the numerical solution of the new arbitrary order model of the non-linear Fornberg-Whitham equation with the aid of the Laplace decomposition technique. The numerical outcomes are displayed in the form of graphs and tables. The results indicate that the Laplace decomposition algorithm is a very user-friendly and reliable scheme for handling such type of non-linear problems of fractional order.

A Software Architecture for Adaptive Modular Sensing Systems

PubMed Central

Lyle, Andrew C.; Naish, Michael D.

2010-01-01

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration. PMID:22163614

A software architecture for adaptive modular sensing systems.

PubMed

Lyle, Andrew C; Naish, Michael D

2010-01-01

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration.

Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Sarukkai, Sekhar R.; Mehra, Pankaj; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

This paper presents a methodology for debugging the performance of message-passing programs on both tightly coupled and loosely coupled distributed-memory machines. The AIMS (Automated Instrumentation and Monitoring System) toolkit, a suite of software tools for measurement and analysis of performance, is introduced and its application illustrated using several benchmark programs drawn from the field of computational fluid dynamics. AIMS includes (i) Xinstrument, a powerful source-code instrumentor, which supports both Fortran77 and C as well as a number of different message-passing libraries including Intel's NX Thinking Machines' CMMD, and PVM; (ii) Monitor, a library of timestamping and trace -collection routines that run on supercomputers (such as Intel's iPSC/860, Delta, and Paragon and Thinking Machines' CM5) as well as on networks of workstations (including Convex Cluster and SparcStations connected by a LAN); (iii) Visualization Kernel, a trace-animation facility that supports source-code clickback, simultaneous visualization of computation and communication patterns, as well as analysis of data movements; (iv) Statistics Kernel, an advanced profiling facility, that associates a variety of performance data with various syntactic components of a parallel program; (v) Index Kernel, a diagnostic tool that helps pinpoint performance bottlenecks through the use of abstract indices; (vi) Modeling Kernel, a facility for automated modeling of message-passing programs that supports both simulation -based and analytical approaches to performance prediction and scalability analysis; (vii) Intrusion Compensator, a utility for recovering true performance from observed performance by removing the overheads of monitoring and their effects on the communication pattern of the program; and (viii) Compatibility Tools, that convert AIMS-generated traces into formats used by other performance-visualization tools, such as ParaGraph, Pablo, and certain AVS/Explorer modules.

GPU-accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

NASA Astrophysics Data System (ADS)

Alvanos, Michail; Christoudias, Theodoros

2017-10-01

This paper presents an application of GPU accelerators in Earth system modeling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate-chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC), used to study climate change and air quality scenarios. A source-to-source compiler outputs a CUDA-compatible kernel by parsing the FORTRAN code generated by the Kinetic PreProcessor (KPP) general analysis tool. All Rosenbrock methods that are available in the KPP numerical library are supported.Performance evaluation, using Fermi and Pascal CUDA-enabled GPU accelerators, shows achieved speed-ups of 4. 5 × and 20. 4 × , respectively, of the kernel execution time. A node-to-node real-world production performance comparison shows a 1. 75 × speed-up over the non-accelerated application using the KPP three-stage Rosenbrock solver. We provide a detailed description of the code optimizations used to improve the performance including memory optimizations, control code simplification, and reduction of idle time. The accuracy and correctness of the accelerated implementation are evaluated by comparing to the CPU-only code of the application. The median relative difference is found to be less than 0.000000001 % when comparing the output of the accelerated kernel the CPU-only code.The approach followed, including the computational workload division, and the developed GPU solver code can potentially be used as the basis for hardware acceleration of numerous geoscientific models that rely on KPP for atmospheric chemical kinetics applications.

Association between cognitive impairments and obsessive-compulsive spectrum presentations following traumatic brain injury.

PubMed

Rydon-Grange, Michelle; Coetzer, Rudi

2017-01-02

This study examined the association between self-reported obsessive-compulsive spectrum symptomatology and cognitive performance in a sample of patients with traumatic brain injury (TBI). Twenty-four adults with a moderate-severe TBI accessing a community brain injury rehabilitation service were recruited. Age ranged between 19 and 69 years. Participants completed a battery of neuropsychological tasks assessing memory, executive functioning, and speed of information processing. Self-report questionnaires assessing obsessive-compulsive (OC) symptoms and obsessive-compulsive personality disorder (OCPD) traits were also completed. Correlational analyses revealed that deficits in cognitive flexibility were associated with greater self-reported OC symptomatology and severity. Greater OC symptom severity was significantly related to poorer performance on a visual memory task. Verbal memory and speed of information processing impairments were unrelated to OC symptoms. Performance on tasks of memory, executive functioning, and speed of information processing were not associated with OCPD traits. Overall, results indicate that greater OC symptomatology and severity were associated with specific neuropsychological functions (i.e., cognitive flexibility, visual memory). OCPD personality traits were unrelated to cognitive performance. Further research is needed to examine the potential causal relationship and longer-term interactions between cognitive sequelae and obsessive-compulsive spectrum presentations post-TBI.

The role of motor memory in action selection and procedural learning: insights from children with typical and atypical development.

PubMed

Tallet, Jessica; Albaret, Jean-Michel; Rivière, James

2015-01-01

Motor memory is the process by which humans can adopt both persistent and flexible motor behaviours. Persistence and flexibility can be assessed through the examination of the cooperation/competition between new and old motor routines in the motor memory repertoire. Two paradigms seem to be particularly relevant to examine this competition/cooperation. First, a manual search task for hidden objects, namely the C-not-B task, which allows examining how a motor routine may influence the selection of action in toddlers. The second paradigm is procedural learning, and more precisely the consolidation stage, which allows assessing how a previously learnt motor routine becomes resistant to subsequent programming or learning of a new - competitive - motor routine. The present article defends the idea that results of both paradigms give precious information to understand the evolution of motor routines in healthy children. Moreover, these findings echo some clinical observations in developmental neuropsychology, particularly in children with Developmental Coordination Disorder. Such studies suggest that the level of equilibrium between persistence and flexibility of motor routines is an index of the maturity of the motor system.

Memory effects for a stochastic fractional oscillator in a magnetic field

NASA Astrophysics Data System (ADS)

Mankin, Romi; Laas, Katrin; Laas, Tõnu; Paekivi, Sander

2018-01-01

The problem of random motion of harmonically trapped charged particles in a constant external magnetic field is studied. A generalized three-dimensional Langevin equation with a power-law memory kernel is used to model the interaction of Brownian particles with the complex structure of viscoelastic media (e.g., dusty plasmas). The influence of a fluctuating environment is modeled by an additive fractional Gaussian noise. In the long-time limit the exact expressions of the first-order and second-order moments of the fluctuating position for the Brownian particle subjected to an external periodic force in the plane perpendicular to the magnetic field have been calculated. Also, the particle's angular momentum is found. It is shown that an interplay of external periodic forcing, memory, and colored noise can generate a variety of cooperation effects, such as memory-induced sign reversals of the angular momentum, multiresonance versus Larmor frequency, and memory-induced particle confinement in the absence of an external trapping field. Particularly in the case without external trapping, if the memory exponent is lower than a critical value, we find a resonancelike behavior of the anisotropy in the particle position distribution versus the driving frequency, implying that it can be efficiently excited by an oscillating electric field. Similarities and differences between the behaviors of the models with internal and external noises are also discussed.

Speeding up 3D speckle tracking using PatchMatch

NASA Astrophysics Data System (ADS)

Zontak, Maria; O'Donnell, Matthew

2016-03-01

Echocardiography provides valuable information to diagnose heart dysfunction. A typical exam records several minutes of real-time cardiac images. To enable complete analysis of 3D cardiac strains, 4-D (3-D+t) echocardiography is used. This results in a huge dataset and requires effective automated analysis. Ultrasound speckle tracking is an effective method for tissue motion analysis. It involves correlation of a 3D kernel (block) around a voxel with kernels in later frames. The search region is usually confined to a local neighborhood, due to biomechanical and computational constraints. For high strains and moderate frame-rates, however, this search region will remain large, leading to a considerable computational burden. Moreover, speckle decorrelation (due to high strains) leads to errors in tracking. To solve this, spatial motion coherency between adjacent voxels should be imposed, e.g., by averaging their correlation functions.1 This requires storing correlation functions for neighboring voxels, thus increasing memory demands. In this work, we propose an efficient search using PatchMatch, 2 a powerful method to find correspondences between images. Here we adopt PatchMatch for 3D volumes and radio-frequency signals. As opposed to an exact search, PatchMatch performs random sampling of the search region and propagates successive matches among neighboring voxels. We show that: 1) Inherently smooth offset propagation in PatchMatch contributes to spatial motion coherence without any additional processing or memory demand. 2) For typical scenarios, PatchMatch is at least 20 times faster than the exact search, while maintaining comparable tracking accuracy.

An Efficient Implementation For Real Time Applications Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Black, Peter; Whitehouse, Harper J.

1986-03-01

The Wigner-Ville Distribution (WVD) is a valuable tool for time-frequency signal analysis. In order to implement the WVD in real time an efficient algorithm and architecture have been developed which may be implemented with commercial components. This algorithm successively computes the analytic signal corresponding to the input signal, forms a weighted kernel function and analyses the kernel via a Discrete Fourier Transform (DFT). To evaluate the analytic signal required by the algorithm it is shown that the time domain definition implemented as a finite impulse response (FIR) filter is practical and more efficient than the frequency domain definition of the analytic signal. The windowed resolution of the WVD in the frequency domain is shown to be similar to the resolution of a windowed Fourier Transform. A real time signal processsor has been designed for evaluation of the WVD analysis system. The system is easily paralleled and can be configured to meet a variety of frequency and time resolutions. The arithmetic unit is based on a pair of high speed VLSI floating-point multiplier and adder chips. Dual operand buses and an independent result bus maximize data transfer rates. The system is horizontally microprogrammed and utilizes a full instruction pipeline. Each microinstruction specifies two operand addresses, a result location, the type of arithmetic and the memory configuration. input and output is via shared memory blocks with front-end processors to handle data transfers during the non access periods of the analyzer.

On the adaptive flexibility of evaluative priming.

PubMed

Fiedler, Klaus; Bluemke, Matthias; Unkelbach, Christian

2011-05-01

If priming effects serve an adaptive function, they have to be both robust and flexible. In four experiments, we demonstrated regular evaluative-priming effects for relatively long stimulus-onset asynchronies, which can, however, be eliminated or reversed strategically. When participants responded to both primes and targets, rather than only to targets, the standard congruity effect disappeared. In Experiments 1a-1c, this result was regularly obtained, independently of the prime response (valence or gender classification) and the response mode (pronunciation or keystroke). In Experiment 2, we showed that once the default congruity effect was eliminated, strategic-priming effects reflected the statistical contingency between prime valence and target valence. Positive contingencies produced congruity, whereas negative contingencies produced equally strong incongruity effects. Altogether, these findings are consistent with an adaptive-cognitive perspective, which highlights the role of flexible strategic processes in working memory as opposed to fixed structures in semantic long-term memory or in the sensorimotor system.

Toward all-carbon electronics: fabrication of graphene-based flexible electronic circuits and memory cards using maskless laser direct writing.

PubMed

Liang, Jiajie; Chen, Yongsheng; Xu, Yanfei; Liu, Zhibo; Zhang, Long; Zhao, Xin; Zhang, Xiaoliang; Tian, Jianguo; Huang, Yi; Ma, Yanfeng; Li, Feifei

2010-11-01

Owing to its extraordinary electronic property, chemical stability, and unique two-dimensional nanostructure, graphene is being considered as an ideal material for the highly expected all-carbon-based micro/nanoscale electronics. Herein, we present a simple yet versatile approach to constructing all-carbon micro/nanoelectronics using solution-processing graphene films directly. From these graphene films, various graphene-based microcosmic patterns and structures have been fabricated using maskless computer-controlled laser cutting. Furthermore, a complete system involving a prototype of a flexible write-once-read-many-times memory card and a fast data-reading system has been demonstrated, with infinite data retention time and high reliability. These results indicate that graphene could be the ideal material for fabricating the highly demanded all-carbon and flexible devices and electronics using the simple and efficient roll-to-roll printing process when combined with maskless direct data writing.

Characteristics of Reduced Graphene Oxide Quantum Dots for a Flexible Memory Thin Film Transistor.

PubMed

Kim, Yo-Han; Lee, Eun Yeol; Lee, Hyun Ho; Seo, Tae Seok

2017-05-17

Reduced graphene oxide quantum dot (rGOQD) devices in formats of capacitor and thin film transistor (TFT) were demonstrated and examined as the first trial to achieve nonambipolar channel property. In addition, through a gold nanoparticle (Au NP) layer embedded between the rGOQD active channel and dielectric layer, memory capacitor and TFT performances were realized by capacitance-voltage (C-V) hysteresis and gate program, erase, and reprogram biases. First, capacitor structure of the rGOQD memory device was constructed to examine memory charging effect featured in hysteretic C-V behavior with a 30 nm dielectric layer of cross-linked poly(vinyl alcohol). For the intervening Au NP charging layer, self-assembled monolayer (SAM) formation of the Au NP was executed to utilize electrostatic interaction by a dip-coating process under ambient environments with a conformal fabrication uniformity. Second, the rGOQD memory TFT device was also constructed in the same format of the Au NPs SAMs on a flexible substrate. Characteristics of the rGOQD TFT output showed novel saturation curves unlike typical graphene-based TFTs. However, The rGOQD TFT device reveals relatively low on/off ratio of 10 1 and mobility of 5.005 cm 2 /V·s. For the memory capacitor, the flat-band voltage shift (ΔV FB ) was measured as 3.74 V for ±10 V sweep, and for the memory TFT, the threshold voltage shift (ΔV th ) by the Au NP charging was detected as 7.84 V. In summary, it was concluded that the rGOQD memory device could accomplish an ideal graphene-based memory performance, which could have provided a wide memory window and saturated output characteristics.

Scalable printed electronics: an organic decoder addressing ferroelectric non-volatile memory.

PubMed

Ng, Tse Nga; Schwartz, David E; Lavery, Leah L; Whiting, Gregory L; Russo, Beverly; Krusor, Brent; Veres, Janos; Bröms, Per; Herlogsson, Lars; Alam, Naveed; Hagel, Olle; Nilsson, Jakob; Karlsson, Christer

2012-01-01

Scalable circuits of organic logic and memory are realized using all-additive printing processes. A 3-bit organic complementary decoder is fabricated and used to read and write non-volatile, rewritable ferroelectric memory. The decoder-memory array is patterned by inkjet and gravure printing on flexible plastics. Simulation models for the organic transistors are developed, enabling circuit designs tolerant of the variations in printed devices. We explain the key design rules in fabrication of complex printed circuits and elucidate the performance requirements of materials and devices for reliable organic digital logic.

Flexibility within working memory and the focus of attention for sequential verbal information does not depend on active maintenance.

PubMed

Sandry, Joshua; Schwark, Jeremy D; MacDonald, Justin

2014-10-01

The focus of attention seems to be a static element within working memory when verbal information is serially presented, unless additional time is available for processing or active maintenance. Experiment 1 manipulated the reward associated with early and medial list positions in a probe recognition paradigm and found evidence that these nonterminal list positions could be retrieved faster and more accurately if participants were appropriately motivated-without additional time for processing or active maintenance. Experiment 2 used articulatory suppression and demonstrated that the underlying maintenance mechanism cannot be attributed to rehearsal, leaving attentional refreshing as the more likely mechanism. These findings suggest that the focus of attention within working memory can flexibly maintain nonterminal early and medial list representations at the expense of other list representations even when there is not additional time for processing or active maintenance. Maintenance seems to be accomplished through an attentional refreshing mechanism.

Language Mediated Concept Activation in Bilingual Memory Facilitates Cognitive Flexibility

PubMed Central

Kharkhurin, Anatoliy V.

2017-01-01

This is the first attempt of empirical investigation of language mediated concept activation (LMCA) in bilingual memory as a cognitive mechanism facilitating divergent thinking. Russian–English bilingual and Russian monolingual college students were tested on a battery of tests including among others Abbreviated Torrance Tests for Adults assessing divergent thinking traits and translingual priming (TLP) test assessing the LMCA. The latter was designed as a lexical decision priming test, in which a prime and a target were not related in Russian (language of testing), but were related through their translation equivalents in English (spoken only by bilinguals). Bilinguals outperformed their monolingual counterparts on divergent thinking trait of cognitive flexibility, and bilinguals’ performance on this trait could be explained by their TLP effect. Age of second language acquisition and proficiency in this language were found to relate to the TLP effect, and therefore were proposed to influence the directionality and strength of connections in bilingual memory. PMID:28701981

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

NASA Astrophysics Data System (ADS)

Mankin, Romi; Paekivi, Sander

2018-01-01

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent αc≈0.402 , which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing.

PubMed

Rajan, Krishna; Garofalo, Erik; Chiolerio, Alessandro

2018-01-27

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing

PubMed Central

Rajan, Krishna; Garofalo, Erik

2018-01-01

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC. PMID:29382050

Mental Fitness for Life: Assessing the Impact of an 8-Week Mental Fitness Program on Healthy Aging.

ERIC Educational Resources Information Center

Cusack, Sandra A.; Thompson, Wendy J. A.; Rogers, Mary E.

2003-01-01

A mental fitness program taught goal setting, critical thinking, creativity, positive attitudes, learning, memory, and self-expression to adults over 50 (n=22). Pre/posttests of depression and cognition revealed significant impacts on mental fitness, cognitive confidence, goal setting, optimism, creativity, flexibility, and memory. Not significant…

Neuroscientific Insights: Attention, Working Memory, and Inhibitory Control

ERIC Educational Resources Information Center

Raver, C. Cybele; Blair, Clancy

2016-01-01

In this article, Cybele Raver and Clancy Blair explore a group of cognitive processes called executive function (EF)--including the flexible control of attention, the ability to hold information through working memory, and the ability to maintain inhibitory control. EF processes are crucial for young children's learning. On the one hand, they can…

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

Lee, Seyong; Vetter, Jeffrey S

Computer architecture experts expect that non-volatile memory (NVM) hierarchies will play a more significant role in future systems including mobile, enterprise, and HPC architectures. With this expectation in mind, we present NVL-C: a novel programming system that facilitates the efficient and correct programming of NVM main memory systems. The NVL-C programming abstraction extends C with a small set of intuitive language features that target NVM main memory, and can be combined directly with traditional C memory model features for DRAM. We have designed these new features to enable compiler analyses and run-time checks that can improve performance and guard againstmore » a number of subtle programming errors, which, when left uncorrected, can corrupt NVM-stored data. Moreover, to enable recovery of data across application or system failures, these NVL-C features include a flexible directive for specifying NVM transactions. So that our implementation might be extended to other compiler front ends and languages, the majority of our compiler analyses are implemented in an extended version of LLVM's intermediate representation (LLVM IR). We evaluate NVL-C on a number of applications to show its flexibility, performance, and correctness.« less

Obesity and unhealthy lifestyle associated with poor executive function among Malaysian adolescents.

PubMed

Tee, Joyce Ying Hui; Gan, Wan Ying; Tan, Kit-Aun; Chin, Yit Siew

2018-01-01

The understanding on the roles of obesity and lifestyle behaviors in predicting executive function of adolescents has been limited. Low executive function proficiency may have adverse effects on adolescents' school academic performance. This cross-sectional study aimed to examine the relationship between BMI-for-age and multiple lifestyle behaviors (operationalized as meal consumption, physical activity, and sleep quality) with executive function (operationalized as inhibition, working memory, and cognitive flexibility) on a sample of Malaysian adolescents aged between 12 and 16 years (N = 513). Participants were recruited from two randomly selected schools in the state of Selangor in Malaysia. Using a self-administered questionnaire, parent participants provided information concerning their sociodemographic data, whereas adolescent participants provided information regarding their meal consumptions, physical activity, and sleep quality. The modified Harvard step test was used to assess adolescents' aerobic fitness, while Stroop color-word, digit span, and trail-making tests were used to assess adolescents' inhibition, working memory, and cognitive flexibility, respectively. Three separate hierarchical regression analyses were conducted for each outcome namely, inhibition, working memory, and cognitive flexibility. After adjusted for sociodemographic factors and BMI-for-age, differential predictors of inhibition and working memory were found. Habitual sleep efficiency significantly and positively predicted inhibition. Regular dinner intakes, physical activity levels, and sleep quality significantly and positively predicted working memory. Household income emerged as a consistent predictor for all executive function domains. In conclusion, an increased trend of obesity and unhealthy lifestyles among adolescents were found to be associated with poorer executive function. Regular dinner intakes, higher physical activity levels and better sleep quality predicted better executive function despite the inverse relationship between obesity and executive function. Future studies may explore how lifestyle modifications can optimize the development of executive function in adolescents as well as relieve the burden of obesity.

Flexible Peripheral Component Interconnect Input/Output Card

NASA Technical Reports Server (NTRS)

Bigelow, Kirk K.; Jerry, Albert L.; Baricio, Alisha G.; Cummings, Jon K.

2010-01-01

The Flexible Peripheral Component Interconnect (PCI) Input/Output (I/O) Card is an innovative circuit board that provides functionality to interface between a variety of devices. It supports user-defined interrupts for interface synchronization, tracks system faults and failures, and includes checksum and parity evaluation of interface data. The card supports up to 16 channels of high-speed, half-duplex, low-voltage digital signaling (LVDS) serial data, and can interface combinations of serial and parallel devices. Placement of a processor within the field programmable gate array (FPGA) controls an embedded application with links to host memory over its PCI bus. The FPGA also provides protocol stacking and quick digital signal processor (DSP) functions to improve host performance. Hardware timers, counters, state machines, and other glue logic support interface communications. The Flexible PCI I/O Card provides an interface for a variety of dissimilar computer systems, featuring direct memory access functionality. The card has the following attributes: 8/16/32-bit, 33-MHz PCI r2.2 compliance, Configurable for universal 3.3V/5V interface slots, PCI interface based on PLX Technology's PCI9056 ASIC, General-use 512K 16 SDRAM memory, General-use 1M 16 Flash memory, FPGA with 3K to 56K logical cells with embedded 27K to 198K bits RAM, I/O interface: 32-channel LVDS differential transceivers configured in eight, 4-bit banks; signaling rates to 200 MHz per channel, Common SCSI-3, 68-pin interface connector.

Time-Dependent Effects of Acute Exercise on University Students’ Cognitive Performance in Temperate and Cold Environments

PubMed Central

Ji, Ling-Yu; Li, Xiao-Ling; Liu, Yang; Sun, Xiu-Wen; Wang, Hui-Fen; Chen, Long; Gao, Liang

2017-01-01

Background: Few studies have examined the acute exercise-induced changes in cognitive performance in different thermal environments and the time course effects. Objective: Investigate the time-dependent effects of acute exercise on university students’ processing speed, working memory and cognitive flexibility in temperate and cold environments. Method: Twenty male university students (age 23.5 ± 2.0 years) with moderate physical activity level participated in a repeated-measures within-subjects design. Processing speed, working memory and cognitive flexibility were assessed using CogState test battery at baseline (BASE), followed by a 45-min rest (REST), immediately after (EX) and 30 min after (POST-EX) 30-min moderate-intensity treadmill running in both temperate (TEMP; 25°C) and cold (COLD; 10°C) environments. Mean skin temperature (MST) and thermal sensation (TS) were also recorded. Two-way repeated measures ANOVA was performed to analyze each variable. Spearman’s rho was used to identify the correlations between MST, TS and cognitive performance. Results: Reaction time (RT) of processing speed and working memory decreased immediately after exercise in both conditions (processing speed: p = 0.003; working memory: p = 0.007). The facilitating effects on processing speed disappeared within 30 min after exercise in TEMP (p = 0.163) and COLD (p = 0.667), while improvements on working memory remained 30 min after exercise in TEMP (p = 0.047), but not in COLD (p = 0.663). Though RT of cognitive flexibility reduced in both conditions (p = 0.003), no significance was found between EX and REST (p = 0.135). Increased MST and TS were significantly associated with reductions in processing speed RT (MST: r = -0.341, p < 0.001; TS: r = -0.262, p = 0.001) and working memory RT (MST: r = -0.282, p < 0.001; TS: r = -0.2229, p = 0.005), and improvements in working memory accuracy (MST: r = 0.249, p = 0.002; TS: r = 0.255, p = 0.001). Conclusion: The results demonstrate different time-dependent effects of acute exercise on cognition in TEMP and COLD. Our study reveals facilitating effects of exercise on university students’ processing speed and working memory in both environments. However, in contrast to TEMP, effects on working memory in COLD are transient. PMID:28747896

Memory for reputational trait information: is social-emotional information processing less flexible in old age?

PubMed

Bell, Raoul; Giang, Trang; Mund, Iris; Buchner, Axel

2013-12-01

How do younger and older adults remember reputational trait information about other people? In the present study, trustworthy-looking and untrustworthy-looking faces were paired with cooperation or cheating in a cooperation game. In a surprise source-memory test, participants were asked to rate the likability of the faces, and were required to remember whether the faces were associated with negative or positive outcomes. The social expectations of younger and older adults were clearly affected by a priori facial trustworthiness. Facial trustworthiness was associated with high cooperation-game investments, high likability ratings, and a tendency toward guessing that a face belonged to a cooperator instead of a cheater in both age groups. Consistent with previous results showing that emotional memory is spared from age-related decline, memory for the association between faces and emotional reputational information was well preserved in older adults. However, younger adults used a flexible encoding strategy to remember the social interaction partners. Source-memory was best for information that violated their (positive) expectations. Older adults, in contrast, showed a uniform memory bias for negative social information; their memory performance was not modulated by their expectations. This finding suggests that older adults are less likely to adjust their encoding strategies to their social expectations than younger adults. This may be in line with older adults' motivational goals to avoid risks in social interactions. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Torsion and bending properties of shape memory and superelastic nickel-titanium rotary instruments.

PubMed

Ninan, Elizabeth; Berzins, David W

2013-01-01

Recently introduced into the market are shape memory nickel-titanium (NiTi) rotary files. The objective of this study was to investigate the torsion and bending properties of shape memory files (CM Wire, HyFlex CM, and Phoenix Flex) and compare them with conventional (ProFile ISO and K3) and M-Wire (GT Series X and ProFile Vortex) NiTi files. Sizes 20, 30, and 40 (n = 12/size/taper) of 0.02 taper CM Wire, Phoenix Flex, K3, and ProFile ISO and 0.04 taper HyFlex CM, ProFile ISO, GT Series X, and Vortex were tested in torsion and bending per ISO 3630-1 guidelines by using a torsiometer. All data were statistically analyzed by analysis of variance and the Tukey-Kramer test (P = .05) to determine any significant differences between the files. Significant interactions were present among factors of size and file. Variability in maximum torque values was noted among the shape memory files brands, sometimes exhibiting the greatest or least torque depending on brand, size, and taper. In general, the shape memory files showed a high angle of rotation before fracture but were not statistically different from some of the other files. However, the shape memory files were more flexible, as evidenced by significantly lower bending moments (P < .008). Shape memory files show greater flexibility compared with several other NiTi rotary file brands. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

A High-Performance Optical Memory Array Based on Inhomogeneity of Organic Semiconductors.

PubMed

Pei, Ke; Ren, Xiaochen; Zhou, Zhiwen; Zhang, Zhichao; Ji, Xudong; Chan, Paddy Kwok Leung

2018-03-01

Organic optical memory devices keep attracting intensive interests for diverse optoelectronic applications including optical sensors and memories. Here, flexible nonvolatile optical memory devices are developed based on the bis[1]benzothieno[2,3-d;2',3'-d']naphtho[2,3-b;6,7-b']dithiophene (BBTNDT) organic field-effect transistors with charge trapping centers induced by the inhomogeneity (nanosprouts) of the organic thin film. The devices exhibit average mobility as high as 7.7 cm 2 V -1 s -1 , photoresponsivity of 433 A W -1 , and long retention time for more than 6 h with a current ratio larger than 10 6 . Compared with the standard floating gate memory transistors, the BBTNDT devices can reduce the fabrication complexity, cost, and time. Based on the reasonable performance of the single device on a rigid substrate, the optical memory transistor is further scaled up to a 16 × 16 active matrix array on a flexible substrate with operating voltage less than 3 V, and it is used to map out 2D optical images. The findings reveal the potentials of utilizing [1]benzothieno[3,2-b][1]benzothiophene (BTBT) derivatives as organic semiconductors for high-performance optical memory transistors with a facile structure. A detailed study on the charge trapping mechanism in the derivatives of BTBT materials is also provided, which is closely related to the nanosprouts formed inside the organic active layer. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The neurobiology of the human memory.

PubMed

Fietta, Pierluigi; Fietta, Pieranna

2011-01-01

Memory can be defined as the ability to acquire, process, store, and retrieve information. Memory is indispensable for learning, adaptation, and survival of every living organism. In humans, the remembering process has acquired great flexibility and complexity, reaching close links with other mental functions, such as thinking and emotions. Changes in synaptic connectivity and interactions among multiple neural networks provide the neurobiological substrates for memory encoding, retention, and consolidation. Memory may be categorized as short-term and long-term memory (according to the storage temporal duration), as implicit and explicit memory (with respect to the consciousness of remembering), as declarative (knowing that [fact]) and procedural (knowing how [skill]) memory, or as sensory (echoic, iconic and haptil), semantic, and episodic memory (according to the various remembering domains). Significant advances have been obtained in understanding memory neurobiology, but much remains to be learned in its cognitive, psychological, and phenomenological aspects.

Head west or left, east or right: interactions between memory systems in neurocognitive aging

PubMed Central

Pereira, Inês Tomás; Gallagher, Michela; Rapp, Peter R.

2018-01-01

Cognitive aging is accompanied by decline in multiple domains of memory. Here, we developed a T-maze task that required rats to learn competing hippocampal, and striatal navigation strategies in succession, across days. A final session increased demands on cognitive flexibility and required within-day switching between strategies, emphasizing capacities that engage the prefrontal cortex. Background characterization in young and aged rats used a water maze protocol optimized for individual differences in hippocampal integrity. Consistent with earlier work, young adults acquired place strategies in the T-maze faster than response, whereas the opposite was observed in aged rats with impaired spatial memory. The novel result was that aged animals with preserved spatial memory displayed a qualitatively distinct pattern, acquiring place and response strategies equally rapidly, without disruption when switching between them. Subsequent in situ hybridization for the plasticity-related immediate-early gene Arc revealed that while increasing demands on cognitive flexibility and within-day strategy switching potently engaged the prefrontal cortex in young adult and aged-impaired rats, Arc expression was insensitive in aged rats with normal spatial memory and superior switching abilities. Together, the results indicate that cognitive aging is an emergent property of the interactions between memory systems, and that successful cognitive outcomes reflect a distinct neuroadaptive process rather than a slower rate of aging. PMID:26281759

Flexible ferroelectric element based on van der Waals heteroepitaxy.

PubMed

Jiang, Jie; Bitla, Yugandhar; Huang, Chun-Wei; Do, Thi Hien; Liu, Heng-Jui; Hsieh, Ying-Hui; Ma, Chun-Hao; Jang, Chi-Yuan; Lai, Yu-Hong; Chiu, Po-Wen; Wu, Wen-Wei; Chen, Yi-Chun; Zhou, Yi-Chun; Chu, Ying-Hao

2017-06-01

We present a promising technology for nonvolatile flexible electronic devices: A direct fabrication of epitaxial lead zirconium titanate (PZT) on flexible mica substrate via van der Waals epitaxy. These single-crystalline flexible ferroelectric PZT films not only retain their performance, reliability, and thermal stability comparable to those on rigid counterparts in tests of nonvolatile memory elements but also exhibit remarkable mechanical properties with robust operation in bent states (bending radii down to 2.5 mm) and cycling tests (1000 times). This study marks the technological advancement toward realizing much-awaited flexible yet single-crystalline nonvolatile electronic devices for the design and development of flexible, lightweight, and next-generation smart devices with potential applications in electronics, robotics, automotive, health care, industrial, and military systems.

Flexible ferroelectric element based on van der Waals heteroepitaxy

PubMed Central

Jiang, Jie; Bitla, Yugandhar; Huang, Chun-Wei; Do, Thi Hien; Liu, Heng-Jui; Hsieh, Ying-Hui; Ma, Chun-Hao; Jang, Chi-Yuan; Lai, Yu-Hong; Chiu, Po-Wen; Wu, Wen-Wei; Chen, Yi-Chun; Zhou, Yi-Chun; Chu, Ying-Hao

2017-01-01

We present a promising technology for nonvolatile flexible electronic devices: A direct fabrication of epitaxial lead zirconium titanate (PZT) on flexible mica substrate via van der Waals epitaxy. These single-crystalline flexible ferroelectric PZT films not only retain their performance, reliability, and thermal stability comparable to those on rigid counterparts in tests of nonvolatile memory elements but also exhibit remarkable mechanical properties with robust operation in bent states (bending radii down to 2.5 mm) and cycling tests (1000 times). This study marks the technological advancement toward realizing much-awaited flexible yet single-crystalline nonvolatile electronic devices for the design and development of flexible, lightweight, and next-generation smart devices with potential applications in electronics, robotics, automotive, health care, industrial, and military systems. PMID:28630922

Fractional Brownian motors and stochastic resonance

NASA Astrophysics Data System (ADS)

Goychuk, Igor; Kharchenko, Vasyl

2012-05-01

We study fluctuating tilt Brownian ratchets based on fractional subdiffusion in sticky viscoelastic media characterized by a power law memory kernel. Unlike the normal diffusion case, the rectification effect vanishes in the adiabatically slow modulation limit and optimizes in a driving frequency range. It is shown also that the anomalous rectification effect is maximal (stochastic resonance effect) at optimal temperature and can be of surprisingly good quality. Moreover, subdiffusive current can flow in the counterintuitive direction upon a change of temperature or driving frequency. The dependence of anomalous transport on load exhibits a remarkably simple universality.

Memory-less response and violation of the fluctuation-dissipation theorem in colloids suspended in an active bath.

PubMed

Maggi, Claudio; Paoluzzi, Matteo; Angelani, Luca; Di Leonardo, Roberto

2017-12-14

We investigate experimentally and numerically the stochastic dynamics and the time-dependent response of colloids subject to a small external perturbation in a dense bath of motile E. coli bacteria. The external field is a magnetic field acting on a superparamagnetic microbead suspended in an active medium. The measured linear response reveals an instantaneous friction kernel despite the complexity of the bacterial bath. By comparing the mean squared displacement and the response function we detect a clear violation of the fluctuation dissipation theorem.

Theory of earthquakes interevent times applied to financial markets

NASA Astrophysics Data System (ADS)

Jagielski, Maciej; Kutner, Ryszard; Sornette, Didier

2017-10-01

We analyze the probability density function (PDF) of waiting times between financial loss exceedances. The empirical PDFs are fitted with the self-excited Hawkes conditional Poisson process with a long power law memory kernel. The Hawkes process is the simplest extension of the Poisson process that takes into account how past events influence the occurrence of future events. By analyzing the empirical data for 15 different financial assets, we show that the formalism of the Hawkes process used for earthquakes can successfully model the PDF of interevent times between successive market losses.

Memory and executive functions in persons with type 2 diabetes: a meta-analysis.

PubMed

Sadanand, Shilpa; Balachandar, Rakesh; Bharath, Srikala

2016-02-01

Literature suggests that persons with type 2 diabetes mellitus (T2DM) are at risk for cognitive impairment, hence dementia. Common domains reported to be affected in those with T2DM are memory and executive functions. The extent of influence of T2DM on these domains has varied among studies. A systematic review and meta-analysis was carried out to understand whether sub-domains contributed to the variations observed in published research. We searched 'PubMed', 'ScienceDirect', 'SciVerseHub', 'Psychinfo', 'Proquest' 'Ebsco' and 'J-gate Plus' databases for published studies on cognition and T2DM among persons aged 50 years and older. Memory, executive functions and processing speed domain and sub-domain scores were extracted; effect sizes (Cohen's d) were calculated and analysed. Eight hundred seventeen articles were found. After various levels of filtering, 15 articles met the inclusion criteria for quantitative analyses. The analyses indicated that in comparison to controls, persons with T2DM showed decrements in episodic memory (d = -0.51), logical memory (d = -0.24), sub-domain of executive functions which included phonemic fluency (d = -0.35) and cognitive flexibility (d = 0.52), and speed of processing (d = -0.22). We found no difference in the sub-domains of verbal short-term memory and working memory. The meta-analysis revealed a detrimental effect of T2DM on cognitive sub-domains, namely, episodic memory and cognitive flexibility. There was a trend for the logical memory, phonemic fluency and processing speed to be affected. The analysis indicates that T2DM is a detrimental factor on certain cognitive sub-domains, rendering the person vulnerable to subsequent dementia. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Memory of occasional events in rats: individual episodic memory profiles, flexibility, and neural substrate.

PubMed

Veyrac, Alexandra; Allerborn, Marina; Gros, Alexandra; Michon, Frederic; Raguet, Louise; Kenney, Jana; Godinot, Florette; Thevenet, Marc; Garcia, Samuel; Messaoudi, Belkacem; Laroche, Serge; Ravel, Nadine

2015-05-13

In search for the mechanisms underlying complex forms of human memory, such as episodic recollection, a primary challenge is to develop adequate animal models amenable to neurobiological investigation. Here, we proposed a novel framework and paradigm that provides means to quantitatively evaluate the ability of rats to form and recollect a combined knowledge of what happened, where it happened, and when or in which context it happened (referred to as episodic-like memory) after a few specific episodes in situations as close as possible to a paradigm we recently developed to study episodic memory in humans. In this task, rats have to remember two odor-drink associations (what happened) encountered in distinct locations (where it happened) within two different multisensory enriched environments (in which context/occasion it happened), each characterized by a particular combination of odors and places. By analyzing licking behavior on each drinking port, we characterized quantitatively individual recollection profiles and showed that rats are able to incidentally form and recollect an accurate, long-term integrated episodic-like memory that can last ≥ 24 d after limited exposure to the episodes. Placing rats in a contextually challenging recollection situation at recall reveals the ability for flexible use of episodic memory as described in humans. We further report that reversible inactivation of the dorsal hippocampus during recall disrupts the animal's capacity to recollect the complete episodic memory. Cellular imaging of c-Fos and Zif268 brain activation reveals that episodic memory recollection recruits a specific, distributed network of hippocampal-prefrontal cortex structures that correlates with the accuracy of the integrated recollection performance. Copyright © 2015 the authors 0270-6474/15/337575-12$15.00/0.

Individual Differences in Cognitive-Flexibility: The Influence of Spontaneous Eyeblink Rate, Trait Psychoticism and Working Memory on Attentional Set-Shifting

ERIC Educational Resources Information Center

Tharp, Ian J.; Pickering, Alan D.

2011-01-01

Individual differences in psychophysiological function have been shown to influence the balance between flexibility and distractibility during attentional set-shifting [e.g., Dreisbach et al. (2005). Dopamine and cognitive control: The influence of spontaneous eyeblink rate and dopamine gene polymorphisms on perseveration and distractibility.…

A Program Structure for Event-Based Speech Synthesis by Rules within a Flexible Segmental Framework.

ERIC Educational Resources Information Center

Hill, David R.

1978-01-01

A program structure based on recently developed techniques for operating system simulation has the required flexibility for use as a speech synthesis algorithm research framework. This program makes synthesis possible with less rigid time and frequency-component structure than simpler schemes. It also meets real-time operation and memory-size…

Horsetail matching: a flexible approach to optimization under uncertainty

NASA Astrophysics Data System (ADS)

Cook, L. W.; Jarrett, J. P.

2018-04-01

It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design's cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.

Implementing real-time robotic systems using CHIMERA II

NASA Technical Reports Server (NTRS)

Stewart, David B.; Schmitz, Donald E.; Khosla, Pradeep K.

1990-01-01

A description is given of the CHIMERA II programming environment and operating system, which was developed for implementing real-time robotic systems. Sensor-based robotic systems contain both general- and special-purpose hardware, and thus the development of applications tends to be a time-consuming task. The CHIMERA II environment is designed to reduce the development time by providing a convenient software interface between the hardware and the user. CHIMERA II supports flexible hardware configurations which are based on one or more VME-backplanes. All communication across multiple processors is transparent to the user through an extensive set of interprocessor communication primitives. CHIMERA II also provides a high-performance real-time kernel which supports both deadline and highest-priority-first scheduling. The flexibility of CHIMERA II allows hierarchical models for robot control, such as NASREM, to be implemented with minimal programming time and effort.

Orbital prefrontal cortex is required for object-in-place scene memory but not performance of a strategy implementation task.

PubMed

Baxter, Mark G; Gaffan, David; Kyriazis, Diana A; Mitchell, Anna S

2007-10-17

The orbital prefrontal cortex is thought to be involved in behavioral flexibility in primates, and human neuroimaging studies have identified orbital prefrontal activation during episodic memory encoding. The goal of the present study was to ascertain whether deficits in strategy implementation and episodic memory that occur after ablation of the entire prefrontal cortex can be ascribed to damage to the orbital prefrontal cortex. Rhesus monkeys were preoperatively trained on two behavioral tasks, the performance of both of which is severely impaired by the disconnection of frontal cortex from inferotemporal cortex. In the strategy implementation task, monkeys were required to learn about two categories of objects, each associated with a different strategy that had to be performed to obtain food reward. The different strategies had to be applied flexibly to optimize the rate of reward delivery. In the scene memory task, monkeys learned 20 new object-in-place discrimination problems in each session. Monkeys were tested on both tasks before and after bilateral ablation of orbital prefrontal cortex. These lesions impaired new scene learning but had no effect on strategy implementation. This finding supports a role for the orbital prefrontal cortex in memory but places limits on the involvement of orbital prefrontal cortex in the representation and implementation of behavioral goals and strategies.

The Emergence of Flexible Spatial Strategies in Young Children

ERIC Educational Resources Information Center

Waismeyer, Anna S.; Jacobs, Lucia F.

2013-01-01

The development of spatial navigation in children depends not only on remembering which landmarks lead to a goal location but also on developing strategies to deal with changes in the environment or imperfections in memory. Using cue combination methods, the authors examined 3- and 4-year-old children's memory for different types of spatial cues…

Scalable printed electronics: an organic decoder addressing ferroelectric non-volatile memory

PubMed Central

Ng, Tse Nga; Schwartz, David E.; Lavery, Leah L.; Whiting, Gregory L.; Russo, Beverly; Krusor, Brent; Veres, Janos; Bröms, Per; Herlogsson, Lars; Alam, Naveed; Hagel, Olle; Nilsson, Jakob; Karlsson, Christer

2012-01-01

Scalable circuits of organic logic and memory are realized using all-additive printing processes. A 3-bit organic complementary decoder is fabricated and used to read and write non-volatile, rewritable ferroelectric memory. The decoder-memory array is patterned by inkjet and gravure printing on flexible plastics. Simulation models for the organic transistors are developed, enabling circuit designs tolerant of the variations in printed devices. We explain the key design rules in fabrication of complex printed circuits and elucidate the performance requirements of materials and devices for reliable organic digital logic. PMID:22900143

Printed dose-recording tag based on organic complementary circuits and ferroelectric nonvolatile memories

PubMed Central

Nga Ng, Tse; Schwartz, David E.; Mei, Ping; Krusor, Brent; Kor, Sivkheng; Veres, Janos; Bröms, Per; Eriksson, Torbjörn; Wang, Yong; Hagel, Olle; Karlsson, Christer

2015-01-01

We have demonstrated a printed electronic tag that monitors time-integrated sensor signals and writes to nonvolatile memories for later readout. The tag is additively fabricated on flexible plastic foil and comprises a thermistor divider, complementary organic circuits, and two nonvolatile memory cells. With a supply voltage below 30 V, the threshold temperatures can be tuned between 0 °C and 80 °C. The time-temperature dose measurement is calibrated for minute-scale integration. The two memory bits are sequentially written in a thermometer code to provide an accumulated dose record. PMID:26307438

Multimodal properties and dynamics of gradient echo quantum memory.

PubMed

Hétet, G; Longdell, J J; Sellars, M J; Lam, P K; Buchler, B C

2008-11-14

We investigate the properties of a recently proposed gradient echo memory (GEM) scheme for information mapping between optical and atomic systems. We show that GEM can be described by the dynamic formation of polaritons in k space. This picture highlights the flexibility and robustness with regards to the external control of the storage process. Our results also show that, as GEM is a frequency-encoding memory, it can accurately preserve the shape of signals that have large time-bandwidth products, even at moderate optical depths. At higher optical depths, we show that GEM is a high fidelity multimode quantum memory.

Does believing in "use it or lose it" relate to self-rated memory control, strategy use, and recall?

PubMed

Hertzog, Christopher; McGuire, Christy L; Horhota, Michelle; Jopp, Daniela

2010-01-01

After an oral free recall task, participants were interviewed about their memory. Despite reporting similar levels of perceived personal control over memory, older and young adults differed in the means in which they believed memory could be controlled. Older adults cited health and wellness practices and exercising memory, consistent with a "use it or lose it" belief system, more often than young adults who were more likely to mention metacognition and flexible strategy use as means of memory control. Young adults reported using more effective relational strategies during study for a free recall test. Use of relational strategies predicted recall in both age groups, but did not materially affect age differences in performance. Metacognitive beliefs, including implicit theories about aging and memory decline, memory self-concept, and perceived control over memory functioning, did not systematically correlate with strategy use or recall.

CALHM1 deficiency impairs cerebral neuron activity and memory flexibility in mice.

PubMed

Vingtdeux, Valérie; Chang, Eric H; Frattini, Stephen A; Zhao, Haitian; Chandakkar, Pallavi; Adrien, Leslie; Strohl, Joshua J; Gibson, Elizabeth L; Ohmoto, Makoto; Matsumoto, Ichiro; Huerta, Patricio T; Marambaud, Philippe

2016-04-12

CALHM1 is a cell surface calcium channel expressed in cerebral neurons. CALHM1 function in the brain remains unknown, but recent results showed that neuronal CALHM1 controls intracellular calcium signaling and cell excitability, two mechanisms required for synaptic function. Here, we describe the generation of Calhm1 knockout (Calhm1(-/-)) mice and investigate CALHM1 role in neuronal and cognitive functions. Structural analysis revealed that Calhm1(-/-) brains had normal regional and cellular architecture, and showed no evidence of neuronal or synaptic loss, indicating that CALHM1 deficiency does not affect brain development or brain integrity in adulthood. However, Calhm1(-/-) mice showed a severe impairment in memory flexibility, assessed in the Morris water maze, and a significant disruption of long-term potentiation without alteration of long-term depression, measured in ex vivo hippocampal slices. Importantly, in primary neurons and hippocampal slices, CALHM1 activation facilitated the phosphorylation of NMDA and AMPA receptors by protein kinase A. Furthermore, neuronal CALHM1 activation potentiated the effect of glutamate on the expression of c-Fos and C/EBPβ, two immediate-early gene markers of neuronal activity. Thus, CALHM1 controls synaptic activity in cerebral neurons and is required for the flexible processing of memory in mice. These results shed light on CALHM1 physiology in the mammalian brain.

Behavioral and Neural Markers of Flexible Attention over Working Memory in Aging.

PubMed

Mok, Robert M; Myers, Nicholas E; Wallis, George; Nobre, Anna Christina

2016-04-01

Working memory (WM) declines as we age and, because of its fundamental role in higher order cognition, this can have highly deleterious effects in daily life. We investigated whether older individuals benefit from flexible orienting of attention within WM to mitigate cognitive decline. We measured magnetoencephalography (MEG) in older adults performing a WM precision task with cues during the maintenance period that retroactively predicted the location of the relevant items for performance (retro-cues). WM performance of older adults significantly benefitted from retro-cues. Whereas WM maintenance declined with age, retro-cues conferred strong attentional benefits. A model-based analysis revealed an increase in the probability of recalling the target, a lowered probability of retrieving incorrect items or guessing, and an improvement in memory precision. MEG recordings showed that retro-cues induced a transient lateralization of alpha (8-14 Hz) and beta (15-30 Hz) oscillatory power. Interestingly, shorter durations of alpha/beta lateralization following retro-cues predicted larger cueing benefits, reinforcing recent ideas about the dynamic nature of access to WM representations. Our results suggest that older adults retain flexible control over WM, but individual differences in control correspond to differences in neural dynamics, possibly reflecting the degree of preservation of control in healthy aging. © The Author 2016. Published by Oxford University Press.

Behavioral and Neural Markers of Flexible Attention over Working Memory in Aging

PubMed Central

Mok, Robert M.; Myers, Nicholas E.; Wallis, George; Nobre, Anna Christina

2016-01-01

Working memory (WM) declines as we age and, because of its fundamental role in higher order cognition, this can have highly deleterious effects in daily life. We investigated whether older individuals benefit from flexible orienting of attention within WM to mitigate cognitive decline. We measured magnetoencephalography (MEG) in older adults performing a WM precision task with cues during the maintenance period that retroactively predicted the location of the relevant items for performance (retro-cues). WM performance of older adults significantly benefitted from retro-cues. Whereas WM maintenance declined with age, retro-cues conferred strong attentional benefits. A model-based analysis revealed an increase in the probability of recalling the target, a lowered probability of retrieving incorrect items or guessing, and an improvement in memory precision. MEG recordings showed that retro-cues induced a transient lateralization of alpha (8–14 Hz) and beta (15–30 Hz) oscillatory power. Interestingly, shorter durations of alpha/beta lateralization following retro-cues predicted larger cueing benefits, reinforcing recent ideas about the dynamic nature of access to WM representations. Our results suggest that older adults retain flexible control over WM, but individual differences in control correspond to differences in neural dynamics, possibly reflecting the degree of preservation of control in healthy aging. PMID:26865653

Foundations of mathematics and literacy: The role of executive functioning components.

PubMed

Purpura, David J; Schmitt, Sara A; Ganley, Colleen M

2017-01-01

The current study investigated the relations between the three cognitive processes that comprise executive functioning (EF)-response inhibition, working memory, and cognitive flexibility-and individual components of mathematics and literacy skills in preschool children. Participants were 125 preschool children ranging in age from 3.12 to 5.26years (M=4.17years, SD=0.58). Approximately 53.2% were female, and the sample was predominantly Caucasian (69.8%). Results suggest that the components of EF may be differentially related to the specific components of early mathematics and literacy. For mathematics, response inhibition was broadly related to most components. Working memory was related to more advanced mathematics skills that involve comparison or combination of numbers and quantities. Cognitive flexibility was related to more conceptual or abstract mathematics skills. For early literacy, response inhibition and cognitive flexibility were related to print knowledge, and working memory was related only to phonological awareness. None of the EF components was related to vocabulary. These findings provide initial evidence for better understanding the ways in which EF components and academic skills are related and measured. Furthermore, the findings provide a foundation for further study of the components of each domain using a broader and more diverse array of measures. Copyright © 2016 Elsevier Inc. All rights reserved.

CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE

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

Czekala, Ian; Andrews, Sean M.; Mandel, Kaisey S.

2015-10-20

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically addresses the common problem of mismatches in model spectralmore » line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line “outliers.” By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high-resolution V-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate-resolution K-band spectrum of Gliese 51, an M5 field dwarf.« less

ASKI: A modular toolbox for scattering-integral-based seismic full waveform inversion and sensitivity analysis utilizing external forward codes

NASA Astrophysics Data System (ADS)

Schumacher, Florian; Friederich, Wolfgang

Due to increasing computational resources, the development of new numerically demanding methods and software for imaging Earth's interior remains of high interest in Earth sciences. Here, we give a description from a user's and programmer's perspective of the highly modular, flexible and extendable software package ASKI-Analysis of Sensitivity and Kernel Inversion-recently developed for iterative scattering-integral-based seismic full waveform inversion. In ASKI, the three fundamental steps of solving the seismic forward problem, computing waveform sensitivity kernels and deriving a model update are solved by independent software programs that interact via file output/input only. Furthermore, the spatial discretizations of the model space used for solving the seismic forward problem and for deriving model updates, respectively, are kept completely independent. For this reason, ASKI does not contain a specific forward solver but instead provides a general interface to established community wave propagation codes. Moreover, the third fundamental step of deriving a model update can be repeated at relatively low costs applying different kinds of model regularization or re-selecting/weighting the inverted dataset without need to re-solve the forward problem or re-compute the kernels. Additionally, ASKI offers the user sensitivity and resolution analysis tools based on the full sensitivity matrix and allows to compose customized workflows in a consistent computational environment. ASKI is written in modern Fortran and Python, it is well documented and freely available under terms of the GNU General Public License (http://www.rub.de/aski).

Quantification and classification of neuronal responses in kernel-smoothed peristimulus time histograms

PubMed Central

Fried, Itzhak; Koch, Christof

2014-01-01

Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development. PMID:25475352

An Adaptive Memory Interface Controller for Improving Bandwidth Utilization of Hybrid and Reconfigurable Systems

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

Castellana, Vito G.; Tumeo, Antonino; Ferrandi, Fabrizio

Emerging applications such as data mining, bioinformatics, knowledge discovery, social network analysis are irregular. They use data structures based on pointers or linked lists, such as graphs, unbalanced trees or unstructures grids, which generates unpredictable memory accesses. These data structures usually are large, but difficult to partition. These applications mostly are memory bandwidth bounded and have high synchronization intensity. However, they also have large amounts of inherent dynamic parallelism, because they potentially perform a task for each one of the element they are exploring. Several efforts are looking at accelerating these applications on hybrid architectures, which integrate general purpose processorsmore » with reconfigurable devices. Some solutions, which demonstrated significant speedups, include custom-hand tuned accelerators or even full processor architectures on the reconfigurable logic. In this paper we present an approach for the automatic synthesis of accelerators from C, targeted at irregular applications. In contrast to typical High Level Synthesis paradigms, which construct a centralized Finite State Machine, our approach generates dynamically scheduled hardware components. While parallelism exploitation in typical HLS-generated accelerators is usually bound within a single execution flow, our solution allows concurrently running multiple execution flow, thus also exploiting the coarser grain task parallelism of irregular applications. Our approach supports multiple, multi-ported and distributed memories, and atomic memory operations. Its main objective is parallelizing as many memory operations as possible, independently from their execution time, to maximize the memory bandwidth utilization. This significantly differs from current HLS flows, which usually consider a single memory port and require precise scheduling of memory operations. A key innovation of our approach is the generation of a memory interface controller, which dynamically maps concurrent memory accesses to multiple ports. We present a case study on a typical irregular kernel, Graph Breadth First search (BFS), exploring different tradeoffs in terms of parallelism and number of memories.« less

Flexible crossbar-structured resistive memory arrays on plastic substrates via inorganic-based laser lift-off.

PubMed

Kim, Seungjun; Son, Jung Hwan; Lee, Seung Hyun; You, Byoung Kuk; Park, Kwi-Il; Lee, Hwan Keon; Byun, Myunghwan; Lee, Keon Jae

2014-11-26

Crossbar-structured memory comprising 32 × 32 arrays with one selector-one resistor (1S-1R) components are initially fabricated on a rigid substrate. They are transferred without mechanical damage via an inorganic-based laser lift-off (ILLO) process as a result of laser-material interaction. Addressing tests of the transferred memory arrays are successfully performed to verify mitigation of cross-talk on a plastic substrate. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ultra-Lightweight Resistive Switching Memory Devices Based on Silk Fibroin.

PubMed

Wang, Hong; Zhu, Bowen; Wang, Hua; Ma, Xiaohua; Hao, Yue; Chen, Xiaodong

2016-07-01

Ultra-lightweight resistive switching memory based on protein has been demonstrated. The memory foil is 0.4 mg cm(-2) , which is 320-fold lighter than silicon substrate, 20-fold lighter than office paper and can be sustained by a human hair. Additionally, high resistance OFF/ON ratio of 10(5) , retention time of 10(4) s, and excellent flexibility (bending radius of 800 μm) have been achieved. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Left ventricle segmentation via graph cut distribution matching.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Li, Shuo; Islam, Ali; Chong, Jaron

2009-01-01

We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. Based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. Unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. Furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.

Cognitive flexibility in young children: General or task-specific capacity?

PubMed

Deák, Gedeon O; Wiseheart, Melody

2015-10-01

Cognitive flexibility is the ability to adapt to changing tasks or problems. To test whether cognitive flexibility is a coherent cognitive capacity in young children, we tested 3- to 5-year-olds' performance on two forms of task switching, rule-based (Three Dimension Changes Card Sorting, 3DCCS) and inductive (Flexible Induction of Meaning-Animates and Objects, FIM-Ob and FIM-An), as well as tests of response speed, verbal working memory, inhibition, and reasoning. Results suggest that cognitive flexibility is not a globally coherent trait; only the two inductive word-meaning (FIM) tests showed high inter-test coherence. Task- and knowledge-specific factors also determine children's flexibility in a given test. Response speed, vocabulary size, and causal reasoning skills further predicted individual and age differences in flexibility, although they did not have the same predictive relation with all three flexibility tests. Copyright © 2015 Elsevier Inc. All rights reserved.

Atomic theory of viscoelastic response and memory effects in metallic glasses

NASA Astrophysics Data System (ADS)

Cui, Bingyu; Yang, Jie; Qiao, Jichao; Jiang, Minqiang; Dai, Lanhong; Wang, Yun-Jiang; Zaccone, Alessio

2017-09-01

An atomic-scale theory of the viscoelastic response of metallic glasses is derived from first principles, using a Zwanzig-Caldeira-Leggett system-bath Hamiltonian as a starting point within the framework of nonaffine linear response to mechanical deformation. This approach provides a generalized Langevin equation (GLE) as the average equation of motion for an atom or ion in the material, from which non-Markovian nonaffine viscoelastic moduli are extracted. These can be evaluated using the vibrational density of states (DOS) as input, where the boson peak plays a prominent role in the mechanics. To compare with experimental data for binary ZrCu alloys, a numerical DOS was obtained from simulations of this system, which also take electronic degrees of freedom into account via the embedded-atom method for the interatomic potential. It is shown that the viscoelastic α -relaxation, including the α -wing asymmetry in the loss modulus, can be very well described by the theory if the memory kernel (the non-Markovian friction) in the GLE is taken to be a stretched-exponential decaying function of time. This finding directly implies strong memory effects in the atomic-scale dynamics and suggests that the α -relaxation time is related to the characteristic time scale over which atoms retain memory of their previous collision history. This memory time grows dramatically below the glass transition.

Profitability of Integrated Management of Fusarium Head Blight in North Carolina Winter Wheat.

PubMed

Cowger, Christina; Weisz, Randy; Arellano, Consuelo; Murphy, Paul

2016-08-01

Fusarium head blight (FHB) is one of the most difficult small-grain diseases to manage, due to the partial effectiveness of management techniques and the narrow window of time in which to apply fungicides profitably. The most effective management approach is to integrate cultivar resistance with FHB-specific fungicide applications; yet, when forecasted risk is intermediate, it is often unclear whether such an application will be profitable. To model the profitability of FHB management under varying conditions, we conducted a 2-year split-plot field experiment having as main plots high-yielding soft red winter wheat cultivars, four moderately resistant (MR) and three susceptible (S) to FHB. Subplots were sprayed at flowering with Prosaro or Caramba, or left untreated. The experiment was planted in seven North Carolina environments (location-year combinations); three were irrigated to promote FHB development and four were not irrigated. Response variables were yield, test weight, disease incidence, disease severity, deoxynivalenol (DON), Fusarium-damaged kernels, and percent infected kernels. Partial profits were compared in two ways: first, across low-, medium-, or high-DON environments; and second, across environment-cultivar combinations divided by risk forecast into "do spray" and "do not spray" categories. After surveying DON and test weight dockage among 21 North Carolina wheat purchasers, three typical market scenarios were used for modeling profitability: feed-wheat, flexible (feed or flour), and the flour market. A major finding was that, on average, MR cultivars were at least as profitable as S cultivars, regardless of epidemic severity or market. Fungicides were profitable in the feed-grain and flexible markets when DON was high, with MR cultivars in the flexible or flour markets when DON was intermediate, and on S cultivars aimed at the flexible market. The flour market was only profitable when FHB was present if DON levels were intermediate and cultivar resistance was combined with a fungicide. It proved impossible to use the risk forecast to predict profitability of fungicide application. Overall, the results indicated that cultivar resistance to FHB was important for profitability, an FHB-targeted fungicide expanded market options when risk was moderate or high, and the efficacy of fungicide decision-making is reduced by factors that limit the accuracy of risk forecasts.

Modifying Memory: Selectively Enhancing and Updating Personal Memories for a Museum Tour by Reactivating Them

PubMed Central

St. Jacques, Peggy L.; Schacter, Daniel L.

2013-01-01

Memory can be modified when reactivated, but little is known about how the properties and extent of reactivation can selectively affect subsequent memory. We developed a novel museum paradigm to directly investigate reactivation-induced plasticity for personal memories. Participants reactivated memories triggered by photos taken from a camera they wore during a museum tour and made relatedness judgments on novel photos taken from a different tour of the same museum. Subsequent recognition memory for events at the museum was better for memories that were highly reactivated (i.e., the retrieval cues during reactivation matched the encoding experience) than for memories that were reactivated at a lower level (i.e., the retrieval cues during reactivation mismatched the encoding experience), but reactivation also increased false recognition of photographs depicting stops that were not experienced during the museum tour. Reactivation thus enables memories to be selectively enhanced and distorted via updating, thereby supporting the dynamic and flexible nature of memory. PMID:23406611

GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.

PubMed

de Molina, Claudia; Serrano, Estefania; Garcia-Blas, Javier; Carretero, Jesus; Desco, Manuel; Abella, Monica

2018-05-15

Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

Language, Cognitive Flexibility, and Explicit False Belief Understanding: Longitudinal Analysis in Typical Development and Specific Language Impairment

ERIC Educational Resources Information Center

Farrant, Brad M.; Maybery, Murray T.; Fletcher, Janet

2012-01-01

The hypothesis that language plays a role in theory-of-mind (ToM) development is supported by a number of lines of evidence (e.g., H. Lohmann & M. Tomasello, 2003). The current study sought to further investigate the relations between maternal language input, memory for false sentential complements, cognitive flexibility, and the development of…

Radial Maze Analog for Pigeons: Evidence for Flexible Coding Strategies May Result from Faulty Assumptions

ERIC Educational Resources Information Center

Gipson, Cassandra D.; DiGian, Kelly A.; Miller, Holly C.; Zentall, Thomas R.

2008-01-01

Previous research with the radial maze has found evidence that rats can remember both places that they have already been (retrospective coding) and places they have yet to visit (prospective coding; Cook, R. G., Brown, M. F., & Riley, D. A. (1985). Flexible memory processing by rats: Use of prospective and retrospective information in the radial…

Different Executive Functions Support Different Kinds of Cognitive Flexibility: Evidence from 2-, 3-, and 4-Year-Olds

ERIC Educational Resources Information Center

Blakey, Emma; Visser, Ingmar; Carroll, Daniel J.

2016-01-01

Improvements in cognitive flexibility during the preschool years have been linked to developments in both working memory and inhibitory control, though the precise contribution of each remains unclear. In the current study, one hundred and twenty 2-, 3-, and 4-year-olds completed two rule-switching tasks. In one version, children switched rules in…

Specifying Links between Executive Functioning and Theory of Mind during Middle Childhood: Cognitive Flexibility Predicts Social Understanding

ERIC Educational Resources Information Center

Bock, Allison M.; Gallaway, Kristin C.; Hund, Alycia M.

2015-01-01

The purpose of this study was to specify the development of and links between executive functioning and theory of mind during middle childhood. One hundred four 7- to 12-year-old children completed a battery of age-appropriate tasks measuring working memory, inhibition, flexibility, theory of mind, and vocabulary. As expected, spatial working…

Striatal volume predicts level of video game skill acquisition.

PubMed

Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Prakash, Ruchika S; Voss, Michelle W; Graybiel, Ann M; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2010-11-01

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.

Treatment of Unruptured Intracranial Aneurysms and Cognitive Performance: Preliminary Results of a Prospective Clinical Trial.

PubMed

Bründl, Elisabeth; Böhm, Christina; Lürding, Ralf; Schödel, Petra; Bele, Sylvia; Hochreiter, Andreas; Scheitzach, Judith; Zeman, Florian; Brawanski, Alexander; Schebesch, Karl-Michael

2016-10-01

Few studies have addressed the effect of treatment of unruptured intracranial aneurysm (UIA) on cognitive function. Neuropsychological assessment after UIA treatment is underreported, and prospective trials have repeatedly been demanded. In 2014, we conducted a prospective controlled study to evaluate the differences in cognitive processing caused by the treatment of anterior circulation UIAs. Thirty patients were enrolled until September 2015. Ten patients received endovascular aneurysm occlusion (EV), 10 patients were treated microsurgically (MS), and 10 patients with surgically treated degenerative lumbar spine disease (LD) served as control. All patients underwent extended standardized neuropsychological assessment before (t 1 ) and 6 weeks after treatment (t 2 ). Tests included verbal, visual, and visuospatial memory, psychomotor functioning, executive functioning, and its subdomains verbal fluency and cognitive flexibility. We statistically evaluated intragroup and intergroup changes. Intragroup comparisons and group-rate analysis showed no significant impairment in overall neuropsychological performance, either postinterventionally or postoperatively. However, the postoperative performance in cognitive processing speed, cognitive flexibility, and executive functioning was significantly worse in the MS group than in the EV (P = 0.038) and LD group (P = 0.02). Compared with the EV group, patients with MS showed significant postoperative impairment in a subtest for auditory-verbal memory (Wechsler Memory Scale, Fourth Edition, Logical Memory II; MS vs. EV P = 0.011). The MS group trended toward posttreatment impairment in subtests for verbal fluency and semantic memory (Regensburg Word Fluency Test; MS vs. EV P = 0.083) and in auditory-verbal memory (Wechsler Memory Scale, Fourth Edition, Logical Memory II; MS vs. LD P = 0.06). Our preliminary data showed no effect of anterior circulation UIA treatment on overall neuropsychological function but impaired short-term executive processing in surgically treated patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Selective inactivation of adenosine A(2A) receptors in striatal neurons enhances working memory and reversal learning.

PubMed

Wei, Catherine J; Singer, Philipp; Coelho, Joana; Boison, Detlev; Feldon, Joram; Yee, Benjamin K; Chen, Jiang-Fan

2011-01-01

The adenosine A(2A) receptor (A(2A)R) is highly enriched in the striatum where it is uniquely positioned to integrate dopaminergic, glutamatergic, and other signals to modulate cognition. Although previous studies support the hypothesis that A(2A)R inactivation can be pro-cognitive, analyses of A(2A)R's effects on cognitive functions have been restricted to a small subset of cognitive domains. Furthermore, the relative contribution of A(2A)Rs in distinct brain regions remains largely unknown. Here, we studied the regulation of multiple memory processes by brain region-specific populations of A(2A)Rs. Specifically, we evaluated the cognitive impacts of conditional A(2A)R deletion restricted to either the entire forebrain (i.e., cerebral cortex, hippocampus, and striatum, fb-A(2A)R KO) or to striatum alone (st-A(2A)R KO) in recognition memory, working memory, reference memory, and reversal learning. This comprehensive, comparative analysis showed for the first time that depletion of A(2A)R-dependent signaling in either the entire forebrain or striatum alone is associated with two specific phenotypes indicative of cognitive flexibility-enhanced working memory and enhanced reversal learning. These selective pro-cognitive phenotypes seemed largely attributed to inactivation of striatal A(2A)Rs as they were captured by A(2A)R deletion restricted to striatal neurons. Neither spatial reference memory acquisition nor spatial recognition memory were grossly affected, and no evidence for compensatory changes in striatal or cortical D(1), D(2), or A(1) receptor expression was found. This study provides the first direct demonstration that targeting striatal A(2A)Rs may be an effective, novel strategy to facilitate cognitive flexibility under normal and pathologic conditions.

Concurrent and Short-term Prospective Relations among Neurocognitive Functioning, Coping, and Depressive Symptoms in Youth

PubMed Central

Evans, Lindsay D.; Kouros, Chrystyna D.; Samanez-Larkin, Silvia; Garber, Judy

2016-01-01

Objective The present short-term longitudinal study examined the concurrent and prospective relations among executive functioning (i.e., working memory and cognitive flexibility), coping (primary and secondary control coping), and depressive symptoms in children. Method Participants were 192 children between 9 and 15 years old (mean age = 12.36 years, SD = 1.77) recruited from the community. Youth were individually administered neuropsychological measures of executive functioning and intelligence, and completed self-report measures of executive dysfunction, coping, and depressive symptoms in small groups; the latter two measures were completed again four months later (Time 2). Linear regression analyses were used to examine direct associations among executive functions, coping, and depressive symptoms, and a bootstrapping procedure was used to test indirect effects of executive functioning on depressive symptoms through coping. Results Significant prospective relations were found between working memory measured at Time 1 (T1) and both primary and secondary control coping measured at Time 2 (T2), controlling for T1 coping. T1 cognitive flexibility significantly predicted T2 secondary control coping, controlling for T1 coping. Working memory deficits significantly predicted increases in depressive symptoms four months later, controlling for T1 depressive symptoms. Bootstrap analyses revealed that primary and secondary control coping each partially mediated the relation between working memory and depressive symptoms; secondary control coping partially mediated the relation between cognitive flexibility and depressive symptoms. Conclusion Coping may be one pathway through which deficits in executive functioning contribute to children's symptoms of depression. PMID:25651455

Shape‐Controlled, Self‐Wrapped Carbon Nanotube 3D Electronics

PubMed Central

Wang, Huiliang; Wang, Yanming; Tee, Benjamin C.‐K.; Kim, Kwanpyo; Lopez, Jeffrey; Cai, Wei

2015-01-01

The mechanical flexibility and structural softness of ultrathin devices based on organic thin films and low‐dimensional nanomaterials have enabled a wide range of applications including flexible display, artificial skin, and health monitoring devices. However, both living systems and inanimate systems that are encountered in daily lives are all 3D. It is therefore desirable to either create freestanding electronics in a 3D form or to incorporate electronics onto 3D objects. Here, a technique is reported to utilize shape‐memory polymers together with carbon nanotube flexible electronics to achieve this goal. Temperature‐assisted shape control of these freestanding electronics in a programmable manner is demonstrated, with theoretical analysis for understanding the shape evolution. The shape control process can be executed with prepatterned heaters, desirable for 3D shape formation in an enclosed environment. The incorporation of carbon nanotube transistors, gas sensors, temperature sensors, and memory devices that are capable of self‐wrapping onto any irregular shaped‐objects without degradations in device performance is demonstrated. PMID:27980972

Modern multicore and manycore architectures: Modelling, optimisation and benchmarking a multiblock CFD code

NASA Astrophysics Data System (ADS)

Hadade, Ioan; di Mare, Luca

2016-08-01

Modern multicore and manycore processors exhibit multiple levels of parallelism through a wide range of architectural features such as SIMD for data parallel execution or threads for core parallelism. The exploitation of multi-level parallelism is therefore crucial for achieving superior performance on current and future processors. This paper presents the performance tuning of a multiblock CFD solver on Intel SandyBridge and Haswell multicore CPUs and the Intel Xeon Phi Knights Corner coprocessor. Code optimisations have been applied on two computational kernels exhibiting different computational patterns: the update of flow variables and the evaluation of the Roe numerical fluxes. We discuss at great length the code transformations required for achieving efficient SIMD computations for both kernels across the selected devices including SIMD shuffles and transpositions for flux stencil computations and global memory transformations. Core parallelism is expressed through threading based on a number of domain decomposition techniques together with optimisations pertaining to alleviating NUMA effects found in multi-socket compute nodes. Results are correlated with the Roofline performance model in order to assert their efficiency for each distinct architecture. We report significant speedups for single thread execution across both kernels: 2-5X on the multicore CPUs and 14-23X on the Xeon Phi coprocessor. Computations at full node and chip concurrency deliver a factor of three speedup on the multicore processors and up to 24X on the Xeon Phi manycore coprocessor.

Gpu Implementation of a Viscous Flow Solver on Unstructured Grids

NASA Astrophysics Data System (ADS)

Xu, Tianhao; Chen, Long

2016-06-01

Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertex-centered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.

Exploring Manycore Multinode Systems for Irregular Applications with FPGA Prototyping

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

Ceriani, Marco; Palermo, Gianluca; Secchi, Simone

We present a prototype of a multi-core architecture implemented on FPGA, designed to enable efficient execution of irregular applications on distributed shared memory machines, while maintaining high performance on regular workloads. The architecture is composed of off-the-shelf soft-core cores, local interconnection and memory interface, integrated with custom components that optimize it for irregular applications. It relies on three key elements: a global address space, multithreading, and fine-grained synchronization. Global addresses are scrambled to reduce the formation of network hot-spots, while the latency of the transactions is covered by integrating an hardware scheduler within the custom load/store buffers to take advantagemore » from the availability of multiple executions threads, increasing the efficiency in a transparent way to the application. We evaluated a dual node system irregular kernels showing scalability in the number of cores and threads.« less

Tumour model with intrusive morphology, progressive phenotypical heterogeneity and memory

NASA Astrophysics Data System (ADS)

Atangana, Abdon; Alqahtani, Rubayyi T.

2018-03-01

The model of a tumour, taking into account invasive morphology, progressive phenotypical heterogeneity and also memory, is developed and analyzed in this paper. Three models are investigated: first we consider the model describing the proliferation concentrates in proximity of tumour boundaries, in which the oxygen levels are pronounced. Then we consider the model where the oxygen around the tumour is considered to be unchanged by the vascular system. Finally, we investigate the model of growth of tumours using the concept of non-local operators with the Mittag-Leffler kernel. We provide the numerical solution using the extended 3/8 Simpson method for the new trends of fractional integration for the proliferation concentrates in the proximity of the tumour model. Then we provide the exact solutions of the Gompertz model with three different fractional differentiations involving power law, exponential decay law and the Mittag-Leffler law.

Cholinergic manipulations bidirectionally regulate object memory destabilization

PubMed Central

Stiver, Mikaela L.; Jacklin, Derek L.; Mitchnick, Krista A.; Vicic, Nevena; Carlin, Justine; O'Hara, Matthew

2015-01-01

Consolidated memories can become destabilized and open to modification upon retrieval. Destabilization is most reliably prompted when novel information is present during memory reactivation. We hypothesized that the neurotransmitter acetylcholine (ACh) plays an important role in novelty-induced memory destabilization because of its established involvement in new learning. Accordingly, we investigated the effects of cholinergic manipulations in rats using an object recognition paradigm that requires reactivation novelty to destabilize object memories. The muscarinic receptor antagonist scopolamine, systemically or infused directly into the perirhinal cortex, blocked this novelty-induced memory destabilization. Conversely, systemic oxotremorine or carbachol, muscarinic receptor agonists, administered systemically or intraperirhinally, respectively, mimicked the destabilizing effect of novel information during reactivation. These bidirectional effects suggest a crucial influence of ACh on memory destabilization and the updating functions of reconsolidation. This is a hitherto unappreciated mnemonic role for ACh with implications for its potential involvement in cognitive flexibility and the dynamic process of long-term memory storage. PMID:25776038

Do the Contents of Visual Working Memory Automatically Influence Attentional Selection During Visual Search?

PubMed Central

Woodman, Geoffrey F.; Luck, Steven J.

2007-01-01

In many theories of cognition, researchers propose that working memory and perception operate interactively. For example, in previous studies researchers have suggested that sensory inputs matching the contents of working memory will have an automatic advantage in the competition for processing resources. The authors tested this hypothesis by requiring observers to perform a visual search task while concurrently maintaining object representations in visual working memory. The hypothesis that working memory activation produces a simple but uncontrollable bias signal leads to the prediction that items matching the contents of working memory will automatically capture attention. However, no evidence for automatic attentional capture was obtained; instead, the participants avoided attending to these items. Thus, the contents of working memory can be used in a flexible manner for facilitation or inhibition of processing. PMID:17469973

Do the contents of visual working memory automatically influence attentional selection during visual search?

PubMed

Woodman, Geoffrey F; Luck, Steven J

2007-04-01

In many theories of cognition, researchers propose that working memory and perception operate interactively. For example, in previous studies researchers have suggested that sensory inputs matching the contents of working memory will have an automatic advantage in the competition for processing resources. The authors tested this hypothesis by requiring observers to perform a visual search task while concurrently maintaining object representations in visual working memory. The hypothesis that working memory activation produces a simple but uncontrollable bias signal leads to the prediction that items matching the contents of working memory will automatically capture attention. However, no evidence for automatic attentional capture was obtained; instead, the participants avoided attending to these items. Thus, the contents of working memory can be used in a flexible manner for facilitation or inhibition of processing.

Intranasal Insulin: A Novel Treatment for Gulf War Multisymptom Illness

DTIC Science & Technology

2016-10-01

unexplained health symptoms; common among them were attention and memory difficulties, fatigue, joint pain, headaches, gastrointestinal complaints...slowing of response speed that affects mental flexibility across multiple cognitive domains (memory, attention , visuospatial functions) especially...Krengel and Sullivan, 2008; Toomey et al., 2009; Chao et al., 2011). Recent studies also have suggested that the response inhibition deficits shown in

Evaluation of Ferroelectric Materials for Memory Applications

DTIC Science & Technology

1990-06-01

as automobile odometers, access counters, and flight time recorders. Detailed product information is provided in Appendix A. 3. Optical Read...volatility but by definition are not reprogrammable , which severely restricts flexibility and makes error correction difficult. Magnetic core is non...battery-backed SRAMs as well. The programs for embedded controllers, such as those increasingly used in automobiles , are kept in nonvolatile memory. The

Deficits in executive and memory processes in delusional disorder: a case-control study.

PubMed

Ibanez-Casas, Inmaculada; De Portugal, Enrique; Gonzalez, Nieves; McKenney, Kathryn A; Haro, Josep M; Usall, Judith; Perez-Garcia, Miguel; Cervilla, Jorge A

2013-01-01

Delusional disorder has been traditionally considered a psychotic syndrome that does not evolve to cognitive deterioration. However, to date, very little empirical research has been done to explore cognitive executive components and memory processes in Delusional Disorder patients. This study will investigate whether patients with delusional disorder are intact in both executive function components (such as flexibility, impulsivity and updating components) and memory processes (such as immediate, short term and long term recall, learning and recognition). A large sample of patients with delusional disorder (n = 86) and a group of healthy controls (n = 343) were compared with regard to their performance in a broad battery of neuropsychological tests including Trail Making Test, Wisconsin Card Sorting Test, Colour-Word Stroop Test, and Complutense Verbal Learning Test (TAVEC). When compared to controls, cases of delusional disorder showed a significantly poorer performance in most cognitive tests. Thus, we demonstrate deficits in flexibility, impulsivity and updating components of executive functions as well as in memory processes. These findings held significant after taking into account sex, age, educational level and premorbid IQ. Our results do not support the traditional notion of patients with delusional disorder being cognitively intact.

Organic-Inorganic Hybrid Halide Perovskites for Memories, Transistors, and Artificial Synapses.

PubMed

Choi, Jaeho; Han, Ji Su; Hong, Kootak; Kim, Soo Young; Jang, Ho Won

2018-05-30

Fascinating characteristics of halide perovskites (HPs), which cannot be seen in conventional semiconductors and metal oxides, have boosted the application of HPs in electronic devices beyond optoelectronics such as solar cells, photodetectors, and light-emitting diodes. Here, recent advances in HP-based memory and logic devices such as resistive-switching memories (i.e., resistive random access memory (RRAM) or memristors), transistors, and artificial synapses are reviewed, focusing on inherently exotic properties of HPs: i) tunable bandgap, ii) facile majority carrier control, iii) fast ion migration, and iv) superflexibility. Various fabrication techniques of HP thin films from solution-based methods to vacuum processes are introduced. Up-to-date work in the field, emphasizing the compositional flexibility of HPs, suggest that HPs are promising candidates for next-generation electronic devices. Taking advantages of their unique electrical properties, low-cost and low-temperature synthesis, and compositional and mechanical flexibility, HPs have enormous potential to provide a new platform for future electronic devices and explosively intensive studies will pave the way in finding new HP materials beyond conventional silicon-based semiconductors to keep up with "More-than-Moore" times. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ultralow Power Consumption Flexible Biomemristors.

PubMed

Kim, Min-Kyu; Lee, Jang-Sik

2018-03-28

Low power consumption is the important requirement in memory devices for saving energy. In particular, improved energy efficiency is essential in implantable electronic devices for operation under a limited power supply. Here, we demonstrate the use of κ-carrageenan (κ-car) as the resistive switching layer to achieve memory that has low power consumption. A carboxymethyl (CM) group is introduced to the κ-car to increase its ionic conductivity. Ag was doped in CM:κ-car to improve the resistive switching properties of the devices. Memory devices based on Ag-doped CM:κ-car showed electroforming-free resistive switching. This device exhibited low reset voltage (∼0.05 V), fast switching speed (50 ns), and high on/off ratio (>10 3 ) under low compliance current (10 -5 A). Its power consumption (∼0.35 μW) is much lower than those of the previously reported biomemristors. The resistive switching may be a result of an electrochemical redox process and Ag filament formation in the CM:κ-car under an electric field. This biopolymer memory can also be fabricated on flexible substrate. This study verifies the feasibility of using biopolymers for applications to future implantable and biocompatible nanoelectronics.

Information Theory for Gabor Feature Selection for Face Recognition

NASA Astrophysics Data System (ADS)

Shen, Linlin; Bai, Li

2006-12-01

A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

Development of full wave code for modeling RF fields in hot non-uniform plasmas

NASA Astrophysics Data System (ADS)

Zhao, Liangji; Svidzinski, Vladimir; Spencer, Andrew; Kim, Jin-Soo

2016-10-01

FAR-TECH, Inc. is developing a full wave RF modeling code to model RF fields in fusion devices and in general plasma applications. As an important component of the code, an adaptive meshless technique is introduced to solve the wave equations, which allows resolving plasma resonances efficiently and adapting to the complexity of antenna geometry and device boundary. The computational points are generated using either a point elimination method or a force balancing method based on the monitor function, which is calculated by solving the cold plasma dispersion equation locally. Another part of the code is the conductivity kernel calculation, used for modeling the nonlocal hot plasma dielectric response. The conductivity kernel is calculated on a coarse grid of test points and then interpolated linearly onto the computational points. All the components of the code are parallelized using MPI and OpenMP libraries to optimize the execution speed and memory. The algorithm and the results of our numerical approach to solving 2-D wave equations in a tokamak geometry will be presented. Work is supported by the U.S. DOE SBIR program.

Associations among false belief understanding, counterfactual reasoning, and executive function.

PubMed

Guajardo, Nicole R; Parker, Jessica; Turley-Ames, Kandi

2009-09-01

The primary purposes of the present study were to clarify previous work on the association between counterfactual thinking and false belief performance to determine (1) whether these two variables are related and (2) if so, whether executive function skills mediate the relationship. A total of 92 3-, 4-, and 5-year-olds completed false belief, counterfactual, working memory, representational flexibility, and language measures. Counterfactual reasoning accounted for limited unique variance in false belief. Both working memory and representational flexibility partially mediated the relationship between counterfactual and false belief. Children, like adults, also generated various types of counterfactual statements to differing degrees. Results demonstrated the importance of language and executive function for both counterfactual and false belief. Implications are discussed.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

A Tradeoff Between Accuracy and Flexibility in a Working Memory Circuit Endowed with Slow Feedback Mechanisms.

PubMed

Pereira, Jacinto; Wang, Xiao-Jing

2015-10-01

Recent studies have shown that reverberation underlying mnemonic persistent activity must be slow, to ensure the stability of a working memory system and to give rise to long neural transients capable of accumulation of information over time. Is the slower the underlying process, the better? To address this question, we investigated 3 slow biophysical mechanisms that are activity-dependent and prominently present in the prefrontal cortex: Depolarization-induced suppression of inhibition (DSI), calcium-dependent nonspecific cationic current (ICAN), and short-term facilitation. Using a spiking network model for spatial working memory, we found that these processes enhance the memory accuracy by counteracting noise-induced drifts, heterogeneity-induced biases, and distractors. Furthermore, the incorporation of DSI and ICAN enlarges the range of network's parameter values required for working memory function. However, when a progressively slower process dominates the network, it becomes increasingly more difficult to erase a memory trace. We demonstrate this accuracy-flexibility tradeoff quantitatively and interpret it using a state-space analysis. Our results supports the scenario where N-methyl-d-aspartate receptor-dependent recurrent excitation is the workhorse for the maintenance of persistent activity, whereas slow synaptic or cellular processes contribute to the robustness of mnemonic function in a tradeoff that potentially can be adjusted according to behavioral demands. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Transistor and memory devices based on novel organic and biomaterials

NASA Astrophysics Data System (ADS)

Tseng, Jia-Hung

Organic semiconductor devices have aroused considerable interest because of the enormous potential in many technological applications. Organic electroluminescent devices have been extensively applied in display technology. Rapid progress has also been made in transistor and memory devices. This thesis considers aspects of the transistor based on novel organic single crystals and memory devices using hybrid nanocomposites comprising polymeric/inorganic nanoparticles, and biomolecule/quantum dots. Organic single crystals represent highly ordered structures with much less imperfections compared to amorphous thin films for probing the intrinsic charge transport in transistor devices. We demonstrate that free-standing, thin organic single crystals with natural flexing ability can be fabricated as flexible transistors. We study the surface properties of the organic crystals to determine a nearly perfect surface leading to high performance transistors. The flexible transistors can maintain high performance under reversible bending conditions. Because of the high quality crystal technique, we further develop applications on organic complementary circuits and organic single crystal photovoltaics. In the second part, two aspects of memory devices are studied. We examine the charge transfer process between conjugated polymers and metal nanoparticles. This charge transfer process is essential for the conductance switching in nanoseconds to induce the memory effect. Under the reduction condition, the charge transfer process is eliminated as well as the memory effect, raising the importance of coupling between conjugated systems and nanoparticle accepters. The other aspect of memory devices focuses on the interaction of virus biomolecules with quantum dots or metal nanoparticles in the devices. We investigate the impact of memory function on the hybrid bio-inorganic system. We perform an experimental analysis of the charge storage activation energy in tobacco mosaic virus with platinum nanoparticles. It is established that the effective barrier height in the materials systems needs to be further engineered in order to have sufficiently long retention times. Finally other novel architectures such as negative differential resistance devices and high density memory arrays are investigated for their influence on memory technology.

Oxide-based thin film transistors for flexible electronics

NASA Astrophysics Data System (ADS)

He, Yongli; Wang, Xiangyu; Gao, Ya; Hou, Yahui; Wan, Qing

2018-01-01

The continuous progress in thin film materials and devices has greatly promoted the development in the field of flexible electronics. As one of the most common thin film devices, thin film transistors (TFTs) are significant building blocks for flexible platforms. Flexible oxide-based TFTs are well compatible with flexible electronic systems due to low process temperature, high carrier mobility, and good uniformity. The present article is a review of the recent progress and major trends in the field of flexible oxide-based thin film transistors. First, an introduction of flexible electronics and flexible oxide-based thin film transistors is given. Next, we introduce oxide semiconductor materials and various flexible oxide-based TFTs classified by substrate materials including polymer plastics, paper sheets, metal foils, and flexible thin glass. Afterwards, applications of flexible oxide-based TFTs including bendable sensors, memories, circuits, and displays are presented. Finally, we give conclusions and a prospect for possible development trends. Project supported in part by the National Science Foundation for Distinguished Young Scholars of China (No. 61425020), in part by the National Natural Science Foundation of China (No. 11674162).

Kmerind: A Flexible Parallel Library for K-mer Indexing of Biological Sequences on Distributed Memory Systems.

PubMed

Pan, Tony; Flick, Patrick; Jain, Chirag; Liu, Yongchao; Aluru, Srinivas

2017-10-09

Counting and indexing fixed length substrings, or k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases every 3 days. We present Kmerind, a high performance parallel k-mer indexing library for distributed memory environments. The Kmerind library provides a set of simple and consistent APIs with sequential semantics and parallel implementations that are designed to be flexible and extensible. Kmerind's k-mer counter performs similarly or better than the best existing k-mer counting tools even on shared memory systems. In a distributed memory environment, Kmerind counts k-mers in a 120 GB sequence read dataset in less than 13 seconds on 1024 Xeon CPU cores, and fully indexes their positions in approximately 17 seconds. Querying for 1% of the k-mers in these indices can be completed in 0.23 seconds and 28 seconds, respectively. Kmerind is the first k-mer indexing library for distributed memory environments, and the first extensible library for general k-mer indexing and counting. Kmerind is available at https://github.com/ParBLiSS/kmerind.

Mechanisms mediating parallel action monitoring in fronto-striatal circuits.

PubMed

Beste, Christian; Ness, Vanessa; Lukas, Carsten; Hoffmann, Rainer; Stüwe, Sven; Falkenstein, Michael; Saft, Carsten

2012-08-01

Flexible response adaptation and the control of conflicting information play a pivotal role in daily life. Yet, little is known about the neuronal mechanisms mediating parallel control of these processes. We examined these mechanisms using a multi-methodological approach that integrated data from event-related potentials (ERPs) with structural MRI data and source localisation using sLORETA. Moreover, we calculated evoked wavelet oscillations. We applied this multi-methodological approach in healthy subjects and patients in a prodromal phase of a major basal ganglia disorder (i.e., Huntington's disease), to directly focus on fronto-striatal networks. Behavioural data indicated, especially the parallel execution of conflict monitoring and flexible response adaptation was modulated across the examined cohorts. When both processes do not co-incide a high integrity of fronto-striatal loops seems to be dispensable. The neurophysiological data suggests that conflict monitoring (reflected by the N2 ERP) and working memory processes (reflected by the P3 ERP) differentially contribute to this pattern of results. Flexible response adaptation under the constraint of high conflict processing affected the N2 and P3 ERP, as well as their delta frequency band oscillations. Yet, modulatory effects were strongest for the N2 ERP and evoked wavelet oscillations in this time range. The N2 ERPs were localized in the anterior cingulate cortex (BA32, BA24). Modulations of the P3 ERP were localized in parietal areas (BA7). In addition, MRI-determined caudate head volume predicted modulations in conflict monitoring, but not working memory processes. The results show how parallel conflict monitoring and flexible adaptation of action is mediated via fronto-striatal networks. While both, response monitoring and working memory processes seem to play a role, especially response selection processes and ACC-basal ganglia networks seem to be the driving force in mediating parallel conflict monitoring and flexible adaptation of actions. Copyright © 2012 Elsevier Inc. All rights reserved.

Cognitive Functions in Elite and Sub-Elite Youth Soccer Players Aged 13 to 17 Years.

PubMed

Huijgen, Barbara C H; Leemhuis, Sander; Kok, Niels M; Verburgh, Lot; Oosterlaan, Jaap; Elferink-Gemser, Marije T; Visscher, Chris

2015-01-01

Soccer players are required to anticipate and react continuously in a changing, relatively unpredictable situation in the field. Cognitive functions might be important to be successful in soccer. The current study investigated the relationship between cognitive functions and performance level in elite and sub-elite youth soccer players aged 13-17 years. A total of 47 elite youth soccer players (mean age 15.5 years, SD = 0.9) and 41 sub-elite youth soccer players (mean age 15.2 years, SD = 1.2) performed tasks for "higher-level" cognitive functions measuring working memory (i.e., Visual Memory Span), inhibitory control (i.e., Stop-Signal Task), cognitive flexibility (i.e., Trail Making Test), and metacognition (i.e., Delis-Kaplan Executive Function System Design Fluency Test). "Lower-level" cognitive processes, i.e., reaction time and visuo-perceptual abilities, were also measured with the previous tasks. ANOVA's showed that elite players outscored sub-elite players at the "higher-level" cognitive tasks only, especially on metacognition (p < .05). Using stepwise discriminant analysis, 62.5% of subjects was correctly assigned to one of the groups based on their metacognition, inhibitory control and cognitive flexibility performance. Controlling for training hours and academic level, MANCOVA's showed differences in favor of the elite youth soccer players on inhibitory control (p = .001), and cognitive flexibility (p = .042), but not on metacognition (p = .27). No differences were found concerning working memory nor the "lower-level" cognitive processes (p > .05). In conclusion, elite youth soccer players have better inhibitory control, cognitive flexibility, and especially metacognition than their sub-elite counterparts. However, when training hours are taken into account, differences between elite and sub-elite youth soccer players remain apparent on inhibitory control and cognitive flexibility in contrast to metacognition. This highlights the need for longitudinal studies to further investigate the importance of "higher-level" cognitive functions for talent identification, talent development and performance in soccer.

Cognitive Functions in Elite and Sub-Elite Youth Soccer Players Aged 13 to 17 Years

PubMed Central

Huijgen, Barbara C. H.; Leemhuis, Sander; Kok, Niels M.; Verburgh, Lot; Oosterlaan, Jaap; Elferink-Gemser, Marije T.; Visscher, Chris

2015-01-01

Soccer players are required to anticipate and react continuously in a changing, relatively unpredictable situation in the field. Cognitive functions might be important to be successful in soccer. The current study investigated the relationship between cognitive functions and performance level in elite and sub-elite youth soccer players aged 13–17 years. A total of 47 elite youth soccer players (mean age 15.5 years, SD = 0.9) and 41 sub-elite youth soccer players (mean age 15.2 years, SD = 1.2) performed tasks for “higher-level” cognitive functions measuring working memory (i.e., Visual Memory Span), inhibitory control (i.e., Stop-Signal Task), cognitive flexibility (i.e., Trail Making Test), and metacognition (i.e., Delis-Kaplan Executive Function System Design Fluency Test). “Lower-level” cognitive processes, i.e., reaction time and visuo-perceptual abilities, were also measured with the previous tasks. ANOVA’s showed that elite players outscored sub-elite players at the “higher-level” cognitive tasks only, especially on metacognition (p < .05). Using stepwise discriminant analysis, 62.5% of subjects was correctly assigned to one of the groups based on their metacognition, inhibitory control and cognitive flexibility performance. Controlling for training hours and academic level, MANCOVA’s showed differences in favor of the elite youth soccer players on inhibitory control (p = .001), and cognitive flexibility (p = .042), but not on metacognition (p = .27). No differences were found concerning working memory nor the “lower-level” cognitive processes (p > .05). In conclusion, elite youth soccer players have better inhibitory control, cognitive flexibility, and especially metacognition than their sub-elite counterparts. However, when training hours are taken into account, differences between elite and sub-elite youth soccer players remain apparent on inhibitory control and cognitive flexibility in contrast to metacognition. This highlights the need for longitudinal studies to further investigate the importance of “higher-level” cognitive functions for talent identification, talent development and performance in soccer. PMID:26657073

Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing

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

Landwehr, Joshua B.; Suetterlein, Joshua D.; Marquez, Andres

2016-05-16

Since 2012, the U.S. Department of Energy’s X-Stack program has been developing solutions including runtime systems, programming models, languages, compilers, and tools for the Exascale system software to address crucial performance and power requirements. Fine grain programming models and runtime systems show a great potential to efficiently utilize the underlying hardware. Thus, they are essential to many X-Stack efforts. An abundant amount of small tasks can better utilize the vast parallelism available on current and future machines. Moreover, finer tasks can recover faster and adapt better, due to a decrease in state and control. Nevertheless, current applications have been writtenmore » to exploit old paradigms (such as Communicating Sequential Processor and Bulk Synchronous Parallel processing). To fully utilize the advantages of these new systems, applications need to be adapted to these new paradigms. As part of the applications’ porting process, in-depth characterization studies, focused on both application characteristics and runtime features, need to take place to fully understand the application performance bottlenecks and how to resolve them. This paper presents a characterization study for a novel high performance runtime system, called the Open Community Runtime, using key HPC kernels as its vehicle. This study has the following contributions: one of the first high performance, fine grain, distributed memory runtime system implementing the OCR standard (version 0.99a); and a characterization study of key HPC kernels in terms of runtime primitives running on both intra and inter node environments. Running on a general purpose cluster, we have found up to 1635x relative speed-up for a parallel tiled Cholesky Kernels on 128 nodes with 16 cores each and a 1864x relative speed-up for a parallel tiled Smith-Waterman kernel on 128 nodes with 30 cores.« less

The structure of the clouds distributed operating system

NASA Technical Reports Server (NTRS)

Dasgupta, Partha; Leblanc, Richard J., Jr.

1989-01-01

A novel system architecture, based on the object model, is the central structuring concept used in the Clouds distributed operating system. This architecture makes Clouds attractive over a wide class of machines and environments. Clouds is a native operating system, designed and implemented at Georgia Tech. and runs on a set of generated purpose computers connected via a local area network. The system architecture of Clouds is composed of a system-wide global set of persistent (long-lived) virtual address spaces, called objects that contain persistent data and code. The object concept is implemented at the operating system level, thus presenting a single level storage view to the user. Lightweight treads carry computational activity through the code stored in the objects. The persistent objects and threads gives rise to a programming environment composed of shared permanent memory, dispensing with the need for hardware-derived concepts such as the file systems and message systems. Though the hardware may be distributed and may have disks and networks, the Clouds provides the applications with a logically centralized system, based on a shared, structured, single level store. The current design of Clouds uses a minimalist philosophy with respect to both the kernel and the operating system. That is, the kernel and the operating system support a bare minimum of functionality. Clouds also adheres to the concept of separation of policy and mechanism. Most low-level operating system services are implemented above the kernel and most high level services are implemented at the user level. From the measured performance of using the kernel mechanisms, we are able to demonstrate that efficient implementations are feasible for the object model on commercially available hardware. Clouds provides a rich environment for conducting research in distributed systems. Some of the topics addressed in this paper include distributed programming environments, consistency of persistent data and fault-tolerance.

Novel memory architecture for video signal processor

NASA Astrophysics Data System (ADS)

Hung, Jen-Sheng; Lin, Chia-Hsing; Jen, Chein-Wei

1993-11-01

An on-chip memory architecture for video signal processor (VSP) is proposed. This memory structure is a two-level design for the different data locality in video applications. The upper level--Memory A provides enough storage capacity to reduce the impact on the limitation of chip I/O bandwidth, and the lower level--Memory B provides enough data parallelism and flexibility to meet the requirements of multiple reconfigurable pipeline function units in a single VSP chip. The needed memory size is decided by the memory usage analysis for video algorithms and the number of function units. Both levels of memory adopted a dual-port memory scheme to sustain the simultaneous read and write operations. Especially, Memory B uses multiple one-read-one-write memory banks to emulate the real multiport memory. Therefore, one can change the configuration of Memory B to several sets of memories with variable read/write ports by adjusting the bus switches. Then the numbers of read ports and write ports in proposed memory can meet requirement of data flow patterns in different video coding algorithms. We have finished the design of a prototype memory design using 1.2- micrometers SPDM SRAM technology and will fabricated it through TSMC, in Taiwan.

Real-Time Symbol Extraction From Grey-Level Images

NASA Astrophysics Data System (ADS)

Massen, R.; Simnacher, M.; Rosch, J.; Herre, E.; Wuhrer, H. W.

1988-04-01

A VME-bus image pipeline processor for extracting vectorized contours from grey-level images in real-time is presented. This 3 Giga operation per second processor uses large kernel convolvers and new non-linear neighbourhood processing algorithms to compute true 1-pixel wide and noise-free contours without thresholding even from grey-level images with quite varying edge sharpness. The local edge orientation is used as an additional cue to compute a list of vectors describing the closed and open contours in real-time and to dump a CAD-like symbolic image description into a symbol memory at pixel clock rate.

Highly flexible and electroforming free resistive switching behavior of tungsten disulfide flakes fabricated through advanced printing technology

NASA Astrophysics Data System (ADS)

Muqeet Rehman, Muhammad; Uddin Siddiqui, Ghayas; Doh, Yang Hoi; Choi, Kyung Hyun

2017-09-01

Tungsten disulfide (WS2) is a transition metal dichalcogenide that differs from other 2D materials such as graphene owing to its distinctive semiconducting nature and tunable band gap. In this study, we have reported the structural, electrical, physical, and mechanical properties of exfoliated WS2 flakes and used them as the functional layer of a rewritable bipolar memory device. We demonstrate this concept by sandwiching few-layered WS2 flakes between two silver (Ag) electrodes on a flexible and transparent PET substrate. The entire device fabrication was carried out through all-printing technology such as reverse offset printing for patterning bottom electrodes, electrohydrodynamic (EHD) atomization for depositing functional thin film and EHD patterning for depositing the top electrode respectively. The memory device was further encapsulated with an atomically thin layer of aluminum oxide (Al2O3), deposited through a spatial atmospheric atomic layer deposition system to protect it against a humid environment. Remarkable resistive switching results were obtained, such as nonvolatile bipolar behavior, a high switching ratio (∼103), a long retention time (∼105 s), high endurance (1500 voltage sweeps), a low operating voltage (∼2 V), low current compliance (50 μA), mechanical robustness (1500 cycles) and unique repeatability at ambient conditions. Ag/WS2/Ag-based memory devices offer a new possibility for integration in flexible electronic devices.

Affect influences feature binding in memory: Trading between richness and strength of memory representations.

PubMed

Spachtholz, Philipp; Kuhbandner, Christof; Pekrun, Reinhard

2016-10-01

Research has shown that long-term memory representations of objects are formed as a natural product of perception even without any intentional memorization. It is not known, however, how rich these representations are in terms of the number of bound object features. In particular, because feature binding rests on resource-limited processes, there may be a context-dependent trade-off between the quantity of stored features and their memory strength. The authors examined whether affective state may bring about such a trade-off. Participants incidentally encoded pictures of real-world objects while experiencing positive or negative affect, and the authors later measured memory for 2 features. Results showed that participants traded between richness and strength of memory representations as a function of affect, with positive affect tuning memory formation toward richness and negative affect tuning memory formation toward strength. These findings demonstrate that memory binding is a flexible process that is modulated by affective state. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Future of Memory: Remembering, Imagining, and the Brain

PubMed Central

Schacter, Daniel L.; Addis, Donna Rose; Hassabis, Demis; Martin, Victoria C.; Spreng, R. Nathan; Szpunar, Karl K.

2013-01-01

During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and non-temporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning. PMID:23177955

A polymer/semiconductor write-once read-many-times memory

NASA Astrophysics Data System (ADS)

Möller, Sven; Perlov, Craig; Jackson, Warren; Taussig, Carl; Forrest, Stephen R.

2003-11-01

Organic devices promise to revolutionize the extent of, and access to, electronics by providing extremely inexpensive, lightweight and capable ubiquitous components that are printed onto plastic, glass or metal foils. One key component of an electronic circuit that has thus far received surprisingly little attention is an organic electronic memory. Here we report an architecture for a write-once read-many-times (WORM) memory, based on the hybrid integration of an electrochromic polymer with a thin-film silicon diode deposited onto a flexible metal foil substrate. WORM memories are desirable for ultralow-cost permanent storage of digital images, eliminating the need for slow, bulky and expensive mechanical drives used in conventional magnetic and optical memories. Our results indicate that the hybrid organic/inorganic memory device is a reliable means for achieving rapid, large-scale archival data storage. The WORM memory pixel exploits a mechanism of current-controlled, thermally activated un-doping of a two-component electrochromic conducting polymer.

Conditional bistability, a generic cellular mnemonic mechanism for robust and flexible working memory computations.

PubMed

Rodriguez, Guillaume; Sarazin, Matthieu; Clemente, Alexandra; Holden, Stephanie; Paz, Jeanne T; Delord, Bruno

2018-04-30

Persistent neural activity, the substrate of working memory, is thought to emerge from synaptic reverberation within recurrent networks. However, reverberation models do not robustly explain fundamental dynamics of persistent activity, including high-spiking irregularity, large intertrial variability, and state transitions. While cellular bistability may contribute to persistent activity, its rigidity appears incompatible with persistent activity labile characteristics. Here, we unravel in a cellular model a form of spike-mediated conditional bistability that is robust, generic and provides a rich repertoire of mnemonic computations. Under asynchronous synaptic inputs of the awakened state, conditional bistability generates spiking/bursting episodes, accounting for the irregularity, variability and state transitions characterizing persistent activity. This mechanism has likely been overlooked because of the sub-threshold input it requires and we predict how to assess it experimentally. Our results suggest a reexamination of the role of intrinsic properties in the collective network dynamics responsible for flexible working memory. SIGNIFICANCE STATEMENT This study unravels a novel form of intrinsic neuronal property, i.e. conditional bistability. We show that, thanks of its conditional character, conditional bistability favors the emergence of flexible and robust forms of persistent activity in PFC neural networks, in opposition to previously studied classical forms of absolute bistability. Specifically, we demonstrate for the first time that conditional bistability 1) is a generic biophysical spike-dependent mechanism of layer V pyramidal neurons in the PFC and that 2) it accounts for essential neurodynamical features for the organisation and flexibility of PFC persistent activity (the large irregularity and intertrial variability of the discharge and its organization under discrete stable states), which remain unexplained in a robust fashion by current models. Copyright © 2018 the authors.

Cholinesterase inhibitors, donepezil and rivastigmine, attenuate spatial memory and cognitive flexibility impairment induced by acute ethanol in the Barnes maze task in rats.

PubMed

Gawel, Kinga; Labuz, Krzysztof; Gibula-Bruzda, Ewa; Jenda, Malgorzata; Marszalek-Grabska, Marta; Filarowska, Joanna; Silberring, Jerzy; Kotlinska, Jolanta H

2016-10-01

Central cholinergic dysfunction contributes to acute spatial memory deficits produced by ethanol administration. Donepezil and rivastigmine elevate acetylcholine levels in the synaptic cleft through the inhibition of cholinesterases-enzymes involved in acetylcholine degradation. The aim of our study was to reveal whether donepezil (acetylcholinesterase inhibitor) and rivastigmine (also butyrylcholinesterase inhibitor) attenuate spatial memory impairment as induced by acute ethanol administration in the Barnes maze task (primary latency and number of errors in finding the escape box) in rats. Additionally, we compared the influence of these drugs on ethanol-disturbed memory. In the first experiment, the dose of ethanol (1.75 g/kg, i.p.) was selected that impaired spatial memory, but did not induce motor impairment. Next, we studied the influence of donepezil (1 and 3 mg/kg, i.p.), as well as rivastigmine (0.5 and 1 mg/kg, i.p.), given either before the probe trial or the reversal learning on ethanol-induced memory impairment. Our study demonstrated that these drugs, when given before the probe trial, were equally effective in attenuating ethanol-induced impairment in both test situations, whereas rivastigmine, at both doses (0.5 and 1 mg/kg, i.p.), and donepezil only at a higher dose (3 mg/kg, i.p.) given prior the reversal learning, attenuated the ethanol-induced impairment in cognitive flexibility. Thus, rivastigmine appears to exert more beneficial effect than donepezil in reversing ethanol-induced cognitive impairments-probably due to its wider spectrum of activity. In conclusion, the ethanol-induced spatial memory impairment may be attenuated by pharmacological manipulation of central cholinergic neurotransmission.

Time-based and event-based prospective memory in autism spectrum disorder: the roles of executive function and theory of mind, and time-estimation.

PubMed

Williams, David; Boucher, Jill; Lind, Sophie; Jarrold, Christopher

2013-07-01

Prospective memory (remembering to carry out an action in the future) has been studied relatively little in ASD. We explored time-based (carry out an action at a pre-specified time) and event-based (carry out an action upon the occurrence of a pre-specified event) prospective memory, as well as possible cognitive correlates, among 21 intellectually high-functioning children with ASD, and 21 age- and IQ-matched neurotypical comparison children. We found impaired time-based, but undiminished event-based, prospective memory among children with ASD. In the ASD group, time-based prospective memory performance was associated significantly with diminished theory of mind, but not with diminished cognitive flexibility. There was no evidence that time-estimation ability contributed to time-based prospective memory impairment in ASD.

Hypovitaminosis D and executive dysfunction in older adults with memory complaint: a memory clinic-based study.

PubMed

Annweiler, Cédric; Maby, Elodie; Meyerber, Marine; Beauchet, Olivier

2014-01-01

Hypovitaminosis D is associated with executive dysfunction as a whole. The purpose of this cross-sectional study was to determine whether lower vitamin D levels were associated with mental flexibility, information updating, or cognitive and motor inhibition among older adults. One hundred Caucasian older community dwellers with memory complaint (mean, 71.02 ± 0.74 years; 52.0% female) were divided into 3 groups according to serum 25-hydroxyvitamin D (25-OHD) concentration (deficiency <25 nmol/l, insufficiency 25-50 nmol/l, sufficiency >50 nmol/l). Executive functions were assessed with error rates by executing the Trail Making Test-B (TMT-B) for mental flexibility, the N-Back Test for information updating, the Stroop Interference Test for cognitive inhibition, and the Go/No-Go score for motor inhibition. Age, gender, BMI, education level, number of morbidities, depressive symptoms, Mini-Mental State Examination score, calcemia, estimated glomerular filtration rate and season tested were considered as potential confounders. Compared to participants with vitamin D insufficiency and sufficiency, those with vitamin D deficiency had a poorer TMT-B performance (p = 0.019 and p = 0.017, respectively) but similar N-Back (p = 0.175), Stroop (p = 0.135) and Go/No-Go (p = 0.224) scores. Cognitive performance did not differ between participants with insufficient and sufficient vitamin D levels. Vitamin D deficiency was associated with poorer performance on TMT-B (adjusted β = 1.48, p = 0.011), but not on N-Back (p = 0.665), Stroop (p = 0.817) or Go/No-Go (p = 0.971). Vitamin D deficiency was associated with poorer mental flexibility among older community dwellers with memory complaint. © 2013 S. Karger AG, Basel.

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

USSR Report Machine Tools and Metalworking Equipment.

DTIC Science & Technology

1986-04-22

directors decided to teach the Bulat a new trade. This generator is now used to strengthen high-speed cutting mills by hardening them in a medium of...modules (GPM) and flexible production complexes ( GPK ). The flexible automated line is usually used for mass production of components. Here the...of programmable coordinates (x^ithout grip) 5 4 Method of programming teaching Memory capacity of robot system, points 300 Positioning error, mm

C-MOS array design techniques: SUMC multiprocessor system study

NASA Technical Reports Server (NTRS)

Clapp, W. A.; Helbig, W. A.; Merriam, A. S.

1972-01-01

The current capabilities of LSI techniques for speed and reliability, plus the possibilities of assembling large configurations of LSI logic and storage elements, have demanded the study of multiprocessors and multiprocessing techniques, problems, and potentialities. Evaluated are three previous systems studies for a space ultrareliable modular computer multiprocessing system, and a new multiprocessing system is proposed that is flexibly configured with up to four central processors, four 1/0 processors, and 16 main memory units, plus auxiliary memory and peripheral devices. This multiprocessor system features a multilevel interrupt, qualified S/360 compatibility for ground-based generation of programs, virtual memory management of a storage hierarchy through 1/0 processors, and multiport access to multiple and shared memory units.

Acute stress impairs cognitive flexibility in men, not women.

PubMed

Shields, Grant S; Trainor, Brian C; Lam, Jovian C W; Yonelinas, Andrew P

2016-09-01

Psychosocial stress influences cognitive abilities, such as long-term memory retrieval. However, less is known about the effects of stress on cognitive flexibility, which is mediated by different neurobiological circuits and could thus be regulated by different neuroendocrine pathways. In this study, we randomly assigned healthy adults to an acute stress induction or control condition and subsequently assessed participants' cognitive flexibility using an open-source version of the Wisconsin Card Sort task. Drawing on work in rodents, we hypothesized that stress would have stronger impairing effects on cognitive flexibility in men than women. As predicted, we found that stress impaired cognitive flexibility in men but did not significantly affect women. Our results thus indicate that stress exerts sex-specific effects on cognitive flexibility in humans and add to the growing body of research highlighting the need to consider sex differences in effects of stress.

7 CFR 810.602 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of flaxseed kernels that are badly ground-damaged, badly weather... instructions. Also, underdeveloped, shriveled, and small pieces of flaxseed kernels removed in properly... recleaning. (c) Heat-damaged kernels. Kernels and pieces of flaxseed kernels that are materially discolored...

Memory and cognitive control circuits in mathematical cognition and learning.

PubMed

Menon, V

2016-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

Dopaminergic neurons write and update memories with cell-type-specific rules

PubMed Central

Aso, Yoshinori; Rubin, Gerald M

2016-01-01

Associative learning is thought to involve parallel and distributed mechanisms of memory formation and storage. In Drosophila, the mushroom body (MB) is the major site of associative odor memory formation. Previously we described the anatomy of the adult MB and defined 20 types of dopaminergic neurons (DANs) that each innervate distinct MB compartments (Aso et al., 2014a, 2014b). Here we compare the properties of memories formed by optogenetic activation of individual DAN cell types. We found extensive differences in training requirements for memory formation, decay dynamics, storage capacity and flexibility to learn new associations. Even a single DAN cell type can either write or reduce an aversive memory, or write an appetitive memory, depending on when it is activated relative to odor delivery. Our results show that different learning rules are executed in seemingly parallel memory systems, providing multiple distinct circuit-based strategies to predict future events from past experiences. DOI: http://dx.doi.org/10.7554/eLife.16135.001 PMID:27441388

Memory and cognitive control circuits in mathematical cognition and learning

PubMed Central

Menon, V.

2018-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

Dielectric elastomer memory

NASA Astrophysics Data System (ADS)

O'Brien, Benjamin M.; McKay, Thomas G.; Xie, Sheng Q.; Calius, Emilio P.; Anderson, Iain A.

2011-04-01

Life shows us that the distribution of intelligence throughout flexible muscular networks is a highly successful solution to a wide range of challenges, for example: human hearts, octopi, or even starfish. Recreating this success in engineered systems requires soft actuator technologies with embedded sensing and intelligence. Dielectric Elastomer Actuator(s) (DEA) are promising due to their large stresses and strains, as well as quiet flexible multimodal operation. Recently dielectric elastomer devices were presented with built in sensor, driver, and logic capability enabled by a new concept called the Dielectric Elastomer Switch(es) (DES). DES use electrode piezoresistivity to control the charge on DEA and enable the distribution of intelligence throughout a DEA device. In this paper we advance the capabilities of DES further to form volatile memory elements. A set reset flip-flop with inverted reset line was developed based on DES and DEA. With a 3200V supply the flip-flop behaved appropriately and demonstrated the creation of dielectric elastomer memory capable of changing state in response to 1 second long set and reset pulses. This memory opens up applications such as oscillator, de-bounce, timing, and sequential logic circuits; all of which could be distributed throughout biomimetic actuator arrays. Future work will include miniaturisation to improve response speed, implementation into more complex circuits, and investigation of longer lasting and more sensitive switching materials.

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

Design, fabrication, testing and delivery of a feasibility model laminated ferrite memory

NASA Technical Reports Server (NTRS)

Heckler, H. C.

1973-01-01

The effect of using multiword addressing with laminated ferrite arrays was made. Both a reduction in the number of components, and a reduction in power consumption was obtained for memory capacities between one million bits and one million words. An investigation into the effect of variations in the processing steps resulted in a number of process modifications that improved the quality of the arrays. A feasibility model laminated ferrite memory system was constructed by modifying a commercial plated wire memory system to operate with laminated ferrite arrays. To provide flexibility for the testing of the laminated ferrite memory, an exerciser has been constructed to automatically control the loading and recirculation of arbitrary size checkerboard patterns of one's and zero's and to display the patterns of stored information on a CRT screen.

A high performance data parallel tensor contraction framework: Application to coupled electro-mechanics

NASA Astrophysics Data System (ADS)

Poya, Roman; Gil, Antonio J.; Ortigosa, Rogelio

2017-07-01

The paper presents aspects of implementation of a new high performance tensor contraction framework for the numerical analysis of coupled and multi-physics problems on streaming architectures. In addition to explicit SIMD instructions and smart expression templates, the framework introduces domain specific constructs for the tensor cross product and its associated algebra recently rediscovered by Bonet et al. (2015, 2016) in the context of solid mechanics. The two key ingredients of the presented expression template engine are as follows. First, the capability to mathematically transform complex chains of operations to simpler equivalent expressions, while potentially avoiding routes with higher levels of computational complexity and, second, to perform a compile time depth-first or breadth-first search to find the optimal contraction indices of a large tensor network in order to minimise the number of floating point operations. For optimisations of tensor contraction such as loop transformation, loop fusion and data locality optimisations, the framework relies heavily on compile time technologies rather than source-to-source translation or JIT techniques. Every aspect of the framework is examined through relevant performance benchmarks, including the impact of data parallelism on the performance of isomorphic and nonisomorphic tensor products, the FLOP and memory I/O optimality in the evaluation of tensor networks, the compilation cost and memory footprint of the framework and the performance of tensor cross product kernels. The framework is then applied to finite element analysis of coupled electro-mechanical problems to assess the speed-ups achieved in kernel-based numerical integration of complex electroelastic energy functionals. In this context, domain-aware expression templates combined with SIMD instructions are shown to provide a significant speed-up over the classical low-level style programming techniques.

Transcranial Direct Current Stimulation Improves Executive Dysfunctions in ADHD: Implications for Inhibitory Control, Interference Control, Working Memory, and Cognitive Flexibility.

PubMed

Nejati, Vahid; Salehinejad, Mohammad Ali; Nitsche, Michael A; Najian, Asal; Javadi, Amir-Homayoun

2017-09-01

This study examined effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC) on major executive functions (EFs), including response inhibition, executive control, working memory (WM), and cognitive flexibility/task switching in ADHD. ADHD children received (a) left anodal/right cathodal DLPFC tDCS and (b) sham stimulation in Experiment 1 and (a) left anodal DLPFC/right cathodal OFC tDCS, (b) left cathodal DLPFC/right anodal OFC tDCS, and (c) sham stimulation in Experiment 2. The current intensity was 1 mA for 15 min with a 72-hr interval between sessions. Participants underwent Go/No-Go task, N-back test, Wisconsin Card Sorting Test (WCST), and Stroop task after each tDCS condition. Anodal left DLPFC tDCS most clearly affected executive control functions (e.g., WM, interference inhibition), while cathodal left DLPFC tDCS improved inhibitory control. Cognitive flexibility/task switching benefited from combined DLPFC-OFC, but not DLPFC stimulation alone. Task-specific stimulation protocols can improve EFs in ADHD.

7 CFR 810.1202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... kernels. Kernels, pieces of rye kernels, and other grains that are badly ground-damaged, badly weather.... Also, underdeveloped, shriveled, and small pieces of rye kernels removed in properly separating the...-damaged kernels. Kernels, pieces of rye kernels, and other grains that are materially discolored and...

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

DOE PAGES

Aktulga, Hasan Metin; Afibuzzaman, Md.; Williams, Samuel; ...

2017-06-01

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using themore » compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.« less

Influence of velocity effects on the shape of N2 (and air) broadened H2O lines revisited with classical molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Ngo, N. H.; Tran, H.; Gamache, R. R.; Bermejo, D.; Domenech, J.-L.

2012-08-01

The modeling of the shape of H2O lines perturbed by N2 (and air) using the Keilson-Storer (KS) kernel for collision-induced velocity changes is revisited with classical molecular dynamics simulations (CMDS). The latter have been performed for a large number of molecules starting from intermolecular-potential surfaces. Contrary to the assumption made in a previous study [H. Tran, D. Bermejo, J.-L. Domenech, P. Joubert, R. R. Gamache, and J.-M. Hartmann, J. Quant. Spectrosc. Radiat. Transf. 108, 126 (2007)], 10.1016/j.jqsrt.2007.03.009, the results of these CMDS show that the velocity-orientation and -modulus changes statistically occur at the same time scale. This validates the use of a single memory parameter in the Keilson-Storer kernel to describe both the velocity-orientation and -modulus changes. The CMDS results also show that velocity- and rotational state-changing collisions are statistically partially correlated. A partially correlated speed-dependent Keilson-Storer model has thus been used to describe the line-shape. For this, the velocity changes KS kernel parameters have been directly determined from CMDS, while the speed-dependent broadening and shifting coefficients have been calculated with a semi-classical approach. Comparisons between calculated spectra and measurements of several lines of H2O broadened by N2 (and air) in the ν3 and 2ν1 + ν2 + ν3 bands for a wide range of pressure show very satisfactory agreement. The evolution of non-Voigt effects from Doppler to collisional regimes is also presented and discussed.

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

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

Aktulga, Hasan Metin; Afibuzzaman, Md.; Williams, Samuel

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using themore » compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.« less

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

PubMed

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

PubMed Central

Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed. PMID:27070143

Support Vector Data Descriptions and k-Means Clustering: One Class?

PubMed

Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

Extremely flexible nanoscale ultrathin body silicon integrated circuits on plastic.

PubMed

Shahrjerdi, Davood; Bedell, Stephen W

2013-01-09

In recent years, flexible devices based on nanoscale materials and structures have begun to emerge, exploiting semiconductor nanowires, graphene, and carbon nanotubes. This is primarily to circumvent the existing shortcomings of the conventional flexible electronics based on organic and amorphous semiconductors. The aim of this new class of flexible nanoelectronics is to attain high-performance devices with increased packing density. However, highly integrated flexible circuits with nanoscale transistors have not yet been demonstrated. Here, we show nanoscale flexible circuits on 60 Å thick silicon, including functional ring oscillators and memory cells. The 100-stage ring oscillators exhibit the stage delay of ~16 ps at a power supply voltage of 0.9 V, the best reported for any flexible circuits to date. The mechanical flexibility is achieved by employing the controlled spalling technology, enabling the large-area transfer of the ultrathin body silicon devices to a plastic substrate at room temperature. These results provide a simple and cost-effective pathway to enable ultralight flexible nanoelectronics with unprecedented level of system complexity based on mainstream silicon technology.

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

Organizing for ontological change: The kernel of an AIDS research infrastructure

PubMed Central

Polk, Jessica Beth

2015-01-01

Is it possible to prepare and plan for emergent and changing objects of research? Members of the Multicenter AIDS Cohort Study have been investigating AIDS for over 30 years, and in that time, the disease has been repeatedly transformed. Over the years and across many changes, members have continued to study HIV disease while in the process regenerating an adaptable research organization. The key to sustaining this technoscientific flexibility has been what we call the kernel of a research infrastructure: ongoing efforts to maintain the availability of resources and services that may be brought to bear in the investigation of new objects. In the case of the Multicenter AIDS Cohort Study, these resources are as follows: specimens and data, calibrated instruments, heterogeneous experts, and participating cohorts of gay and bisexual men. We track three ontological transformations, examining how members prepared for and responded to changes: the discovery of a novel retroviral agent (HIV), the ability to test for that agent, and the transition of the disease from fatal to chronic through pharmaceutical intervention. Respectively, we call the work, ‘technologies’, and techniques of adapting to these changes, ‘repurposing’, ‘elaborating’, and ‘extending the kernel’. PMID:26477206

Biosynthesis and characterization of polyhydroxyalkanoate containing high 3-hydroxyhexanoate monomer fraction from crude palm kernel oil by recombinant Cupriavidus necator.

PubMed

Wong, Yoke-Ming; Brigham, Christopher J; Rha, ChoKyun; Sinskey, Anthony J; Sudesh, Kumar

2012-10-01

The potential of plant oils as sole carbon sources for production of P(3HB-co-3HHx) copolymer containing a high 3HHx monomer fraction using the recombinant Cupriavidus necator strain Re2160/pCB113 has been investigated. Various types and concentrations of plant oils were evaluated for efficient conversion of P(3HB-co-3HHx) copolymer. Crude palm kernel oil (CPKO) at a concentration of 2.5 g/L was found to be most suitable for production of copolymer with a 3HHx content of approximately 70 mol%. The time profile of these cells was also examined in order to study the trend of 3HHx monomer incorporation, PHA production and PHA synthase activity. (1)H NMR and (13)C NMR analyses confirmed the presence of P(3HB-co-3HHx) copolymer containing a high 3HHx monomer fraction, in which monomers were not randomly distributed. The results of various characterization analyses revealed that the copolymers containing a high 3HHx monomer fraction demonstrated soft and flexible mechanical properties. Copyright © 2012 Elsevier Ltd. All rights reserved.

Controlling the stream of thought: working memory capacity predicts adjustment of mind-wandering to situational demands.

PubMed

Rummel, Jan; Boywitt, C Dennis

2014-10-01

Although engaging in task-unrelated thoughts can be enjoyable and functional under certain circumstances, allowing one's mind to wander off-task will come at a cost to performance in many situations. Given that task-unrelated thoughts need to be blocked out when the current task requires full attention, it has been argued that cognitive control is necessary to prevent mind-wandering from becoming maladaptive. Extending this idea, we exposed participants to tasks of different demands and assessed mind-wandering via thought probes. Employing a latent-change model, we found mind-wandering to be adjusted to current task demands. As hypothesized, the degree of adjustment was predicted by working memory capacity, indicating that participants with higher working memory capacity were more flexible in their coordination of on- and off-task thoughts. Notably, the better the adjustment, the smaller performance decrements due to increased task demands were. On the basis of these findings, we argue that cognitive control does not simply allow blocking out task-unrelated thoughts but, rather, allows one to flexibly adjust mind-wandering to situational demands.

Resistive switching characteristics of HfO2-based memory devices on flexible plastics.

PubMed

Han, Yong; Cho, Kyoungah; Park, Sukhyung; Kim, Sangsig

2014-11-01

In this study, we examine the characteristics of HfO2-based resistive switching random access memory (ReRAM) devices on flexible plastics. The Pt/HfO2/Au ReRAM devices exhibit the unipolar resistive switching behaviors caused by the conducting filaments. From the Auger depth profiles of the HfO2 thin film, it is confirmed that the relatively lower oxygen content in the interface of the bottom electrode is responsible for the resistive switching by oxygen vacancies. And the unipolar resistive switching behaviors are analyzed from the C-V characteristics in which negative and positive capacitances are measured in the low-resistance state and the high-resistance state, respectively. The devices have a high on/off ratio of 10(4) and the excellent retention properties even after a continuous bending test of two thousand cycles. The correlation between the device size and the memory characteristics is investigated as well. A relatively smaller-sized device having a higher on/off ratio operates at a higher voltage than a relatively larger-sized device.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

7 CFR 810.802 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of grain kernels for which standards have been established under the.... (d) Heat-damaged kernels. Kernels and pieces of grain kernels for which standards have been...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Impaired cognitive plasticity and goal-directed control in adolescent obsessive-compulsive disorder.

PubMed

Gottwald, Julia; de Wit, Sanne; Apergis-Schoute, Annemieke M; Morein-Zamir, Sharon; Kaser, Muzaffer; Cormack, Francesca; Sule, Akeem; Limmer, Winifred; Morris, Anna Conway; Robbins, Trevor W; Sahakian, Barbara J

2018-01-22

Youths with obsessive-compulsive disorder (OCD) experience severe distress and impaired functioning at school and at home. Critical cognitive domains for daily functioning and academic success are learning, memory, cognitive flexibility and goal-directed behavioural control. Performance in these important domains among teenagers with OCD was therefore investigated in this study. A total of 36 youths with OCD and 36 healthy comparison subjects completed two memory tasks: Pattern Recognition Memory (PRM) and Paired Associates Learning (PAL); as well as the Intra-Extra Dimensional Set Shift (IED) task to quantitatively gauge learning as well as cognitive flexibility. A subset of 30 participants of each group also completed a Differential-Outcome Effect (DOE) task followed by a Slips-of-Action Task, designed to assess the balance of goal-directed and habitual behavioural control. Adolescent OCD patients showed a significant learning and memory impairment. Compared with healthy comparison subjects, they made more errors on PRM and PAL and in the first stages of IED involving discrimination and reversal learning. Patients were also slower to learn about contingencies in the DOE task and were less sensitive to outcome devaluation, suggesting an impairment in goal-directed control. This study advances the characterization of juvenile OCD. Patients demonstrated impairments in all learning and memory tasks. We also provide the first experimental evidence of impaired goal-directed control and lack of cognitive plasticity early in the development of OCD. The extent to which the impairments in these cognitive domains impact academic performance and symptom development warrants further investigation.