Sparse distributed memory prototype: Principles of operation

NASA Technical Reports Server (NTRS)

Flynn, Michael J.; Kanerva, Pentti; Ahanin, Bahram; Bhadkamkar, Neal; Flaherty, Paul; Hickey, Philip

1988-01-01

Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.

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

NASA Technical Reports Server (NTRS)

Keeler, James D.

1988-01-01

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

Sparse distributed memory: Principles and operation

NASA Technical Reports Server (NTRS)

Flynn, M. J.; Kanerva, P.; Bhadkamkar, N.

1989-01-01

Sparse distributed memory is a generalized random access memory (RAM) for long (1000 bit) binary words. Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech recognition and scene analysis, in signal detection and verification, and in adaptive control of automated equipment, in general, in dealing with real world information in real time. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. Major design issues were resolved which were faced in building the memories. The design is described of a prototype memory with 256 bit addresses and from 8 to 128 K locations for 256 bit words. A key aspect of the design is extensive use of dynamic RAM and other standard components.

A performance study of sparse Cholesky factorization on INTEL iPSC/860

NASA Technical Reports Server (NTRS)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Computer Sciences and Data Systems, volume 1

NASA Technical Reports Server (NTRS)

1987-01-01

Topics addressed include: software engineering; university grants; institutes; concurrent processing; sparse distributed memory; distributed operating systems; intelligent data management processes; expert system for image analysis; fault tolerant software; and architecture research.

A manual for PARTI runtime primitives

NASA Technical Reports Server (NTRS)

Berryman, Harry; Saltz, Joel

1990-01-01

Primitives are presented that are designed to help users efficiently program irregular problems (e.g., unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial differential equations solvers) on distributed memory machines. These primitives are also designed for use in compilers for distributed memory multiprocessors. Communications patterns are captured at runtime, and the appropriate send and receive messages are automatically generated.

Analysis, tuning and comparison of two general sparse solvers for distributed memory computers

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

Amestoy, P.R.; Duff, I.S.; L'Excellent, J.-Y.

2000-06-30

We describe the work performed in the context of a Franco-Berkeley funded project between NERSC-LBNL located in Berkeley (USA) and CERFACS-ENSEEIHT located in Toulouse (France). We discuss both the tuning and performance analysis of two distributed memory sparse solvers (superlu from Berkeley and mumps from Toulouse) on the 512 processor Cray T3E from NERSC (Lawrence Berkeley National Laboratory). This project gave us the opportunity to improve the algorithms and add new features to the codes. We then quite extensively analyze and compare the two approaches on a set of large problems from real applications. We further explain the main differencesmore » in the behavior of the approaches on artificial regular grid problems. As a conclusion to this activity report, we mention a set of parallel sparse solvers on which this type of study should be extended.« less

Sparse distributed memory

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study

NASA Technical Reports Server (NTRS)

Olshausen, Bruno; Watson, Andrew

1990-01-01

An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.

A manual for PARTI runtime primitives, revision 1

NASA Technical Reports Server (NTRS)

Das, Raja; Saltz, Joel; Berryman, Harry

1991-01-01

Primitives are presented that are designed to help users efficiently program irregular problems (e.g., unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial differential equations solvers) on distributed memory machines. These primitives are also designed for use in compilers for distributed memory multiprocessors. Communications patterns are captured at runtime, and the appropriate send and receive messages are automatically generated.

Efficient diagonalization of the sparse matrices produced within the framework of the UK R-matrix molecular codes

NASA Astrophysics Data System (ADS)

Galiatsatos, P. G.; Tennyson, J.

2012-11-01

The most time consuming step within the framework of the UK R-matrix molecular codes is that of the diagonalization of the inner region Hamiltonian matrix (IRHM). Here we present the method that we follow to speed up this step. We use shared memory machines (SMM), distributed memory machines (DMM), the OpenMP directive based parallel language, the MPI function based parallel language, the sparse matrix diagonalizers ARPACK and PARPACK, a variation for real symmetric matrices of the official coordinate sparse matrix format and finally a parallel sparse matrix-vector product (PSMV). The efficient application of the previous techniques rely on two important facts: the sparsity of the matrix is large enough (more than 98%) and in order to get back converged results we need a small only part of the matrix spectrum.

Two-dimensional shape recognition using sparse distributed memory

NASA Technical Reports Server (NTRS)

Kanerva, Pentti; Olshausen, Bruno

1990-01-01

Researchers propose a method for recognizing two-dimensional shapes (hand-drawn characters, for example) with an associative memory. The method consists of two stages: first, the image is preprocessed to extract tangents to the contour of the shape; second, the set of tangents is converted to a long bit string for recognition with sparse distributed memory (SDM). SDM provides a simple, massively parallel architecture for an associative memory. Long bit vectors (256 to 1000 bits, for example) serve as both data and addresses to the memory, and patterns are grouped or classified according to similarity in Hamming distance. At the moment, tangents are extracted in a simple manner by progressively blurring the image and then using a Canny-type edge detector (Canny, 1986) to find edges at each stage of blurring. This results in a grid of tangents. While the technique used for obtaining the tangents is at present rather ad hoc, researchers plan to adopt an existing framework for extracting edge orientation information over a variety of resolutions, such as suggested by Watson (1987, 1983), Marr and Hildreth (1980), or Canny (1986).

Data traffic reduction schemes for sparse Cholesky factorizations

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1988-01-01

Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.

FleCSI: Connection to Legion

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

Bergen, Benjamin Karl

2016-08-03

These are slides which are part of the ASC L2 Milestone Review. The following topics are covered: Legion Backend, Distributed-Memory Partitioning, Sparse Data Representations, and MPI-Legion Interoperability.

Using data tagging to improve the performance of Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1988-01-01

The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.

Efficient packing of patterns in sparse distributed memory by selective weighting of input bits

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1991-01-01

When a set of patterns is stored in a distributed memory, any given storage location participates in the storage of many patterns. From the perspective of any one stored pattern, the other patterns act as noise, and such noise limits the memory's storage capacity. The more similar the retrieval cues for two patterns are, the more the patterns interfere with each other in memory, and the harder it is to separate them on retrieval. A method is described of weighting the retrieval cues to reduce such interference and thus to improve the separability of patterns that have similar cues.

Distributed memory compiler methods for irregular problems: Data copy reuse and runtime partitioning

NASA Technical Reports Server (NTRS)

Das, Raja; Ponnusamy, Ravi; Saltz, Joel; Mavriplis, Dimitri

1991-01-01

Outlined here are two methods which we believe will play an important role in any distributed memory compiler able to handle sparse and unstructured problems. We describe how to link runtime partitioners to distributed memory compilers. In our scheme, programmers can implicitly specify how data and loop iterations are to be distributed between processors. This insulates users from having to deal explicitly with potentially complex algorithms that carry out work and data partitioning. We also describe a viable mechanism for tracking and reusing copies of off-processor data. In many programs, several loops access the same off-processor memory locations. As long as it can be verified that the values assigned to off-processor memory locations remain unmodified, we show that we can effectively reuse stored off-processor data. We present experimental data from a 3-D unstructured Euler solver run on iPSC/860 to demonstrate the usefulness of our methods.

Comparison between sparsely distributed memory and Hopfield-type neural network models

NASA Technical Reports Server (NTRS)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

Recognition of simple visual images using a sparse distributed memory: Some implementations and experiments

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1990-01-01

Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.

Weather prediction using a genetic memory

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

Neural networks and MIMD-multiprocessors

NASA Technical Reports Server (NTRS)

Vanhala, Jukka; Kaski, Kimmo

1990-01-01

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

Effects of partitioning and scheduling sparse matrix factorization on communication and load balance

NASA Technical Reports Server (NTRS)

Venugopal, Sesh; Naik, Vijay K.

1991-01-01

A block based, automatic partitioning and scheduling methodology is presented for sparse matrix factorization on distributed memory systems. Using experimental results, this technique is analyzed for communication and load imbalance overhead. To study the performance effects, these overheads were compared with those obtained from a straightforward 'wrap mapped' column assignment scheme. All experimental results were obtained using test sparse matrices from the Harwell-Boeing data set. The results show that there is a communication and load balance tradeoff. The block based method results in lower communication cost whereas the wrap mapped scheme gives better load balance.

Summer Proceedings 2016: The Center for Computing Research at Sandia National Laboratories

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

Carleton, James Brian; Parks, Michael L.

Solving sparse linear systems from the discretization of elliptic partial differential equations (PDEs) is an important building block in many engineering applications. Sparse direct solvers can solve general linear systems, but are usually slower and use much more memory than effective iterative solvers. To overcome these two disadvantages, a hierarchical solver (LoRaSp) based on H2-matrices was introduced in [22]. Here, we have developed a parallel version of the algorithm in LoRaSp to solve large sparse matrices on distributed memory machines. On a single processor, the factorization time of our parallel solver scales almost linearly with the problem size for three-dimensionalmore » problems, as opposed to the quadratic scalability of many existing sparse direct solvers. Moreover, our solver leads to almost constant numbers of iterations, when used as a preconditioner for Poisson problems. On more than one processor, our algorithm has significant speedups compared to sequential runs. With this parallel algorithm, we are able to solve large problems much faster than many existing packages as demonstrated by the numerical experiments.« less

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

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

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

DOE PAGES

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter; ...

2016-06-30

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits.

PubMed

Boguslawski, Bartosz; Gripon, Vincent; Seguin, Fabrice; Heitzmann, Frédéric

2016-02-01

Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of the bits used). Recently, a new family of sparse associative memories achieving almost optimal efficiency has been proposed. Their structure, relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that nonuniformity of the stored messages can lead to a dramatic decrease in performance. In this paper, we show the impact of nonuniformity on the performance of this recent model, and we exploit the structure of the model to improve its performance in practical applications, where data are not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with the real-world data to optimize power consumption of electronic circuits in practical test cases.

Effects of Ordering Strategies and Programming Paradigms on Sparse Matrix Computations

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Li, Xiaoye; Husbands, Parry; Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2002-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. For systems that are ill-conditioned, it is often necessary to use a preconditioning technique. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and ILU(O) preconditioned CG (PCG) using different programming paradigms and architectures. Results show that for this class of applications: ordering significantly improves overall performance on both distributed and distributed shared-memory systems, that cache reuse may be more important than reducing communication, that it is possible to achieve message-passing performance using shared-memory constructs through careful data ordering and distribution, and that a hybrid MPI+OpenMP paradigm increases programming complexity with little performance gains. A implementation of CG on the Cray MTA does not require special ordering or partitioning to obtain high efficiency and scalability, giving it a distinct advantage for adaptive applications; however, it shows limited scalability for PCG due to a lack of thread level parallelism.

Sparse orthogonal population representation of spatial context in the retrosplenial cortex.

PubMed

Mao, Dun; Kandler, Steffen; McNaughton, Bruce L; Bonin, Vincent

2017-08-15

Sparse orthogonal coding is a key feature of hippocampal neural activity, which is believed to increase episodic memory capacity and to assist in navigation. Some retrosplenial cortex (RSC) neurons convey distributed spatial and navigational signals, but place-field representations such as observed in the hippocampus have not been reported. Combining cellular Ca 2+ imaging in RSC of mice with a head-fixed locomotion assay, we identified a population of RSC neurons, located predominantly in superficial layers, whose ensemble activity closely resembles that of hippocampal CA1 place cells during the same task. Like CA1 place cells, these RSC neurons fire in sequences during movement, and show narrowly tuned firing fields that form a sparse, orthogonal code correlated with location. RSC 'place' cell activity is robust to environmental manipulations, showing partial remapping similar to that observed in CA1. This population code for spatial context may assist the RSC in its role in memory and/or navigation.Neurons in the retrosplenial cortex (RSC) encode spatial and navigational signals. Here the authors use calcium imaging to show that, similar to the hippocampus, RSC neurons also encode place cell-like activity in a sparse orthogonal representation, partially anchored to the allocentric cues on the linear track.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

Accessing sparse arrays in parallel memories

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

Banerjee, U.; Gajski, D.; Kuck, D.

The concept of dense and sparse execution of arrays is introduced. Arrays themselves can be stored in a dense or sparse manner in a parallel memory with m memory modules. The paper proposes hardware for speeding up the execution of array operations of the form c(c/sub 0/+ci)=a(a/sub 0/+ai) op b(b/sub 0/+bi), where a/sub 0/, a, b/sub 0/, b, c/sub 0/, c are integer constants and i is an index variable. The hardware handles 'sparse execution', in which the operation op is not executed for every value of i. The hardware also makes provision for 'sparse storage', in which memory spacemore » is not provided for every array element. It is shown how to access array elements of the above form without conflict in an efficient way. The efficiency is obtained by using some specialised units which are basically smart memories with priority detection, one's counting or associative searching. Generalisation to multidimensional arrays is shown possible under restrictions defined in the paper. 12 references.« less

Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination.

PubMed

Lin, Andrew C; Bygrave, Alexei M; de Calignon, Alix; Lee, Tzumin; Miesenböck, Gero

2014-04-01

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor specificity of memories.

Massively parallel sparse matrix function calculations with NTPoly

NASA Astrophysics Data System (ADS)

Dawson, William; Nakajima, Takahito

2018-04-01

We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

Some methods of encoding simple visual images for use with a sparse distributed memory, with applications to character recognition

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1989-01-01

To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.

A Cerebellar-model Associative Memory as a Generalized Random-access Memory

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1989-01-01

A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.

A class of designs for a sparse distributed memory

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1989-01-01

A general class of designs for a space distributed memory (SDM) is described. The author shows that Kanerva's original design and the selected-coordinate design are related, and that there is a series of possible intermediate designs between those two designs. In each such design, the set of addresses that activate a memory location is a sphere in the address space. We can also have hybrid designs, in which the memory locations may be a mixture of those found in the other designs. In some applications, the bits of the read and write addresses that will actually be used might be mostly zeros; that is, the addresses might lie on or near z hyperplane in the address space. The author describes a hyperplane design which is adapted to this situation and compares it to an adaptation of Kanerva's design. To study the performance of these designs, he computes the expected number of memory locations activated by both of two addresses.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

PubMed

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Distributed memory approaches for robotic neural controllers

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1990-01-01

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

Highly parallel sparse Cholesky factorization

NASA Technical Reports Server (NTRS)

Gilbert, John R.; Schreiber, Robert

1990-01-01

Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.

Arc expression identifies the lateral amygdala fear memory trace

PubMed Central

Gouty-Colomer, L A; Hosseini, B; Marcelo, I M; Schreiber, J; Slump, D E; Yamaguchi, S; Houweling, A R; Jaarsma, D; Elgersma, Y; Kushner, S A

2016-01-01

Memories are encoded within sparsely distributed neuronal ensembles. However, the defining cellular properties of neurons within a memory trace remain incompletely understood. Using a fluorescence-based Arc reporter, we were able to visually identify the distinct subset of lateral amygdala (LA) neurons activated during auditory fear conditioning. We found that Arc-expressing neurons have enhanced intrinsic excitability and are preferentially recruited into newly encoded memory traces. Furthermore, synaptic potentiation of thalamic inputs to the LA during fear conditioning is learning-specific, postsynaptically mediated and highly localized to Arc-expressing neurons. Taken together, our findings validate the immediate-early gene Arc as a molecular marker for the LA neuronal ensemble recruited during fear learning. Moreover, these results establish a model of fear memory formation in which intrinsic excitability determines neuronal selection, whereas learning-related encoding is governed by synaptic plasticity. PMID:25802982

Visual properties and memorising scenes: Effects of image-space sparseness and uniformity.

PubMed

Lukavský, Jiří; Děchtěrenko, Filip

2017-10-01

Previous studies have demonstrated that humans have a remarkable capacity to memorise a large number of scenes. The research on memorability has shown that memory performance can be predicted by the content of an image. We explored how remembering an image is affected by the image properties within the context of the reference set, including the extent to which it is different from its neighbours (image-space sparseness) and if it belongs to the same category as its neighbours (uniformity). We used a reference set of 2,048 scenes (64 categories), evaluated pairwise scene similarity using deep features from a pretrained convolutional neural network (CNN), and calculated the image-space sparseness and uniformity for each image. We ran three memory experiments, varying the memory workload with experiment length and colour/greyscale presentation. We measured the sensitivity and criterion value changes as a function of image-space sparseness and uniformity. Across all three experiments, we found separate effects of 1) sparseness on memory sensitivity, and 2) uniformity on the recognition criterion. People better remembered (and correctly rejected) images that were more separated from others. People tended to make more false alarms and fewer miss errors in images from categorically uniform portions of the image-space. We propose that both image-space properties affect human decisions when recognising images. Additionally, we found that colour presentation did not yield better memory performance over grayscale images.

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

PubMed

Fink, Christian G

2017-01-01

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

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

A survey of visual preprocessing and shape representation techniques

NASA Technical Reports Server (NTRS)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Memory Retrieval in Mice and Men

PubMed Central

Ben-Yakov, Aya; Dudai, Yadin; Mayford, Mark R.

2015-01-01

Retrieval, the use of learned information, was until recently mostly terra incognita in the neurobiology of memory, owing to shortage of research methods with the spatiotemporal resolution required to identify and dissect fast reactivation or reconstruction of complex memories in the mammalian brain. The development of novel paradigms, model systems, and new tools in molecular genetics, electrophysiology, optogenetics, in situ microscopy, and functional imaging, have contributed markedly in recent years to our ability to investigate brain mechanisms of retrieval. We review selected developments in the study of explicit retrieval in the rodent and human brain. The picture that emerges is that retrieval involves coordinated fast interplay of sparse and distributed corticohippocampal and neocortical networks that may permit permutational binding of representational elements to yield specific representations. These representations are driven largely by the activity patterns shaped during encoding, but are malleable, subject to the influence of time and interaction of the existing memory with novel information. PMID:26438596

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

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

DOE PAGES

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry; ...

2016-10-27

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

Performance of Point and Range Queries for In-memory Databases using Radix Trees on GPUs

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

Alam, Maksudul; Yoginath, Srikanth B; Perumalla, Kalyan S

In in-memory database systems augmented by hardware accelerators, accelerating the index searching operations can greatly increase the runtime performance of database queries. Recently, adaptive radix trees (ART) have been shown to provide very fast index search implementation on the CPU. Here, we focus on an accelerator-based implementation of ART. We present a detailed performance study of our GPU-based adaptive radix tree (GRT) implementation over a variety of key distributions, synthetic benchmarks, and actual keys from music and book data sets. The performance is also compared with other index-searching schemes on the GPU. GRT on modern GPUs achieves some of themore » highest rates of index searches reported in the literature. For point queries, a throughput of up to 106 million and 130 million lookups per second is achieved for sparse and dense keys, respectively. For range queries, GRT yields 600 million and 1000 million lookups per second for sparse and dense keys, respectively, on a large dataset of 64 million 32-bit keys.« less

Memory Allocation: Mechanisms and Function.

PubMed

Josselyn, Sheena A; Frankland, Paul W

2018-04-25

Memories for events are thought to be represented in sparse, distributed neuronal ensembles (or engrams). In this article, we review how neurons are chosen to become part of a particular engram, via a process of neuronal allocation. Experiments in rodents indicate that eligible neurons compete for allocation to a given engram, with more excitable neurons winning this competition. Moreover, fluctuations in neuronal excitability determine how engrams interact, promoting either memory integration (via coallocation to overlapping engrams) or separation (via disallocation to nonoverlapping engrams). In parallel with rodent studies, recent findings in humans verify the importance of this memory integration process for linking memories that occur close in time or share related content. A deeper understanding of allocation promises to provide insights into the logic underlying how knowledge is normally organized in the brain and the disorders in which this process has gone awry. Expected final online publication date for the Annual Review of Neuroscience Volume 41 is July 8, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

PubMed

Yang, C L; Wei, H Y; Adler, A; Soleimani, M

2013-06-01

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

HPC-NMF: A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization

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

Kannan, Ramakrishnan; Sukumar, Sreenivas R.; Ballard, Grey M.

NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets. We propose a high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems formore » $$\\WW$$ and $$\\HH$$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementation, our algorithm is also flexible: It performs well for both dense and sparse matrices, and allows the user to choose any one of the multiple algorithms for solving the updates to low rank factors $$\\WW$$ and $$\\HH$$ within the alternating iterations.« less

Human memory retrieval as Lévy foraging

NASA Astrophysics Data System (ADS)

Rhodes, Theo; Turvey, Michael T.

2007-11-01

When people attempt to recall as many words as possible from a specific category (e.g., animal names) their retrievals occur sporadically over an extended temporal period. Retrievals decline as recall progresses, but short retrieval bursts can occur even after tens of minutes of performing the task. To date, efforts to gain insight into the nature of retrieval from this fundamental phenomenon of semantic memory have focused primarily upon the exponential growth rate of cumulative recall. Here we focus upon the time intervals between retrievals. We expected and found that, for each participant in our experiment, these intervals conformed to a Lévy distribution suggesting that the Lévy flight dynamics that characterize foraging behavior may also characterize retrieval from semantic memory. The closer the exponent on the inverse square power-law distribution of retrieval intervals approximated the optimal foraging value of 2, the more efficient was the retrieval. At an abstract dynamical level, foraging for particular foods in one's niche and searching for particular words in one's memory must be similar processes if particular foods and particular words are randomly and sparsely located in their respective spaces at sites that are not known a priori. We discuss whether Lévy dynamics imply that memory processes, like foraging, are optimized in an ecological way.

Limited-memory trust-region methods for sparse relaxation

NASA Astrophysics Data System (ADS)

Adhikari, Lasith; DeGuchy, Omar; Erway, Jennifer B.; Lockhart, Shelby; Marcia, Roummel F.

2017-08-01

In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.

Holographic implementation of a binary associative memory for improved recognition

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Somnath; Ghosh, Ajay; Datta, Asit K.

1998-03-01

Neural network associate memory has found wide application sin pattern recognition techniques. We propose an associative memory model for binary character recognition. The interconnection strengths of the memory are binary valued. The concept of sparse coding is sued to enhance the storage efficiency of the model. The question of imposed preconditioning of pattern vectors, which is inherent in a sparsely coded conventional memory, is eliminated by using a multistep correlation technique an the ability of correct association is enhanced in a real-time application. A potential optoelectronic implementation of the proposed associative memory is also described. The learning and recall is possible by using digital optical matrix-vector multiplication, where full use of parallelism and connectivity of optics is made. A hologram is used in the experiment as a longer memory (LTM) for storing all input information. The short-term memory or the interconnection weight matrix required during the recall process is configured by retrieving the necessary information from the holographic LTM.

Scalability improvements to NRLMOL for DFT calculations of large molecules

NASA Astrophysics Data System (ADS)

Diaz, Carlos Manuel

Advances in high performance computing (HPC) have provided a way to treat large, computationally demanding tasks using thousands of processors. With the development of more powerful HPC architectures, the need to create efficient and scalable code has grown more important. Electronic structure calculations are valuable in understanding experimental observations and are routinely used for new materials predictions. For the electronic structure calculations, the memory and computation time are proportional to the number of atoms. Memory requirements for these calculations scale as N2, where N is the number of atoms. While the recent advances in HPC offer platforms with large numbers of cores, the limited amount of memory available on a given node and poor scalability of the electronic structure code hinder their efficient usage of these platforms. This thesis will present some developments to overcome these bottlenecks in order to study large systems. These developments, which are implemented in the NRLMOL electronic structure code, involve the use of sparse matrix storage formats and the use of linear algebra using sparse and distributed matrices. These developments along with other related development now allow ground state density functional calculations using up to 25,000 basis functions and the excited state calculations using up to 17,000 basis functions while utilizing all cores on a node. An example on a light-harvesting triad molecule is described. Finally, future plans to further improve the scalability will be presented.

Data traffic reduction schemes for Cholesky factorization on asynchronous multiprocessor systems

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1989-01-01

Communication requirements of Cholesky factorization of dense and sparse symmetric, positive definite matrices are analyzed. The communication requirement is characterized by the data traffic generated on multiprocessor systems with local and shared memory. Lower bound proofs are given to show that when the load is uniformly distributed the data traffic associated with factoring an n x n dense matrix using n to the alpha power (alpha less than or equal 2) processors is omega(n to the 2 + alpha/2 power). For n x n sparse matrices representing a square root of n x square root of n regular grid graph the data traffic is shown to be omega(n to the 1 + alpha/2 power), alpha less than or equal 1. Partitioning schemes that are variations of block assignment scheme are described and it is shown that the data traffic generated by these schemes are asymptotically optimal. The schemes allow efficient use of up to O(n to the 2nd power) processors in the dense case and up to O(n) processors in the sparse case before the total data traffic reaches the maximum value of O(n to the 3rd power) and O(n to the 3/2 power), respectively. It is shown that the block based partitioning schemes allow a better utilization of the data accessed from shared memory and thus reduce the data traffic than those based on column-wise wrap around assignment schemes.

Brain Region-Specific Activity Patterns after Recent or Remote Memory Retrieval of Auditory Conditioned Fear

ERIC Educational Resources Information Center

Kwon, Jeong-Tae; Jhang, Jinho; Kim, Hyung-Su; Lee, Sujin; Han, Jin-Hee

2012-01-01

Memory is thought to be sparsely encoded throughout multiple brain regions forming unique memory trace. Although evidence has established that the amygdala is a key brain site for memory storage and retrieval of auditory conditioned fear memory, it remains elusive whether the auditory brain regions may be involved in fear memory storage or…

The search for a hippocampal engram.

PubMed

Mayford, Mark

2014-01-05

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory.

The search for a hippocampal engram

PubMed Central

Mayford, Mark

2014-01-01

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory. PMID:24298162

Parallel Conjugate Gradient: Effects of Ordering Strategies, Programming Paradigms, and Architectural Platforms

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Heber, Gerd; Biswas, Rupak

2000-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations within a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming paradigms and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multi-threaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

NASA Technical Reports Server (NTRS)

Orth, Rene; Koster, Randal Dean; Seneviratne, Sonia I.

2013-01-01

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

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

NASA Technical Reports Server (NTRS)

Keeler, James D.

1987-01-01

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

Regional brain activity that determines successful and unsuccessful working memory formation.

PubMed

Teramoto, Shohei; Inaoka, Tsubasa; Ono, Yumie

2016-08-01

Using EEG source reconstruction with Multiple Sparse Priors (MSP), we investigated the regional brain activity that determines successful memory encoding in two participant groups of high and low accuracy rates. Eighteen healthy young adults performed a sequential fashion of visual Sternberg memory task. The 32-channel EEG was continuously measured during participants performed two 70 trials of memory task. The regional brain activity corresponding to the oscillatory EEG activity in the alpha band (8-13 Hz) during encoding period was analyzed by MSP implemented in SPM8. We divided the data of all participants into 2 groups (low- and highperformance group) and analyzed differences in regional brain activity between trials in which participants answered correctly and incorrectly within each of the group. Participants in low-performance group showed significant activity increase in the visual cortices in their successful trials compared to unsuccessful ones. On the other hand, those in high-performance group showed a significant activity increase in widely distributed cortical regions in the frontal, temporal, and parietal areas including those suggested as Baddeley's working memory model. Further comparison of activated cortical volumes and mean current source intensities within the cortical regions of Baddeley's model during memory encoding demonstrated that participants in high-performance group showed enhanced activity in the right premotor cortex, which plays an important role in maintaining visuospatial attention, compared to those in low performance group. Our results suggest that better ability in memory encoding is associated with distributed and stronger regional brain activities including the premotor cortex, possibly indicating efficient allocation of cognitive load and maintenance of attention.

Memory in Autistic Spectrum Disorder

ERIC Educational Resources Information Center

Boucher, Jill; Mayes, Andrew; Bigham, Sally

2012-01-01

Behavioral evidence concerning memory in forms of high-functioning autism (HFA) and in moderately low-functioning autism (M-LFA) is reviewed and compared. Findings on M-LFA are sparse. However, it is provisionally concluded that memory profiles in HFA and M-LFA (relative to ability-matched controls) are similar but that declarative memory…

Testing of Error-Correcting Sparse Permutation Channel Codes

NASA Technical Reports Server (NTRS)

Shcheglov, Kirill, V.; Orlov, Sergei S.

2008-01-01

A computer program performs Monte Carlo direct numerical simulations for testing sparse permutation channel codes, which offer strong error-correction capabilities at high code rates and are considered especially suitable for storage of digital data in holographic and volume memories. A word in a code of this type is characterized by, among other things, a sparseness parameter (M) and a fixed number (K) of 1 or "on" bits in a channel block length of N.

Hardware Acceleration of Sparse Cognitive Algorithms

DTIC Science & Technology

2016-05-01

Processor in Memory (PiM) extensions and a 65 nm ASIC version. They were compared against a 28 nm GPU baseline using the KTH video action recognition...30 Table 17. Memory Requirement of Proposed ASIC...for improvement of performance per unit of power for customized implementations of the Sparsey and Numenta Hierarchical Temporal Memory (HTM

Experience-Driven Formation of Parts-Based Representations in a Model of Layered Visual Memory

PubMed Central

Jitsev, Jenia; von der Malsburg, Christoph

2009-01-01

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces. PMID:19862345

Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

NASA Astrophysics Data System (ADS)

Aoyagi, Toshio; Nomura, Masaki

1999-08-01

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

Support for an auto-associative model of spoken cued recall: evidence from fMRI.

PubMed

de Zubicaray, Greig; McMahon, Katie; Eastburn, Mathew; Pringle, Alan J; Lorenz, Lina; Humphreys, Michael S

2007-03-02

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

How Does the Sparse Memory “Engram” Neurons Encode the Memory of a Spatial–Temporal Event?

PubMed Central

Guan, Ji-Song; Jiang, Jun; Xie, Hong; Liu, Kai-Yuan

2016-01-01

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns. PMID:27601979

How Does the Sparse Memory "Engram" Neurons Encode the Memory of a Spatial-Temporal Event?

PubMed

Guan, Ji-Song; Jiang, Jun; Xie, Hong; Liu, Kai-Yuan

2016-01-01

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns.

Computational efficiency improvements for image colorization

NASA Astrophysics Data System (ADS)

Yu, Chao; Sharma, Gaurav; Aly, Hussein

2013-03-01

We propose an efficient algorithm for colorization of greyscale images. As in prior work, colorization is posed as an optimization problem: a user specifies the color for a few scribbles drawn on the greyscale image and the color image is obtained by propagating color information from the scribbles to surrounding regions, while maximizing the local smoothness of colors. In this formulation, colorization is obtained by solving a large sparse linear system, which normally requires substantial computation and memory resources. Our algorithm improves the computational performance through three innovations over prior colorization implementations. First, the linear system is solved iteratively without explicitly constructing the sparse matrix, which significantly reduces the required memory. Second, we formulate each iteration in terms of integral images obtained by dynamic programming, reducing repetitive computation. Third, we use a coarseto- fine framework, where a lower resolution subsampled image is first colorized and this low resolution color image is upsampled to initialize the colorization process for the fine level. The improvements we develop provide significant speedup and memory savings compared to the conventional approach of solving the linear system directly using off-the-shelf sparse solvers, and allow us to colorize images with typical sizes encountered in realistic applications on typical commodity computing platforms.

The phonological neighbourhood effect on short-term memory for order.

PubMed

Clarkson, L; Roodenrys, S; Miller, L M; Hulme, C

2017-03-01

There is a growing body of literature that suggests that long-term memory (LTM) and short-term memory (STM) structures that were once thought to be distinct are actually co-dependent, and that LTM can aid retrieval from STM. The mechanism behind this effect is commonly argued to act on item memory but not on order memory. The aim of the current study was to examine whether LTM could exert an influence on STM for order by examining an effect attributed to LTM, the phonological neighbourhood effect, in a task that reduced the requirement to retain item information. In Experiment 1, 18 participants completed a serial reconstruction task where neighbourhood density alternated within the lists. In Experiment 2, 22 participants completed a serial reconstruction task using pure lists of dense and sparse neighbourhood words. In Experiment 3, 22 participants completed a reconstruction task with both mixed and pure lists. There was a significant effect of neighbourhood density with better recall for dense than sparse neighbourhood words in pure lists but not in mixed lists. Results suggest that LTM exerts an influence prior to that proposed by many models of memory for order.

The storage capacity of Potts models for semantic memory retrieval

NASA Astrophysics Data System (ADS)

Kropff, Emilio; Treves, Alessandro

2005-08-01

We introduce and analyse a minimal network model of semantic memory in the human brain. The model is a global associative memory structured as a collection of N local modules, each coding a feature, which can take S possible values, with a global sparseness a (the average fraction of features describing a concept). We show that, under optimal conditions, the number cM of modules connected on average to a module can range widely between very sparse connectivity (high dilution, c_{M}/N\\to 0 ) and full connectivity (c_{M}\\to N ), maintaining a global network storage capacity (the maximum number pc of stored and retrievable concepts) that scales like pc~cMS2/a, with logarithmic corrections consistent with the constraint that each synapse may store up to a fraction of a bit.

Paradeisos: A perfect hashing algorithm for many-body eigenvalue problems

NASA Astrophysics Data System (ADS)

Jia, C. J.; Wang, Y.; Mendl, C. B.; Moritz, B.; Devereaux, T. P.

2018-03-01

We describe an essentially perfect hashing algorithm for calculating the position of an element in an ordered list, appropriate for the construction and manipulation of many-body Hamiltonian, sparse matrices. Each element of the list corresponds to an integer value whose binary representation reflects the occupation of single-particle basis states for each element in the many-body Hilbert space. The algorithm replaces conventional methods, such as binary search, for locating the elements of the ordered list, eliminating the need to store the integer representation for each element, without increasing the computational complexity. Combined with the "checkerboard" decomposition of the Hamiltonian matrix for distribution over parallel computing environments, this leads to a substantial savings in aggregate memory. While the algorithm can be applied broadly to many-body, correlated problems, we demonstrate its utility in reducing total memory consumption for a series of fermionic single-band Hubbard model calculations on small clusters with progressively larger Hilbert space dimension.

Solving large tomographic linear systems: size reduction and error estimation

NASA Astrophysics Data System (ADS)

Voronin, Sergey; Mikesell, Dylan; Slezak, Inna; Nolet, Guust

2014-10-01

We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved.

Normalization for sparse encoding of odors by a wide-field interneuron.

PubMed

Papadopoulou, Maria; Cassenaer, Stijn; Nowotny, Thomas; Laurent, Gilles

2011-05-06

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Scenario generation for stochastic optimization problems via the sparse grid method

DOE PAGES

Chen, Michael; Mehrotra, Sanjay; Papp, David

2015-04-19

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid methodmore » can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.« less

A communication-avoiding, hybrid-parallel, rank-revealing orthogonalization method.

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

Hoemmen, Mark

2010-11-01

Orthogonalization consumes much of the run time of many iterative methods for solving sparse linear systems and eigenvalue problems. Commonly used algorithms, such as variants of Gram-Schmidt or Householder QR, have performance dominated by communication. Here, 'communication' includes both data movement between the CPU and memory, and messages between processors in parallel. Our Tall Skinny QR (TSQR) family of algorithms requires asymptotically fewer messages between processors and data movement between CPU and memory than typical orthogonalization methods, yet achieves the same accuracy as Householder QR factorization. Furthermore, in block orthogonalizations, TSQR is faster and more accurate than existing approaches formore » orthogonalizing the vectors within each block ('normalization'). TSQR's rank-revealing capability also makes it useful for detecting deflation in block iterative methods, for which existing approaches sacrifice performance, accuracy, or both. We have implemented a version of TSQR that exploits both distributed-memory and shared-memory parallelism, and supports real and complex arithmetic. Our implementation is optimized for the case of orthogonalizing a small number (5-20) of very long vectors. The shared-memory parallel component uses Intel's Threading Building Blocks, though its modular design supports other shared-memory programming models as well, including computation on the GPU. Our implementation achieves speedups of 2 times or more over competing orthogonalizations. It is available now in the development branch of the Trilinos software package, and will be included in the 10.8 release.« less

A low complexity reweighted proportionate affine projection algorithm with memory and row action projection

NASA Astrophysics Data System (ADS)

Liu, Jianming; Grant, Steven L.; Benesty, Jacob

2015-12-01

A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l 0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter's coefficients is combined with row action projections (RAP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed RPAPA MRAP algorithm outperforms both the affine projection algorithm (APA) and PAPA, and has performance similar to l 0 PAPA and mu-law PAPA, in terms of convergence speed and tracking ability. Meanwhile, the proposed RPAPA MRAP has much lower computational complexity than PAPA, mu-law PAPA, and l 0 PAPA, etc., which makes it very appealing for real-time implementation.

Efficient ICCG on a shared memory multiprocessor

NASA Technical Reports Server (NTRS)

Hammond, Steven W.; Schreiber, Robert

1989-01-01

Different approaches are discussed for exploiting parallelism in the ICCG (Incomplete Cholesky Conjugate Gradient) method for solving large sparse symmetric positive definite systems of equations on a shared memory parallel computer. Techniques for efficiently solving triangular systems and computing sparse matrix-vector products are explored. Three methods for scheduling the tasks in solving triangular systems are implemented on the Sequent Balance 21000. Sample problems that are representative of a large class of problems solved using iterative methods are used. We show that a static analysis to determine data dependences in the triangular solve can greatly improve its parallel efficiency. We also show that ignoring symmetry and storing the whole matrix can reduce solution time substantially.

Dense modifiable interconnections utilizing photorefractive volume holograms

NASA Astrophysics Data System (ADS)

Psaltis, Demetri; Qiao, Yong

1990-11-01

This report describes an experimental two-layer optical neural network built at Caltech. The system uses photorefractive volume holograms to implement dense, modifiable synaptic interconnections and liquid crystal light valves (LCVS) to perform nonlinear thresholding operations. Kanerva's Sparse, Distributed Memory was implemented using this network and its ability to recognize handwritten character-alphabet (A-Z) has been demonstrated experimentally. According to Kanerva's model, the first layer has fixed, random weights of interconnections and the second layer is trained by sum-of-outer-products rule. After training, the recognition rates of the network on the training set (104 patterns) and test set (520 patterns) are 100 and 50 percent, respectively.

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

Noise in Attractor Networks in the Brain Produced by Graded Firing Rate Representations

PubMed Central

Webb, Tristan J.; Rolls, Edmund T.; Deco, Gustavo; Feng, Jianfeng

2011-01-01

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry. PMID:21931607

Optogenetic stimulation of a hippocampal engram activates fear memory recall.

PubMed

Liu, Xu; Ramirez, Steve; Pang, Petti T; Puryear, Corey B; Govindarajan, Arvind; Deisseroth, Karl; Tonegawa, Susumu

2012-03-22

A specific memory is thought to be encoded by a sparse population of neurons. These neurons can be tagged during learning for subsequent identification and manipulation. Moreover, their ablation or inactivation results in reduced memory expression, suggesting their necessity in mnemonic processes. However, the question of sufficiency remains: it is unclear whether it is possible to elicit the behavioural output of a specific memory by directly activating a population of neurons that was active during learning. Here we show in mice that optogenetic reactivation of hippocampal neurons activated during fear conditioning is sufficient to induce freezing behaviour. We labelled a population of hippocampal dentate gyrus neurons activated during fear learning with channelrhodopsin-2 (ChR2) and later optically reactivated these neurons in a different context. The mice showed increased freezing only upon light stimulation, indicating light-induced fear memory recall. This freezing was not detected in non-fear-conditioned mice expressing ChR2 in a similar proportion of cells, nor in fear-conditioned mice with cells labelled by enhanced yellow fluorescent protein instead of ChR2. Finally, activation of cells labelled in a context not associated with fear did not evoke freezing in mice that were previously fear conditioned in a different context, suggesting that light-induced fear memory recall is context specific. Together, our findings indicate that activating a sparse but specific ensemble of hippocampal neurons that contribute to a memory engram is sufficient for the recall of that memory. Moreover, our experimental approach offers a general method of mapping cellular populations bearing memory engrams.

Optogenetic stimulation of a hippocampal engram activates fear memory recall

PubMed Central

Liu, Xu; Ramirez, Steve; Pang, Petti T.; Puryear, Corey B.; Govindarajan, Arvind; Deisseroth, Karl; Tonegawa, Susumu

2012-01-01

A specific memory is thought to be encoded by a sparse population of neurons1,2. These neurons can be tagged during learning for subsequent identification3 and manipulation4,5,6. Moreover, their ablation or inactivation results in reduced memory expression, suggesting their necessity in mnemonic processes. However, a critical question of sufficiency remains: can one elicit the behavioral output of a specific memory by directly activating a population of neurons that was active during learning? Here we show that optogenetic reactivation of hippocampal neurons activated during fear conditioning is sufficient to induce freezing behavior. We labeled a population of hippocampal dentate gyrus neurons activated during fear learning with channelrhodopsin-2 (ChR2)7,8 and later optically reactivated these neurons in a different context. The mice showed increased freezing only upon light stimulation, indicating light-induced fear memory recall. This freezing was not detected in non-fear conditioned mice expressing ChR2 in a similar proportion of cells, nor in fear conditioned mice with cells labeled by EYFP instead of ChR2. Finally, activation of cells labeled in a context not associated with fear did not evoke freezing in mice that were previously fear conditioned in a different context, suggesting that light-induced fear memory recall is context-specific. Together, our findings indicate that activating a sparse but specific ensemble of hippocampal neurons that contribute to a memory engram is sufficient for the recall of that memory. Moreover, our experimental approach offers a general method of mapping cellular populations bearing memory engrams. PMID:22441246

Run-time scheduling and execution of loops on message passing machines

NASA Technical Reports Server (NTRS)

Crowley, Kay; Saltz, Joel; Mirchandaney, Ravi; Berryman, Harry

1989-01-01

Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent.

Run-time scheduling and execution of loops on message passing machines

NASA Technical Reports Server (NTRS)

Saltz, Joel; Crowley, Kathleen; Mirchandaney, Ravi; Berryman, Harry

1990-01-01

Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent.

A distributed-memory approximation algorithm for maximum weight perfect bipartite matching

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

Azad, Ariful; Buluc, Aydin; Li, Xiaoye S.

We design and implement an efficient parallel approximation algorithm for the problem of maximum weight perfect matching in bipartite graphs, i.e. the problem of finding a set of non-adjacent edges that covers all vertices and has maximum weight. This problem differs from the maximum weight matching problem, for which scalable approximation algorithms are known. It is primarily motivated by finding good pivots in scalable sparse direct solvers before factorization where sequential implementations of maximum weight perfect matching algorithms, such as those available in MC64, are widely used due to the lack of scalable alternatives. To overcome this limitation, we proposemore » a fully parallel distributed memory algorithm that first generates a perfect matching and then searches for weightaugmenting cycles of length four in parallel and iteratively augments the matching with a vertex disjoint set of such cycles. For most practical problems the weights of the perfect matchings generated by our algorithm are very close to the optimum. An efficient implementation of the algorithm scales up to 256 nodes (17,408 cores) on a Cray XC40 supercomputer and can solve instances that are too large to be handled by a single node using the sequential algorithm.« less

Impact of the implementation of MPI point-to-point communications on the performance of two general sparse solvers

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

Amestoy, Patrick R.; Duff, Iain S.; L'Excellent, Jean-Yves

2001-10-10

We examine the mechanics of the send and receive mechanism of MPI and in particular how we can implement message passing in a robust way so that our performance is not significantly affected by changes to the MPI system. This leads us to using the Isend/Irecv protocol which will entail sometimes significant algorithmic changes. We discuss this within the context of two different algorithms for sparse Gaussian elimination that we have parallelized. One is a multifrontal solver called MUMPS, the other is a supernodal solver called SuperLU. Both algorithms are difficult to parallelize on distributed memory machines. Our initial strategiesmore » were based on simple MPI point-to-point communication primitives. With such approaches, the parallel performance of both codes are very sensitive to the MPI implementation, the way MPI internal buffers are used in particular. We then modified our codes to use more sophisticated nonblocking versions of MPI communication. This significantly improved the performance robustness (independent of the MPI buffering mechanism) and scalability, but at the cost of increased code complexity.« less

Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.

PubMed

Masuda, Y; Misztal, I; Legarra, A; Tsuruta, S; Lourenco, D A L; Fragomeni, B O; Aguilar, I

2017-01-01

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix () including genotyped animals and their ancestors. The elements of were rapidly calculated with the Henderson's rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix-vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.

The role of sleep spindles and slow-wave activity in integrating new information in semantic memory.

PubMed

Tamminen, Jakke; Lambon Ralph, Matthew A; Lewis, Penelope A

2013-09-25

Assimilating new information into existing knowledge is a fundamental part of consolidating new memories and allowing them to guide behavior optimally and is vital for conceptual knowledge (semantic memory), which is accrued over many years. Sleep is important for memory consolidation, but its impact upon assimilation of new information into existing semantic knowledge has received minimal examination. Here, we examined the integration process by training human participants on novel words with meanings that fell into densely or sparsely populated areas of semantic memory in two separate sessions. Overnight sleep was polysomnographically monitored after each training session and recall was tested immediately after training, after a night of sleep, and 1 week later. Results showed that participants learned equal numbers of both word types, thus equating amount and difficulty of learning across the conditions. Measures of word recognition speed showed a disadvantage for novel words in dense semantic neighborhoods, presumably due to interference from many semantically related concepts, suggesting that the novel words had been successfully integrated into semantic memory. Most critically, semantic neighborhood density influenced sleep architecture, with participants exhibiting more sleep spindles and slow-wave activity after learning the sparse compared with the dense neighborhood words. These findings provide the first evidence that spindles and slow-wave activity mediate integration of new information into existing semantic networks.

Short-term memory capacity in networks via the restricted isometry property.

PubMed

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Sparse grid techniques for particle-in-cell schemes

NASA Astrophysics Data System (ADS)

Ricketson, L. F.; Cerfon, A. J.

2017-02-01

We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

Parallel Finite Element Domain Decomposition for Structural/Acoustic Analysis

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.; Tungkahotara, Siroj; Watson, Willie R.; Rajan, Subramaniam D.

2005-01-01

A domain decomposition (DD) formulation for solving sparse linear systems of equations resulting from finite element analysis is presented. The formulation incorporates mixed direct and iterative equation solving strategics and other novel algorithmic ideas that are optimized to take advantage of sparsity and exploit modern computer architecture, such as memory and parallel computing. The most time consuming part of the formulation is identified and the critical roles of direct sparse and iterative solvers within the framework of the formulation are discussed. Experiments on several computer platforms using several complex test matrices are conducted using software based on the formulation. Small-scale structural examples are used to validate thc steps in the formulation and large-scale (l,000,000+ unknowns) duct acoustic examples are used to evaluate the ORIGIN 2000 processors, and a duster of 6 PCs (running under the Windows environment). Statistics show that the formulation is efficient in both sequential and parallel computing environmental and that the formulation is significantly faster and consumes less memory than that based on one of the best available commercialized parallel sparse solvers.

Matrix decomposition graphics processing unit solver for Poisson image editing

NASA Astrophysics Data System (ADS)

Lei, Zhao; Wei, Li

2012-10-01

In recent years, gradient-domain methods have been widely discussed in the image processing field, including seamless cloning and image stitching. These algorithms are commonly carried out by solving a large sparse linear system: the Poisson equation. However, solving the Poisson equation is a computational and memory intensive task which makes it not suitable for real-time image editing. A new matrix decomposition graphics processing unit (GPU) solver (MDGS) is proposed to settle the problem. A matrix decomposition method is used to distribute the work among GPU threads, so that MDGS will take full advantage of the computing power of current GPUs. Additionally, MDGS is a hybrid solver (combines both the direct and iterative techniques) and has two-level architecture. These enable MDGS to generate identical solutions with those of the common Poisson methods and achieve high convergence rate in most cases. This approach is advantageous in terms of parallelizability, enabling real-time image processing, low memory-taken and extensive applications.

Paradeisos: A perfect hashing algorithm for many-body eigenvalue problems

DOE PAGES

Jia, C. J.; Wang, Y.; Mendl, C. B.; ...

2017-12-02

Here, we describe an essentially perfect hashing algorithm for calculating the position of an element in an ordered list, appropriate for the construction and manipulation of many-body Hamiltonian, sparse matrices. Each element of the list corresponds to an integer value whose binary representation reflects the occupation of single-particle basis states for each element in the many-body Hilbert space. The algorithm replaces conventional methods, such as binary search, for locating the elements of the ordered list, eliminating the need to store the integer representation for each element, without increasing the computational complexity. Combined with the “checkerboard” decomposition of the Hamiltonian matrixmore » for distribution over parallel computing environments, this leads to a substantial savings in aggregate memory. While the algorithm can be applied broadly to many-body, correlated problems, we demonstrate its utility in reducing total memory consumption for a series of fermionic single-band Hubbard model calculations on small clusters with progressively larger Hilbert space dimension.« less

Sevoflurane-induced down-regulation of hippocampal oxytocin and arginine vasopressin impairs juvenile social behavioral abilities.

PubMed

Zhou, Zhi-Bin; Yang, Xiao-Yu; Yuan, Bao-Long; Niu, Li-Jun; Zhou, Xue; Huang, Wen-Qi; Feng, Xia; Zhou, Li-Hua

2015-05-01

Cumulative evidence indicates that early childhood anesthesia can alter a child's future behavioral performance. Animal researchers have found that sevoflurane, the most commonly used anesthetic for children, can produce damage in the neonatal brains of rodents. To further investigate this phenomenon, we focused on the influence of sevoflurane anesthesia on the development of juvenile social behavioral abilities and the pro-social proteins oxytocin (OT) and arginine vasopressin (AVP) in the neonatal hippocampus. A single 6-h sevoflurane exposure for postnatal day 5 mice resulted in decreased OT and AVP messenger RNA (mRNA) and protein levels in the hippocampus. OT and AVP proteins became sparsely distributed in the dorsal hippocampus after the exposure to sevoflurane. Compared with the air-treated group, mice in the sevoflurane-treated group showed signs of impairment in social recognition memory formation and social discrimination ability. Sevoflurane anesthesia reduces OT and AVP activities in the neonatal hippocampus and impairs social recognition memory formation and social discrimination ability in juvenile mice.

Paradeisos: A perfect hashing algorithm for many-body eigenvalue problems

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

Jia, C. J.; Wang, Y.; Mendl, C. B.

Here, we describe an essentially perfect hashing algorithm for calculating the position of an element in an ordered list, appropriate for the construction and manipulation of many-body Hamiltonian, sparse matrices. Each element of the list corresponds to an integer value whose binary representation reflects the occupation of single-particle basis states for each element in the many-body Hilbert space. The algorithm replaces conventional methods, such as binary search, for locating the elements of the ordered list, eliminating the need to store the integer representation for each element, without increasing the computational complexity. Combined with the “checkerboard” decomposition of the Hamiltonian matrixmore » for distribution over parallel computing environments, this leads to a substantial savings in aggregate memory. While the algorithm can be applied broadly to many-body, correlated problems, we demonstrate its utility in reducing total memory consumption for a series of fermionic single-band Hubbard model calculations on small clusters with progressively larger Hilbert space dimension.« less

The Use of Sparse Direct Solver in Vector Finite Element Modeling for Calculating Two Dimensional (2-D) Magnetotelluric Responses in Transverse Electric (TE) Mode

NASA Astrophysics Data System (ADS)

Yihaa Roodhiyah, Lisa’; Tjong, Tiffany; Nurhasan; Sutarno, D.

2018-04-01

The late research, linear matrices of vector finite element in two dimensional(2-D) magnetotelluric (MT) responses modeling was solved by non-sparse direct solver in TE mode. Nevertheless, there is some weakness which have to be improved especially accuracy in the low frequency (10-3 Hz-10-5 Hz) which is not achieved yet and high cost computation in dense mesh. In this work, the solver which is used is sparse direct solver instead of non-sparse direct solverto overcome the weaknesses of solving linear matrices of vector finite element metod using non-sparse direct solver. Sparse direct solver will be advantageous in solving linear matrices of vector finite element method because of the matrix properties which is symmetrical and sparse. The validation of sparse direct solver in solving linear matrices of vector finite element has been done for a homogen half-space model and vertical contact model by analytical solution. Thevalidation result of sparse direct solver in solving linear matrices of vector finite element shows that sparse direct solver is more stable than non-sparse direct solver in computing linear problem of vector finite element method especially in low frequency. In the end, the accuracy of 2D MT responses modelling in low frequency (10-3 Hz-10-5 Hz) has been reached out under the efficient allocation memory of array and less computational time consuming.

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

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

Xie, Huiqiao; Yang, Yi; Tang, Xiangyang

2015-06-15

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, whichmore » are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.« less

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring

NASA Astrophysics Data System (ADS)

Fiandrotti, Attilio; Fosson, Sophie M.; Ravazzi, Chiara; Magli, Enrico

2018-04-01

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.

Simultaneous analysis of large INTEGRAL/SPI1 datasets: Optimizing the computation of the solution and its variance using sparse matrix algorithms

NASA Astrophysics Data System (ADS)

Bouchet, L.; Amestoy, P.; Buttari, A.; Rouet, F.-H.; Chauvin, M.

2013-02-01

Nowadays, analyzing and reducing the ever larger astronomical datasets is becoming a crucial challenge, especially for long cumulated observation times. The INTEGRAL/SPI X/γ-ray spectrometer is an instrument for which it is essential to process many exposures at the same time in order to increase the low signal-to-noise ratio of the weakest sources. In this context, the conventional methods for data reduction are inefficient and sometimes not feasible at all. Processing several years of data simultaneously requires computing not only the solution of a large system of equations, but also the associated uncertainties. We aim at reducing the computation time and the memory usage. Since the SPI transfer function is sparse, we have used some popular methods for the solution of large sparse linear systems; we briefly review these methods. We use the Multifrontal Massively Parallel Solver (MUMPS) to compute the solution of the system of equations. We also need to compute the variance of the solution, which amounts to computing selected entries of the inverse of the sparse matrix corresponding to our linear system. This can be achieved through one of the latest features of the MUMPS software that has been partly motivated by this work. In this paper we provide a brief presentation of this feature and evaluate its effectiveness on astrophysical problems requiring the processing of large datasets simultaneously, such as the study of the entire emission of the Galaxy. We used these algorithms to solve the large sparse systems arising from SPI data processing and to obtain both their solutions and the associated variances. In conclusion, thanks to these newly developed tools, processing large datasets arising from SPI is now feasible with both a reasonable execution time and a low memory usage.

Novel Fourier-based iterative reconstruction for sparse fan projection using alternating direction total variation minimization

NASA Astrophysics Data System (ADS)

Zhao, Jin; Han-Ming, Zhang; Bin, Yan; Lei, Li; Lin-Yuan, Wang; Ai-Long, Cai

2016-03-01

Sparse-view x-ray computed tomography (CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform (NUFFT) is presented in this work along with advanced total variation (TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively. Projected supported by the National High Technology Research and Development Program of China (Grant No. 2012AA011603) and the National Natural Science Foundation of China (Grant No. 61372172).

Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data

DOT National Transportation Integrated Search

2018-02-02

Traffic congestion at arterial intersections and freeway bottlenecks degrades the air quality and threatens the public health. Conventionally, air pollutants are monitored by sparsely-distributed Quality Assurance Air Monitoring Sites. Sparse mobile ...

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2014-09-30

underwater acoustic communication technologies for autonomous distributed underwater networks , through innovative signal processing, coding, and...4. TITLE AND SUBTITLE Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and...coding: 3) OFDM modulated dynamic coded cooperation in underwater acoustic channels; 3 Localization, Networking , and Testbed: 4) On-demand

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

The fate of object memory traces under change detection and change blindness.

PubMed

Busch, Niko A

2013-07-03

Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Simulations inform design of regional occupancy-based monitoring for a sparsely distributed, territorial species

Treesearch

Quresh S. Latif; Martha M. Ellis; Victoria A. Saab; Kim Mellen-McLean

2017-01-01

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy-based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify...

Neurogenesis and pattern separation: time for a divorce.

PubMed

Becker, Suzanna

2017-05-01

The generation of new neurons in the adult mammalian brain has led to numerous theories as to their functional significance. One of the most widely held views is that adult neurogenesis promotes pattern separation, a process by which overlapping patterns of neural activation are mapped to less overlapping representations. While a large body of evidence supports a role for neurogenesis in high interference memory tasks, it does not support the proposed function of neurogenesis in mediating pattern separation. Instead, the adult-generated neurons seem to generate highly overlapping and yet distinct distributed representations for similar events. One way in which these immature, highly plastic, hyperactive neurons may contribute to novel memory formation while avoiding interference is by virtue of their extremely sparse connectivity with incoming perforant path fibers. Another intriguing proposal, awaiting empirical confirmation, is that the young neurons' recruitment into memory formation is gated by a novelty/mismatch mechanism mediated by CA3 or hilar back-projections. Ongoing research into the intriguing link between neurogenesis, stress-related mood disorders, and age-related neurodegeneration may lead to promising neurogenesis-based treatments for this wide range of clinical disorders. WIREs Cogn Sci 2017, 8:e1427. doi: 10.1002/wcs.1427 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Sparsely sampling the sky: Regular vs. random sampling

NASA Astrophysics Data System (ADS)

Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.

2015-09-01

Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.

Mojibake - The rehearsal of word fragments in verbal recall.

PubMed

Lange-Küttner, Christiane; Sykorova, Eva

2015-01-01

Theories of verbal rehearsal usually assume that whole words are being rehearsed. However, words consist of letter sequences, or syllables, or word onset-vowel-coda, amongst many other conceptualizations of word structure. A more general term is the 'grain size' of word units (Ziegler and Goswami, 2005). In the current study, a new method measured the quantitative percentage of correctly remembered word structure. The amount of letters in the correct letter sequence as per cent of word length was calculated, disregarding missing or added letters. A forced rehearsal was tested by repeating each memory list four times. We tested low frequency (LF) English words versus geographical (UK) town names to control for content. We also tested unfamiliar international (INT) non-words and names of international (INT) European towns to control for familiarity. An immediate versus distributed repetition was tested with a between-subject design. Participants responded with word fragments in their written recall especially when they had to remember unfamiliar words. While memory of whole words was sensitive to content, presentation distribution and individual sex and language differences, recall of word fragments was not. There was no trade-off between memory of word fragments with whole word recall during the repetition, instead also word fragments significantly increased. Moreover, while whole word responses correlated with each other during repetition, and word fragment responses correlated with each other during repetition, these two types of word recall responses were not correlated with each other. Thus there may be a lower layer consisting of free, sparse word fragments and an upper layer that consists of language-specific, orthographically and semantically constrained words.

Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

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

Wang, Felix; Quach, Tu-Thach; Wheeler, Jason

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used tomore » reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.« less

Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

DOE PAGES

Wang, Felix; Quach, Tu-Thach; Wheeler, Jason; ...

2018-04-05

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used tomore » reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.« less

Cortical rewiring and information storage

NASA Astrophysics Data System (ADS)

Chklovskii, D. B.; Mel, B. W.; Svoboda, K.

2004-10-01

Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical `wiring diagram'. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning.

Sparse Matrix Software Catalog, Sparse Matrix Symposium 1982, Fairfield Glade, Tennessee, October 24-27, 1982,

DTIC Science & Technology

1982-10-27

are buried within * a much larger, special purpose package. We regret such omissions, but to have reached the practi- tioners in each of the diverse...sparse matrix (form PAQ ) 4. Method of solution: Distribution count sort 5. Programming language: FORTRAN g Precision: Single and double precision 7

Investigation of wall-bounded turbulence over sparsely distributed roughness

NASA Astrophysics Data System (ADS)

Placidi, Marco; Ganapathisubramani, Bharath

2011-11-01

The effects of sparsely distributed roughness elements on the structure of a turbulent boundary layer are examined by performing a series of Particle Image Velocimetry (PIV) experiments in a wind tunnel. From the literature, the best way to characterise a rough wall, especially one where the density of roughness elements is sparse, is unclear. In this study, rough surfaces consisting of sparsely and uniformly distributed LEGO® blocks are used. Five different patterns are adopted in order to examine the effects of frontal solidity (λf, frontal area of the roughness elements per unit wall-parallel area), plan solidity (λp, plan area of roughness elements per unit wall-parallel area) and the geometry of the roughness element (square and cylindrical elements), on the turbulence structure. The Karman number, Reτ , has been matched, at the value of approximately 2300, in order to compare across the different cases. In the talk, we will present detailed analysis of mean and rms velocity profiles, Reynolds stresses and quadrant decomposition.

Communication requirements of sparse Cholesky factorization with nested dissection ordering

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1989-01-01

Load distribution schemes for minimizing the communication requirements of the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems are presented. The total data traffic in factoring an n x n sparse symmetric positive definite matrix representing an n-vertex regular two-dimensional grid graph using n exp alpha, alpha not greater than 1, processors are shown to be O(n exp 1 + alpha/2). It is O(n), when n exp alpha, alpha not smaller than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal.

Artificial neural network does better spatiotemporal compressive sampling

NASA Astrophysics Data System (ADS)

Lee, Soo-Young; Hsu, Charles; Szu, Harold

2012-06-01

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Mojibake – The rehearsal of word fragments in verbal recall

PubMed Central

Lange-Küttner, Christiane; Sykorova, Eva

2015-01-01

Theories of verbal rehearsal usually assume that whole words are being rehearsed. However, words consist of letter sequences, or syllables, or word onset-vowel-coda, amongst many other conceptualizations of word structure. A more general term is the ‘grain size’ of word units (Ziegler and Goswami, 2005). In the current study, a new method measured the quantitative percentage of correctly remembered word structure. The amount of letters in the correct letter sequence as per cent of word length was calculated, disregarding missing or added letters. A forced rehearsal was tested by repeating each memory list four times. We tested low frequency (LF) English words versus geographical (UK) town names to control for content. We also tested unfamiliar international (INT) non-words and names of international (INT) European towns to control for familiarity. An immediate versus distributed repetition was tested with a between-subject design. Participants responded with word fragments in their written recall especially when they had to remember unfamiliar words. While memory of whole words was sensitive to content, presentation distribution and individual sex and language differences, recall of word fragments was not. There was no trade-off between memory of word fragments with whole word recall during the repetition, instead also word fragments significantly increased. Moreover, while whole word responses correlated with each other during repetition, and word fragment responses correlated with each other during repetition, these two types of word recall responses were not correlated with each other. Thus there may be a lower layer consisting of free, sparse word fragments and an upper layer that consists of language-specific, orthographically and semantically constrained words. PMID:25941500

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

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

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

New shape models of asteroids reconstructed from sparse-in-time photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Vanco, Radim; Oszkiewicz, Dagmara Anna

2015-08-01

Asteroid physical parameters - the shape, the sidereal rotation period, and the spin axis orientation - can be reconstructed from the disk-integrated photometry either dense (classical lightcurves) or sparse in time by the lightcurve inversion method. We will review our recent progress in asteroid shape reconstruction from sparse photometry. The problem of finding a unique solution of the inverse problem is time consuming because the sidereal rotation period has to be found by scanning a wide interval of possible periods. This can be efficiently solved by splitting the period parameter space into small parts that are sent to computers of volunteers and processed in parallel. We will show how this approach of distributed computing works with currently available sparse photometry processed in the framework of project Asteroids@home. In particular, we will show the results based on the Lowell Photometric Database. The method produce reliable asteroid models with very low rate of false solutions and the pipelines and codes can be directly used also to other sources of sparse photometry - Gaia data, for example. We will present the distribution of spin axis of hundreds of asteroids, discuss the dependence of the spin obliquity on the size of an asteroid,and show examples of spin-axis distribution in asteroid families that confirm the Yarkovsky/YORP evolution scenario.

A Method for Optimizing Non-Axisymmetric Liners for Multimodal Sound Sources

NASA Technical Reports Server (NTRS)

Watson, W. R.; Jones, M. G.; Parrott, T. L.; Sobieski, J.

2002-01-01

Central processor unit times and memory requirements for a commonly used solver are compared to that of a state-of-the-art, parallel, sparse solver. The sparse solver is then used in conjunction with three constrained optimization methodologies to assess the relative merits of non-axisymmetric versus axisymmetric liner concepts for improving liner acoustic suppression. This assessment is performed with a multimodal noise source (with equal mode amplitudes and phases) in a finite-length rectangular duct without flow. The sparse solver is found to reduce memory requirements by a factor of five and central processing time by a factor of eleven when compared with the commonly used solver. Results show that the optimum impedance of the uniform liner is dominated by the least attenuated mode, whose attenuation is maximized by the Cremer optimum impedance. An optimized, four-segmented liner with impedance segments in a checkerboard arrangement is found to be inferior to an optimized spanwise segmented liner. This optimized spanwise segmented liner is shown to attenuate substantially more sound than the optimized uniform liner and tends to be more effective at the higher frequencies. The most important result of this study is the discovery that when optimized, a spanwise segmented liner with two segments gives attenuations equal to or substantially greater than an optimized axially segmented liner with the same number of segments.

But: Do Age and Working Memory Influence Conventional Implicature Processing?

ERIC Educational Resources Information Center

Janssens, Leen; Drooghmans, Stephanie; Schaeken, Walter

2015-01-01

Conventional implicatures are omnipresent in daily life communication but experimental research on this topic is sparse, especially research with children. The aim of this study was to investigate if eight- to twelve-year-old children spontaneously make the conventional implicature induced by "but," "so," and…

Network, cellular, and molecular mechanisms underlying long-term memory formation.

PubMed

Carasatorre, Mariana; Ramírez-Amaya, Víctor

2013-01-01

The neural network stores information through activity-dependent synaptic plasticity that occurs in populations of neurons. Persistent forms of synaptic plasticity may account for long-term memory storage, and the most salient forms are the changes in the structure of synapses. The theory proposes that encoding should use a sparse code and evidence suggests that this can be achieved through offline reactivation or by sparse initial recruitment of the network units. This idea implies that in some cases the neurons that underwent structural synaptic plasticity might be a subpopulation of those originally recruited; However, it is not yet clear whether all the neurons recruited during acquisition are the ones that underwent persistent forms of synaptic plasticity and responsible for memory retrieval. To determine which neural units underlie long-term memory storage, we need to characterize which are the persistent forms of synaptic plasticity occurring in these neural ensembles and the best hints so far are the molecular signals underlying structural modifications of the synapses. Structural synaptic plasticity can be achieved by the activity of various signal transduction pathways, including the NMDA-CaMKII and ACh-MAPK. These pathways converge with the Rho family of GTPases and the consequent ERK 1/2 activation, which regulates multiple cellular functions such as protein translation, protein trafficking, and gene transcription. The most detailed explanation may come from models that allow us to determine the contribution of each piece of this fascinating puzzle that is the neuron and the neural network.

Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

NASA Astrophysics Data System (ADS)

Zheng, Maoteng; Zhang, Yongjun; Zhou, Shunping; Zhu, Junfeng; Xiong, Xiaodong

2016-07-01

In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data.

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

The application of nonlinear programming and collocation to optimal aeroassisted orbital transfers

NASA Astrophysics Data System (ADS)

Shi, Y. Y.; Nelson, R. L.; Young, D. H.; Gill, P. E.; Murray, W.; Saunders, M. A.

1992-01-01

Sequential quadratic programming (SQP) and collocation of the differential equations of motion were applied to optimal aeroassisted orbital transfers. The Optimal Trajectory by Implicit Simulation (OTIS) computer program codes with updated nonlinear programming code (NZSOL) were used as a testbed for the SQP nonlinear programming (NLP) algorithms. The state-of-the-art sparse SQP method is considered to be effective for solving large problems with a sparse matrix. Sparse optimizers are characterized in terms of memory requirements and computational efficiency. For the OTIS problems, less than 10 percent of the Jacobian matrix elements are nonzero. The SQP method encompasses two phases: finding an initial feasible point by minimizing the sum of infeasibilities and minimizing the quadratic objective function within the feasible region. The orbital transfer problem under consideration involves the transfer from a high energy orbit to a low energy orbit.

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

An efficient implementation of a high-order filter for a cubed-sphere spectral element model

NASA Astrophysics Data System (ADS)

Kang, Hyun-Gyu; Cheong, Hyeong-Bin

2017-03-01

A parallel-scalable, isotropic, scale-selective spatial filter was developed for the cubed-sphere spectral element model on the sphere. The filter equation is a high-order elliptic (Helmholtz) equation based on the spherical Laplacian operator, which is transformed into cubed-sphere local coordinates. The Laplacian operator is discretized on the computational domain, i.e., on each cell, by the spectral element method with Gauss-Lobatto Lagrange interpolating polynomials (GLLIPs) as the orthogonal basis functions. On the global domain, the discrete filter equation yielded a linear system represented by a highly sparse matrix. The density of this matrix increases quadratically (linearly) with the order of GLLIP (order of the filter), and the linear system is solved in only O (Ng) operations, where Ng is the total number of grid points. The solution, obtained by a row reduction method, demonstrated the typical accuracy and convergence rate of the cubed-sphere spectral element method. To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix-vector multiplication). The density of the inverse matrix was lowered to only a few times of the original sparse matrix without degrading the accuracy of the solution. For better computational efficiency, a local-domain high-order filter was introduced: The filter equation is applied to multiple cells, and then the central cell was only used to reconstruct the filtered field. The parallel efficiency of applying the inverse matrix method to the global- and local-domain filter was evaluated by the scalability on a distributed-memory parallel computer. The scale-selective performance of the filter was demonstrated on Earth topography. The usefulness of the filter as a hyper-viscosity for the vorticity equation was also demonstrated.

Representation of memories in the cortical-hippocampal system: Results from the application of population similarity analyses

PubMed Central

McKenzie, Sam; Keene, Chris; Farovik, Anja; Blandon, John; Place, Ryan; Komorowski, Robert; Eichenbaum, Howard

2016-01-01

Here we consider the value of neural population analysis as an approach to understanding how information is represented in the hippocampus and cortical areas and how these areas might interact as a brain system to support memory. We argue that models based on sparse coding of different individual features by single neurons in these areas (e.g., place cells, grid cells) are inadequate to capture the complexity of experience represented within this system. By contrast, population analyses of neurons with denser coding and mixed selectivity reveal new and important insights into the organization of memories. Furthermore, comparisons of the organization of information in interconnected areas suggest a model of hippocampal-cortical interactions that mediates the fundamental features of memory. PMID:26748022

Functional fixedness in a technologically sparse culture.

PubMed

German, Tim P; Barrett, H Clark

2005-01-01

Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.

Return probabilities and hitting times of random walks on sparse Erdös-Rényi graphs.

PubMed

Martin, O C; Sulc, P

2010-03-01

We consider random walks on random graphs, focusing on return probabilities and hitting times for sparse Erdös-Rényi graphs. Using the tree approach, which is expected to be exact in the large graph limit, we show how to solve for the distribution of these quantities and we find that these distributions exhibit a form of self-similarity.

Distributed Compressive Sensing

DTIC Science & Technology

2009-01-01

example, smooth signals are sparse in the Fourier basis, and piecewise smooth signals are sparse in a wavelet basis [8]; the commercial coding standards MP3...including wavelets [8], Gabor bases [8], curvelets [35], etc., are widely used for representation and compression of natural signals, images, and...spikes and the sine waves of a Fourier basis, or the Fourier basis and wavelets . Signals that are sparsely represented in frames or unions of bases can

Optimization of Ferroelectric Ceramics by Design at the Microstructure Level

NASA Astrophysics Data System (ADS)

Jayachandran, K. P.; Guedes, J. M.; Rodrigues, H. C.

2010-05-01

Ferroelectric materials show remarkable physical behaviors that make them essential for many devices and have been extensively studied for their applications of nonvolatile random access memory (NvRAM) and high-speed random access memories. Although ferroelectric ceramics (polycrystals) present ease in manufacture and in compositional modifications and represent the widest application area of materials, computational and theoretical studies are sparse owing to many reasons including the large number of constituent atoms. Macroscopic properties of ferroelectric polycrystals are dominated by the inhomogeneities at the crystallographic domain/grain level. Orientation of grains/domains is critical to the electromechanical response of the single crystalline and polycrystalline materials. Polycrystalline materials have the potential of exhibiting better performance at a macroscopic scale by design of the domain/grain configuration at the domain-size scale. This suggests that piezoelectric properties can be optimized by a proper choice of the parameters which control the distribution of grain orientations. Nevertheless, this choice is complicated and it is impossible to analyze all possible combinations of the distribution parameters or the angles themselves. Hence we have implemented the stochastic optimization technique of simulated annealing combined with the homogenization for the optimization problem. The mathematical homogenization theory of a piezoelectric medium is implemented in the finite element method (FEM) by solving the coupled equilibrium electrical and mechanical fields. This implementation enables the study of the dependence of the macroscopic electromechanical properties of a typical crystalline and polycrystalline ferroelectric ceramic on the grain orientation.

A Performance Comparison of the Parallel Preconditioners for Iterative Methods for Large Sparse Linear Systems Arising from Partial Differential Equations on Structured Grids

NASA Astrophysics Data System (ADS)

Ma, Sangback

In this paper we compare various parallel preconditioners such as Point-SSOR (Symmetric Successive OverRelaxation), ILU(0) (Incomplete LU) in the Wavefront ordering, ILU(0) in the Multi-color ordering, Multi-Color Block SOR (Successive OverRelaxation), SPAI (SParse Approximate Inverse) and pARMS (Parallel Algebraic Recursive Multilevel Solver) for solving large sparse linear systems arising from two-dimensional PDE (Partial Differential Equation)s on structured grids. Point-SSOR is well-known, and ILU(0) is one of the most popular preconditioner, but it is inherently serial. ILU(0) in the Wavefront ordering maximizes the parallelism in the natural order, but the lengths of the wave-fronts are often nonuniform. ILU(0) in the Multi-color ordering is a simple way of achieving a parallelism of the order N, where N is the order of the matrix, but its convergence rate often deteriorates as compared to that of natural ordering. We have chosen the Multi-Color Block SOR preconditioner combined with direct sparse matrix solver, since for the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with the Multi-Color ordering. By using block version we expect to minimize the interprocessor communications. SPAI computes the sparse approximate inverse directly by least squares method. Finally, ARMS is a preconditioner recursively exploiting the concept of independent sets and pARMS is the parallel version of ARMS. Experiments were conducted for the Finite Difference and Finite Element discretizations of five two-dimensional PDEs with large meshsizes up to a million on an IBM p595 machine with distributed memory. Our matrices are real positive, i. e., their real parts of the eigenvalues are positive. We have used GMRES(m) as our outer iterative method, so that the convergence of GMRES(m) for our test matrices are mathematically guaranteed. Interprocessor communications were done using MPI (Message Passing Interface) primitives. The results show that in general ILU(0) in the Multi-Color ordering ahd ILU(0) in the Wavefront ordering outperform the other methods but for symmetric and nearly symmetric 5-point matrices Multi-Color Block SOR gives the best performance, except for a few cases with a small number of processors.

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

Advancing Cost-Effective Readiness by Improving the Supply Chain Management of Sparse, Intermittently-Demanded Parts

DTIC Science & Technology

2015-03-26

DEMANDED PARTS DISSERTATION Gregory H. Gehret AFIT-ENS-DS-15-M- 256 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE...protection in the United States. AFIT-ENS-DS-15-M- 256 ADVANCING COST-EFFECTIVE READINESS BY IMPROVING THE SUPPLY CHAIN MANAGEMENT OF SPARSE...RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENS-DS-15-M- 256 ADVANCING COST-EFFECTIVE READINESS BY IMPROVING THE SUPPLY CHAIN MANAGEMENT OF SPARSE

Intrusion Detection and Forensics for Self-Defending Wireless Networks

DTIC Science & Technology

2012-12-01

ICNP), Nov. 2007. 5. Yao Zhao, Yan Chen, Bo Li, and Qian Zhang, Hop ID: A Virtual Coordinate based Routing for Sparse Mobile Ad Hoc Networks, in...Liu, Hongbo Zhao, Kai Chen and Yan Chen, " DISCO : Memory Efficient and Accurate Flow Statistics for Network Measurement", in the Proc. of IEEE ICDCS

Shields for Enhanced Protection Against High-Speed Debris

NASA Technical Reports Server (NTRS)

Christiansen, Eric L.; Kerr, Justin H.

2003-01-01

A report describes improvements over the conventional Whipple shield (two thin, spaced aluminum walls) for protecting spacecraft against high-speed impacts of orbiting debris. The debris in question arise mainly from breakup of older spacecraft. The improved shields include exterior bumper layers composed of hybrid fabrics woven from combinations of ceramic fibers and high-density metallic wires or, alternatively, completely metallic outer layers composed of high-strength steel or copper wires. These shields are designed to be light in weight, yet capable of protecting against orbital debris with mass densities up to about 9 g/cm3, without generating damaging secondary debris particles. As yet another design option, improved shields can include sparsely distributed wires made of shape-memory metals that can be thermally activated from compact storage containers to form shields of predetermined shape upon arrival in orbit. The improved shields could also be used to augment shields installed previously.

Shields for Enhanced Protection Against High-Speed Debris

NASA Technical Reports Server (NTRS)

Christiansen, Eric L.; Kerr, Justin H.

2003-01-01

A report describes improvements over the conventional Whipple shield (two thin, spaced aluminum walls) for protecting spacecraft against high-speed impacts of orbiting debris. The debris in question arises mainly from breakup of older spacecraft. The improved shields include exterior "bumper" layers composed of hybrid fabrics woven from combinations of ceramic fibers and high-density metallic wires or, alternatively, completely metallic outer layers composed of high-strength steel or copper wires. These shields are designed to be light in weight, yet capable of protecting against orbital debris with mass densities up to about 9 g/cubic cm, without generating damaging secondary debris particles. As yet another design option, improved shields can include sparsely distributed wires made of shape memory metals that can be thermally activated from compact storage containers to form shields of predetermined shape upon arrival in orbit. The improved shields could also be used to augment shields installed previously.

The w-effect in interferometric imaging: from a fast sparse measurement operator to superresolution

NASA Astrophysics Data System (ADS)

Dabbech, A.; Wolz, L.; Pratley, L.; McEwen, J. D.; Wiaux, Y.

2017-11-01

Modern radio telescopes, such as the Square Kilometre Array, will probe the radio sky over large fields of view, which results in large w-modulations of the sky image. This effect complicates the relationship between the measured visibilities and the image under scrutiny. In algorithmic terms, it gives rise to massive memory and computational time requirements. Yet, it can be a blessing in terms of reconstruction quality of the sky image. In recent years, several works have shown that large w-modulations promote the spread spectrum effect. Within the compressive sensing framework, this effect increases the incoherence between the sensing basis and the sparsity basis of the signal to be recovered, leading to better estimation of the sky image. In this article, we revisit the w-projection approach using convex optimization in realistic settings, where the measurement operator couples the w-terms in Fourier and the de-gridding kernels. We provide sparse, thus fast, models of the Fourier part of the measurement operator through adaptive sparsification procedures. Consequently, memory requirements and computational cost are significantly alleviated at the expense of introducing errors on the radio interferometric data model. We present a first investigation of the impact of the sparse variants of the measurement operator on the image reconstruction quality. We finally analyse the interesting superresolution potential associated with the spread spectrum effect of the w-modulation, and showcase it through simulations. Our c++ code is available online on GitHub.

Laplace-domain waveform modeling and inversion for the 3D acoustic-elastic coupled media

NASA Astrophysics Data System (ADS)

Shin, Jungkyun; Shin, Changsoo; Calandra, Henri

2016-06-01

Laplace-domain waveform inversion reconstructs long-wavelength subsurface models by using the zero-frequency component of damped seismic signals. Despite the computational advantages of Laplace-domain waveform inversion over conventional frequency-domain waveform inversion, an acoustic assumption and an iterative matrix solver have been used to invert 3D marine datasets to mitigate the intensive computing cost. In this study, we develop a Laplace-domain waveform modeling and inversion algorithm for 3D acoustic-elastic coupled media by using a parallel sparse direct solver library (MUltifrontal Massively Parallel Solver, MUMPS). We precisely simulate a real marine environment by coupling the 3D acoustic and elastic wave equations with the proper boundary condition at the fluid-solid interface. In addition, we can extract the elastic properties of the Earth below the sea bottom from the recorded acoustic pressure datasets. As a matrix solver, the parallel sparse direct solver is used to factorize the non-symmetric impedance matrix in a distributed memory architecture and rapidly solve the wave field for a number of shots by using the lower and upper matrix factors. Using both synthetic datasets and real datasets obtained by a 3D wide azimuth survey, the long-wavelength component of the P-wave and S-wave velocity models is reconstructed and the proposed modeling and inversion algorithm are verified. A cluster of 80 CPU cores is used for this study.

Scavengers on the move: behavioural changes in foraging search patterns during the annual cycle.

PubMed

López-López, Pascual; Benavent-Corai, José; García-Ripollés, Clara; Urios, Vicente

2013-01-01

Optimal foraging theory predicts that animals will tend to maximize foraging success by optimizing search strategies. However, how organisms detect sparsely distributed food resources remains an open question. When targets are sparse and unpredictably distributed, a Lévy strategy should maximize foraging success. By contrast, when resources are abundant and regularly distributed, simple brownian random movement should be sufficient. Although very different groups of organisms exhibit Lévy motion, the shift from a Lévy to a brownian search strategy has been suggested to depend on internal and external factors such as sex, prey density, or environmental context. However, animal response at the individual level has received little attention. We used GPS satellite-telemetry data of Egyptian vultures Neophron percnopterus to examine movement patterns at the individual level during consecutive years, with particular interest in the variations in foraging search patterns during the different periods of the annual cycle (i.e. breeding vs. non-breeding). Our results show that vultures followed a brownian search strategy in their wintering sojourn in Africa, whereas they exhibited a more complex foraging search pattern at breeding grounds in Europe, including Lévy motion. Interestingly, our results showed that individuals shifted between search strategies within the same period of the annual cycle in successive years. Results could be primarily explained by the different environmental conditions in which foraging activities occur. However, the high degree of behavioural flexibility exhibited during the breeding period in contrast to the non-breeding period is challenging, suggesting that not only environmental conditions explain individuals' behaviour but also individuals' cognitive abilities (e.g., memory effects) could play an important role. Our results support the growing awareness about the role of behavioural flexibility at the individual level, adding new empirical evidence about how animals in general, and particularly scavengers, solve the problem of efficiently finding food resources.

Memory Effects on Movement Behavior in Animal Foraging

PubMed Central

Bracis, Chloe; Gurarie, Eliezer; Van Moorter, Bram; Goodwin, R. Andrew

2015-01-01

An individual’s choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems. PMID:26288228

Memory Effects on Movement Behavior in Animal Foraging.

PubMed

Bracis, Chloe; Gurarie, Eliezer; Van Moorter, Bram; Goodwin, R Andrew

2015-01-01

An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.

Eigensolver for a Sparse, Large Hermitian Matrix

NASA Technical Reports Server (NTRS)

Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris

2003-01-01

A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).

Semi-automatic sparse preconditioners for high-order finite element methods on non-uniform meshes

NASA Astrophysics Data System (ADS)

Austin, Travis M.; Brezina, Marian; Jamroz, Ben; Jhurani, Chetan; Manteuffel, Thomas A.; Ruge, John

2012-05-01

High-order finite elements often have a higher accuracy per degree of freedom than the classical low-order finite elements. However, in the context of implicit time-stepping methods, high-order finite elements present challenges to the construction of efficient simulations due to the high cost of inverting the denser finite element matrix. There are many cases where simulations are limited by the memory required to store the matrix and/or the algorithmic components of the linear solver. We are particularly interested in preconditioned Krylov methods for linear systems generated by discretization of elliptic partial differential equations with high-order finite elements. Using a preconditioner like Algebraic Multigrid can be costly in terms of memory due to the need to store matrix information at the various levels. We present a novel method for defining a preconditioner for systems generated by high-order finite elements that is based on a much sparser system than the original high-order finite element system. We investigate the performance for non-uniform meshes on a cube and a cubed sphere mesh, showing that the sparser preconditioner is more efficient and uses significantly less memory. Finally, we explore new methods to construct the sparse preconditioner and examine their effectiveness for non-uniform meshes. We compare results to a direct use of Algebraic Multigrid as a preconditioner and to a two-level additive Schwarz method.

Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.

PubMed

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P; Gee, James C

2009-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.

Automatic Correction of Intensity Nonuniformity from Sparseness of Gradient Distribution in Medical Images

PubMed Central

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P.; Gee, James C.

2013-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities. PMID:20426191

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

Ridit Analysis for Cooper-Harper and Other Ordinal Ratings for Sparse Data - A Distance-based Approach

DTIC Science & Technology

2016-09-01

is to fit empirical Beta distributions to observed data, and then to use a randomization approach to make inferences on the difference between...a Ridit analysis on the often sparse data sets in many Flying Qualities applicationsi. The method of this paper is to fit empirical Beta ...One such measure is the discrete- probability-distribution version of the (squared) ‘Hellinger Distance’ (Yang & Le Cam , 2000) 2(, ) = 1

The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging

PubMed Central

Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2017-01-01

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach. PMID:28604583

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

PubMed Central

Guo, Qijia; Wang, Jie; Chang, Tianying

2017-01-01

The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively. PMID:29113083

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition.

PubMed

Zhang, Shanwen; Wu, Xiaowei; You, Zhuhong

2017-01-01

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.

Greedy Sparse Approaches for Homological Coverage in Location Unaware Sensor Networks

DTIC Science & Technology

2017-12-08

GlobalSIP); 2013 Dec; Austin , TX . p. 595– 598. 33. Farah C, Schwaner F, Abedi A, Worboys M. Distributed homology algorithm to detect topological events...ARL-TR-8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence...8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence J Moore

Over-Distribution in Source Memory

PubMed Central

Brainerd, C. J.; Reyna, V. F.; Holliday, R. E.; Nakamura, K.

2012-01-01

Semantic false memories are confounded with a second type of error, over-distribution, in which items are attributed to contradictory episodic states. Over-distribution errors have proved to be more common than false memories when the two are disentangled. We investigated whether over-distribution is prevalent in another classic false memory paradigm: source monitoring. It is. Conventional false memory responses (source misattributions) were predominantly over-distribution errors, but unlike semantic false memory, over-distribution also accounted for more than half of true memory responses (correct source attributions). Experimental control of over-distribution was achieved via a series of manipulations that affected either recollection of contextual details or item memory (concreteness, frequency, list-order, number of presentation contexts, and individual differences in verbatim memory). A theoretical model was used to analyze the data (conjoint process dissociation) that predicts that predicts that (a) over-distribution is directly proportional to item memory but inversely proportional to recollection and (b) item memory is not a necessary precondition for recollection of contextual details. The results were consistent with both predictions. PMID:21942494

Learners misperceive the benefits of redundant text in multimedia learning.

PubMed

Fenesi, Barbara; Kim, Joseph A

2014-01-01

Research on metacognition has consistently demonstrated that learners fail to endorse instructional designs that produce benefits to memory, and often prefer designs that actually impair comprehension. Unlike previous studies in which learners were only exposed to a single multimedia design, the current study used a within-subjects approach to examine whether exposure to both redundant text and non-redundant text multimedia presentations improved learners' metacognitive judgments about presentation styles that promote better understanding. A redundant text multimedia presentation containing narration paired with verbatim on-screen text (Redundant) was contrasted with two non-redundant text multimedia presentations: (1) narration paired with images and minimal text (Complementary) or (2) narration paired with minimal text (Sparse). Learners watched presentation pairs of either Redundant + Complementary, or Redundant + Sparse. Results demonstrate that Complementary and Sparse presentations produced highest overall performance on the final comprehension assessment, but the Redundant presentation produced highest perceived understanding and engagement ratings. These findings suggest that learners misperceive the benefits of redundant text, even after direct exposure to a non-redundant, effective presentation.

Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso.

PubMed

Liu, Xiaoli; Goncalves, André R; Cao, Peng; Zhao, Dazhe; Banerjee, Arindam

2018-06-01

Alzheimer's disease (AD) is a severe neurodegenerative disorder characterized by loss of memory and reduction in cognitive functions due to progressive degeneration of neurons and their connections, eventually leading to death. In this paper, we consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative). To solve the problem, we present a multi-task sparse group lasso (MT-SGL) framework, which estimates sparse features coupled across tasks, and can work with loss functions associated with any Generalized Linear Models. Through comparisons with a variety of baseline models using multiple evaluation metrics, we illustrate the promising predictive performance of MT-SGL on ADNI along with its ability to identify brain regions more likely to help the characterization Alzheimer's disease progression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Augmented l1 and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm. Revision 1

DTIC Science & Technology

2012-10-17

nonzero and sampled from the standard Gaussian distribution (for Figure 2) or the Bernoulli distribution (for Figure 3). Both tests had the same sensing...dual variable y(k) Figure 3: Convergence of primal and dual variables of three algorithms on Bernoulli sparse x0 was the slowest. Besides the obvious...slower convergence than the final stage. Comparing the results of two tests, the convergence was faster on the Bernoulli sparse signal than the

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

DOE PAGES

Li, Ruipeng; Saad, Yousef

2017-08-01

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

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

Li, Ruipeng; Saad, Yousef

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Objective sea level pressure analysis for sparse data areas

NASA Technical Reports Server (NTRS)

Druyan, L. M.

1972-01-01

A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.

Large-Scale Cubic-Scaling Random Phase Approximation Correlation Energy Calculations Using a Gaussian Basis.

PubMed

Wilhelm, Jan; Seewald, Patrick; Del Ben, Mauro; Hutter, Jürg

2016-12-13

We present an algorithm for computing the correlation energy in the random phase approximation (RPA) in a Gaussian basis requiring [Formula: see text] operations and [Formula: see text] memory. The method is based on the resolution of the identity (RI) with the overlap metric, a reformulation of RI-RPA in the Gaussian basis, imaginary time, and imaginary frequency integration techniques, and the use of sparse linear algebra. Additional memory reduction without extra computations can be achieved by an iterative scheme that overcomes the memory bottleneck of canonical RPA implementations. We report a massively parallel implementation that is the key for the application to large systems. Finally, cubic-scaling RPA is applied to a thousand water molecules using a correlation-consistent triple-ζ quality basis.

BI-sparsity pursuit for robust subspace recovery

DOE PAGES

Bian, Xiao; Krim, Hamid

2015-09-01

Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness ofmore » our method by experiments on real-world vision data.« less

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

PubMed

Hugelier, Siewert; Piqueras, Sara; Bedia, Carmen; de Juan, Anna; Ruckebusch, Cyril

2018-02-13

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L 0 -norm penalty is applied. This L 0 -norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model. Copyright © 2017 Elsevier B.V. All rights reserved.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Perceptually controlled doping for audio source separation

NASA Astrophysics Data System (ADS)

Mahé, Gaël; Nadalin, Everton Z.; Suyama, Ricardo; Romano, João MT

2014-12-01

The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a `doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

EIT Imaging Regularization Based on Spectral Graph Wavelets.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

2017-09-01

The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

Spontaneous recovery of memory functions in an untreated case of anti NMDAR encephalitis - a reason to maintain hope.

PubMed

McIvor, Katherine; Moore, Perry

2017-01-01

Anti N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an autoimmune disorder that was only fully discovered recently and neuropsychological outcome data remains sparse. We present the case of BA, a 19-year-old male, which illustrates the cognitive outcome in an untreated case over a time period of over 2½ years. We conducted three cognitive assessments, including tests of memory and executive functioning, over this time period and considered the evidence for reliable change in memory function using the Wechsler Advanced Clinical Solutions (ACS) serial assessment package. Our findings revealed mild memory problems 6 months post-discharge with, at best, static and potentially declining memory functioning at follow-up assessment 12 months post-discharge. However, the results of testing at 30 months post-discharge revealed significant improvements in immediate and delayed memory index performances. Our report of a case of anti-NMDAR encephalitis provides evidence for spontaneous improvements in memory functioning occurring more than 2 years after initial assessment and also demonstrates both the utility and potential limitations of the ACS serial assessment software when used in a relatively typical clinical assessment situation.

GPU color space conversion

NASA Astrophysics Data System (ADS)

Chase, Patrick; Vondran, Gary

2011-01-01

Tetrahedral interpolation is commonly used to implement continuous color space conversions from sparse 3D and 4D lookup tables. We investigate the implementation and optimization of tetrahedral interpolation algorithms for GPUs, and compare to the best known CPU implementations as well as to a well known GPU-based trilinear implementation. We show that a 500 NVIDIA GTX-580 GPU is 3x faster than a 1000 Intel Core i7 980X CPU for 3D interpolation, and 9x faster for 4D interpolation. Performance-relevant GPU attributes are explored including thread scheduling, local memory characteristics, global memory hierarchy, and cache behaviors. We consider existing tetrahedral interpolation algorithms and tune based on the structure and branching capabilities of current GPUs. Global memory performance is improved by reordering and expanding the lookup table to ensure optimal access behaviors. Per multiprocessor local memory is exploited to implement optimally coalesced global memory accesses, and local memory addressing is optimized to minimize bank conflicts. We explore the impacts of lookup table density upon computation and memory access costs. Also presented are CPU-based 3D and 4D interpolators, using SSE vector operations that are faster than any previously published solution.

EPR oximetry in three spatial dimensions using sparse spin distribution

NASA Astrophysics Data System (ADS)

Som, Subhojit; Potter, Lee C.; Ahmad, Rizwan; Vikram, Deepti S.; Kuppusamy, Periannan

2008-08-01

A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field. Data processing exploits the spatial sparseness of spins to detect voxels with nonzero spin and to estimate the spectral linewidth for those voxels. The parsimonious representation of spin locations and linewidths permits an order of magnitude reduction in data acquisition time, compared to four-dimensional tomographic reconstruction using traditional spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa- n-butoxy naphthalocyanine (LiNc-BuO) probe using an L-band EPR spectrometer.

Efficient parallel linear scaling construction of the density matrix for Born-Oppenheimer molecular dynamics.

PubMed

Mniszewski, S M; Cawkwell, M J; Wall, M E; Mohd-Yusof, J; Bock, N; Germann, T C; Niklasson, A M N

2015-10-13

We present an algorithm for the calculation of the density matrix that for insulators scales linearly with system size and parallelizes efficiently on multicore, shared memory platforms with small and controllable numerical errors. The algorithm is based on an implementation of the second-order spectral projection (SP2) algorithm [ Niklasson, A. M. N. Phys. Rev. B 2002 , 66 , 155115 ] in sparse matrix algebra with the ELLPACK-R data format. We illustrate the performance of the algorithm within self-consistent tight binding theory by total energy calculations of gas phase poly(ethylene) molecules and periodic liquid water systems containing up to 15,000 atoms on up to 16 CPU cores. We consider algorithm-specific performance aspects, such as local vs nonlocal memory access and the degree of matrix sparsity. Comparisons to sparse matrix algebra implementations using off-the-shelf libraries on multicore CPUs, graphics processing units (GPUs), and the Intel many integrated core (MIC) architecture are also presented. The accuracy and stability of the algorithm are illustrated with long duration Born-Oppenheimer molecular dynamics simulations of 1000 water molecules and a 303 atom Trp cage protein solvated by 2682 water molecules.

Large Scale Density Estimation of Blue and Fin Whales: Utilizing Sparse Array Data to Develop and Implement a New Method for Estimating Blue and Fin Whale Density

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Large Scale Density Estimation of Blue and Fin Whales ...Utilizing Sparse Array Data to Develop and Implement a New Method for Estimating Blue and Fin Whale Density Len Thomas & Danielle Harris Centre...to develop and implement a new method for estimating blue and fin whale density that is effective over large spatial scales and is designed to cope

The dark matter of galaxy voids

NASA Astrophysics Data System (ADS)

Sutter, P. M.; Lavaux, Guilhem; Wandelt, Benjamin D.; Weinberg, David H.; Warren, Michael S.

2014-03-01

How do observed voids relate to the underlying dark matter distribution? To examine the spatial distribution of dark matter contained within voids identified in galaxy surveys, we apply Halo Occupation Distribution models representing sparsely and densely sampled galaxy surveys to a high-resolution N-body simulation. We compare these galaxy voids to voids found in the halo distribution, low-resolution dark matter and high-resolution dark matter. We find that voids at all scales in densely sampled surveys - and medium- to large-scale voids in sparse surveys - trace the same underdensities as dark matter, but they are larger in radius by ˜20 per cent, they have somewhat shallower density profiles and they have centres offset by ˜ 0.4Rv rms. However, in void-to-void comparison we find that shape estimators are less robust to sampling, and the largest voids in sparsely sampled surveys suffer fragmentation at their edges. We find that voids in galaxy surveys always correspond to underdensities in the dark matter, though the centres may be offset. When this offset is taken into account, we recover almost identical radial density profiles between galaxies and dark matter. All mock catalogues used in this work are available at http://www.cosmicvoids.net.

Streaming data analytics via message passing with application to graph algorithms

DOE PAGES

Plimpton, Steven J.; Shead, Tim

2014-05-06

The need to process streaming data, which arrives continuously at high-volume in real-time, arises in a variety of contexts including data produced by experiments, collections of environmental or network sensors, and running simulations. Streaming data can also be formulated as queries or transactions which operate on a large dynamic data store, e.g. a distributed database. We describe a lightweight, portable framework named PHISH which enables a set of independent processes to compute on a stream of data in a distributed-memory parallel manner. Datums are routed between processes in patterns defined by the application. PHISH can run on top of eithermore » message-passing via MPI or sockets via ZMQ. The former means streaming computations can be run on any parallel machine which supports MPI; the latter allows them to run on a heterogeneous, geographically dispersed network of machines. We illustrate how PHISH can support streaming MapReduce operations, and describe streaming versions of three algorithms for large, sparse graph analytics: triangle enumeration, subgraph isomorphism matching, and connected component finding. Lastly, we also provide benchmark timings for MPI versus socket performance of several kernel operations useful in streaming algorithms.« less

Uncertainty Analysis Based on Sparse Grid Collocation and Quasi-Monte Carlo Sampling with Application in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.

2011-12-01

Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently than traditional MCMC.

GPU-accelerated Modeling and Element-free Reverse-time Migration with Gauss Points Partition

NASA Astrophysics Data System (ADS)

Zhen, Z.; Jia, X.

2014-12-01

Element-free method (EFM) has been applied to seismic modeling and migration. Compared with finite element method (FEM) and finite difference method (FDM), it is much cheaper and more flexible because only the information of the nodes and the boundary of the study area are required in computation. In the EFM, the number of Gauss points should be consistent with the number of model nodes; otherwise the accuracy of the intermediate coefficient matrices would be harmed. Thus when we increase the nodes of velocity model in order to obtain higher resolution, we find that the size of the computer's memory will be a bottleneck. The original EFM can deal with at most 81×81 nodes in the case of 2G memory, as tested by Jia and Hu (2006). In order to solve the problem of storage and computation efficiency, we propose a concept of Gauss points partition (GPP), and utilize the GPUs to improve the computation efficiency. Considering the characteristics of the Gaussian points, the GPP method doesn't influence the propagation of seismic wave in the velocity model. To overcome the time-consuming computation of the stiffness matrix (K) and the mass matrix (M), we also use the GPUs in our computation program. We employ the compressed sparse row (CSR) format to compress the intermediate sparse matrices and try to simplify the operations by solving the linear equations with the CULA Sparse's Conjugate Gradient (CG) solver instead of the linear sparse solver 'PARDISO'. It is observed that our strategy can significantly reduce the computational time of K and Mcompared with the algorithm based on CPU. The model tested is Marmousi model. The length of the model is 7425m and the depth is 2990m. We discretize the model with 595x298 nodes, 300x300 Gauss cells and 3x3 Gauss points in each cell. In contrast to the computational time of the conventional EFM, the GPUs-GPP approach can substantially improve the efficiency. The speedup ratio of time consumption of computing K, M is 120 and the speedup ratio time consumption of RTM is 11.5. At the same time, the accuracy of imaging is not harmed. Another advantage of the GPUs-GPP method is its easy applications in other numerical methods such as the FEM. Finally, in the GPUs-GPP method, the arrays require quite limited memory storage, which makes the method promising in dealing with large-scale 3D problems.

Spatial information outflow from the hippocampal circuit: distributed spatial coding and phase precession in the subiculum.

PubMed

Kim, Steve M; Ganguli, Surya; Frank, Loren M

2012-08-22

Hippocampal place cells convey spatial information through a combination of spatially selective firing and theta phase precession. The way in which this information influences regions like the subiculum that receive input from the hippocampus remains unclear. The subiculum receives direct inputs from area CA1 of the hippocampus and sends divergent output projections to many other parts of the brain, so we examined the firing patterns of rat subicular neurons. We found a substantial transformation in the subicular code for space from sparse to dense firing rate representations along a proximal-distal anatomical gradient: neurons in the proximal subiculum are more similar to canonical, sparsely firing hippocampal place cells, whereas neurons in the distal subiculum have higher firing rates and more distributed spatial firing patterns. Using information theory, we found that the more distributed spatial representation in the subiculum carries, on average, more information about spatial location and context than the sparse spatial representation in CA1. Remarkably, despite the disparate firing rate properties of subicular neurons, we found that neurons at all proximal-distal locations exhibit robust theta phase precession, with similar spiking oscillation frequencies as neurons in area CA1. Our findings suggest that the subiculum is specialized to compress sparse hippocampal spatial codes into highly informative distributed codes suitable for efficient communication to other brain regions. Moreover, despite this substantial compression, the subiculum maintains finer scale temporal properties that may allow it to participate in oscillatory phase coding and spike timing-dependent plasticity in coordination with other regions of the hippocampal circuit.

Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox.

PubMed

Szyda, Joanna; Liu, Zengting; Zatoń-Dobrowolska, Magdalena; Wierzbicki, Heliodor; Rzasa, Anna

2008-01-01

We analysed data from a selective DNA pooling experiment with 130 individuals of the arctic fox (Alopex lagopus), which originated from 2 different types regarding body size. The association between alleles of 6 selected unlinked molecular markers and body size was tested by using univariate and multinomial logistic regression models, applying odds ratio and test statistics from the power divergence family. Due to the small sample size and the resulting sparseness of the data table, in hypothesis testing we could not rely on the asymptotic distributions of the tests. Instead, we tried to account for data sparseness by (i) modifying confidence intervals of odds ratio; (ii) using a normal approximation of the asymptotic distribution of the power divergence tests with different approaches for calculating moments of the statistics; and (iii) assessing P values empirically, based on bootstrap samples. As a result, a significant association was observed for 3 markers. Furthermore, we used simulations to assess the validity of the normal approximation of the asymptotic distribution of the test statistics under the conditions of small and sparse samples.

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Tonic Inhibitory Control of Dentate Gyrus Granule Cells by α5-Containing GABAA Receptors Reduces Memory Interference.

PubMed

Engin, Elif; Zarnowska, Ewa D; Benke, Dietmar; Tsvetkov, Evgeny; Sigal, Maksim; Keist, Ruth; Bolshakov, Vadim Y; Pearce, Robert A; Rudolph, Uwe

2015-10-07

Interference between similar or overlapping memories formed at different times poses an important challenge on the hippocampal declarative memory system. Difficulties in managing interference are at the core of disabling cognitive deficits in neuropsychiatric disorders. Computational models have suggested that, in the normal brain, the sparse activation of the dentate gyrus granule cells maintained by tonic inhibitory control enables pattern separation, an orthogonalization process that allows distinct representations of memories despite interference. To test this mechanistic hypothesis, we generated mice with significantly reduced expression of the α5-containing GABAA (α5-GABAARs) receptors selectively in the granule cells of the dentate gyrus (α5DGKO mice). α5DGKO mice had reduced tonic inhibition of the granule cells without any change in fast phasic inhibition and showed increased activation in the dentate gyrus when presented with novel stimuli. α5DGKO mice showed impairments in cognitive tasks characterized by high interference, without any deficiencies in low-interference tasks, suggesting specific impairment of pattern separation. Reduction of fast phasic inhibition in the dentate gyrus through granule cell-selective knock-out of α2-GABAARs or the knock-out of the α5-GABAARs in the downstream CA3 area did not detract from pattern separation abilities, which confirms the anatomical and molecular specificity of the findings. In addition to lending empirical support to computational hypotheses, our findings have implications for the treatment of interference-related cognitive symptoms in neuropsychiatric disorders, particularly considering the availability of pharmacological agents selectively targeting α5-GABAARs. Interference between similar memories poses a significant limitation on the hippocampal declarative memory system, and impaired interference management is a cognitive symptom in many disorders. Thus, understanding mechanisms of successful interference management or processes that can lead to interference-related memory problems has high theoretical and translational importance. This study provides empirical evidence that tonic inhibition in the dentate gyrus (DG), which maintains sparseness of neuronal activation in the DG, is essential for management of interference. The specificity of findings to tonic, but not faster, more transient types of neuronal inhibition and to the DG, but not the neighboring brain areas, is presented through control experiments. Thus, the findings link interference management to a specific mechanism, proposed previously by computational models. Copyright © 2015 the authors 0270-6474/15/3513699-15$15.00/0.

Concurrent Tumor Segmentation and Registration with Uncertainty-based Sparse non-Uniform Graphs

PubMed Central

Parisot, Sarah; Wells, William; Chemouny, Stéphane; Duffau, Hugues; Paragios, Nikos

2014-01-01

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. PMID:24717540

Reconstructing three-dimensional protein crystal intensities from sparse unoriented two-axis X-ray diffraction patterns

PubMed Central

Lan, Ti-Yen; Wierman, Jennifer L.; Tate, Mark W.; Philipp, Hugh T.; Elser, Veit

2017-01-01

Recently, there has been a growing interest in adapting serial microcrystallography (SMX) experiments to existing storage ring (SR) sources. For very small crystals, however, radiation damage occurs before sufficient numbers of photons are diffracted to determine the orientation of the crystal. The challenge is to merge data from a large number of such ‘sparse’ frames in order to measure the full reciprocal space intensity. To simulate sparse frames, a dataset was collected from a large lysozyme crystal illuminated by a dim X-ray source. The crystal was continuously rotated about two orthogonal axes to sample a subset of the rotation space. With the EMC algorithm [expand–maximize–compress; Loh & Elser (2009). Phys. Rev. E, 80, 026705], it is shown that the diffracted intensity of the crystal can still be reconstructed even without knowledge of the orientation of the crystal in any sparse frame. Moreover, parallel computation implementations were designed to considerably improve the time and memory scaling of the algorithm. The results show that EMC-based SMX experiments should be feasible at SR sources. PMID:28808431

Sparse Gaussian elimination with controlled fill-in on a shared memory multiprocessor

NASA Technical Reports Server (NTRS)

Alaghband, Gita; Jordan, Harry F.

1989-01-01

It is shown that in sparse matrices arising from electronic circuits, it is possible to do computations on many diagonal elements simultaneously. A technique for obtaining an ordered compatible set directly from the ordered incompatible table is given. The ordering is based on the Markowitz number of the pivot candidates. This technique generates a set of compatible pivots with the property of generating few fills. A novel heuristic algorithm is presented that combines the idea of an order-compatible set with a limited binary tree search to generate several sets of compatible pivots in linear time. An elimination set for reducing the matrix is generated and selected on the basis of a minimum Markowitz sum number. The parallel pivoting technique presented is a stepwise algorithm and can be applied to any submatrix of the original matrix. Thus, it is not a preordering of the sparse matrix and is applied dynamically as the decomposition proceeds. Parameters are suggested to obtain a balance between parallelism and fill-ins. Results of applying the proposed algorithms on several large application matrices using the HEP multiprocessor (Kowalik, 1985) are presented and analyzed.

Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory.

PubMed

Protopapa, Foteini; Siettos, Constantinos I; Evdokimidis, Ioannis; Smyrnis, Nikolaos

2014-01-01

We employed spectral Granger causality analysis on a full set of 56 electroencephalographic recordings acquired during the execution of either a 2D movement pointing or a perceptual (yes/no) change detection task with memory and non-memory conditions. On the basis of network characteristics across frequency bands, we provide evidence for the full dissociation of the corresponding cognitive processes. Movement-memory trial types exhibited higher degree nodes during the first 2 s of the delay period, mainly at central, left frontal and right-parietal areas. Change detection-memory trial types resulted in a three-peak temporal pattern of the total degree with higher degree nodes emerging mainly at central, right frontal, and occipital areas. Functional connectivity networks resulting from non-memory trial types were characterized by more sparse structures for both tasks. The movement-memory trial types encompassed an apparent coarse flow from frontal to parietal areas while the opposite flow from occipital, parietal to central and frontal areas was evident for the change detection-memory trial types. The differences among tasks and conditions were more profound in α (8-12 Hz) and β (12-30 Hz) and less in γ (30-45 Hz) band. Our results favor the hypothesis which considers spatial working memory as a by-product of specific mental processes that engages common brain areas under different network organizations.

A general purpose subroutine for fast fourier transform on a distributed memory parallel machine

NASA Technical Reports Server (NTRS)

Dubey, A.; Zubair, M.; Grosch, C. E.

1992-01-01

One issue which is central in developing a general purpose Fast Fourier Transform (FFT) subroutine on a distributed memory parallel machine is the data distribution. It is possible that different users would like to use the FFT routine with different data distributions. Thus, there is a need to design FFT schemes on distributed memory parallel machines which can support a variety of data distributions. An FFT implementation on a distributed memory parallel machine which works for a number of data distributions commonly encountered in scientific applications is presented. The problem of rearranging the data after computing the FFT is also addressed. The performance of the implementation on a distributed memory parallel machine Intel iPSC/860 is evaluated.

Dissociation of item and source memory in rhesus monkeys.

PubMed

Basile, Benjamin M; Hampton, Robert R

2017-09-01

Source memory, or memory for the context in which a memory was formed, is a defining characteristic of human episodic memory and source memory errors are a debilitating symptom of memory dysfunction. Evidence for source memory in nonhuman primates is sparse despite considerable evidence for other types of sophisticated memory and the practical need for good models of episodic memory in nonhuman primates. A previous study showed that rhesus monkeys confused the identity of a monkey they saw with a monkey they heard, but only after an extended memory delay. This suggests that they initially remembered the source - visual or auditory - of the information but forgot the source as time passed. Here, we present a monkey model of source memory that is based on this previous study. In each trial, monkeys studied two images, one that they simply viewed and touched and the other that they classified as a bird, fish, flower, or person. In a subsequent memory test, they were required to select the image from one source but avoid the other. With training, monkeys learned to suppress responding to images from the to-be-avoided source. After longer memory intervals, monkeys continued to show reliable item memory, discriminating studied images from distractors, but made many source memory errors. Monkeys discriminated source based on study method, not study order, providing preliminary evidence that our manipulation of retention interval caused errors due to source forgetting instead of source confusion. Finally, some monkeys learned to select remembered images from either source on cue, showing that they did indeed remember both items and both sources. This paradigm potentially provides a new model to study a critical aspect of episodic memory in nonhuman primates. Copyright © 2017 Elsevier B.V. All rights reserved.

Power Enhancement in High Dimensional Cross-Sectional Tests

PubMed Central

Fan, Jianqing; Liao, Yuan; Yao, Jiawei

2016-01-01

We propose a novel technique to boost the power of testing a high-dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated only by a couple of components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme-value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a “power enhancement component”, which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. As specific applications, the proposed methods are applied to testing the factor pricing models and validating the cross-sectional independence in panel data models. PMID:26778846

Holding multiple items in short term memory: a neural mechanism.

PubMed

Rolls, Edmund T; Dempere-Marco, Laura; Deco, Gustavo

2013-01-01

Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging.

Holding Multiple Items in Short Term Memory: A Neural Mechanism

PubMed Central

Rolls, Edmund T.; Dempere-Marco, Laura; Deco, Gustavo

2013-01-01

Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging. PMID:23613789

A single-trace dual-process model of episodic memory: a novel computational account of familiarity and recollection.

PubMed

Greve, Andrea; Donaldson, David I; van Rossum, Mark C W

2010-02-01

Dual-process theories of episodic memory state that retrieval is contingent on two independent processes: familiarity (providing a sense of oldness) and recollection (recovering events and their context). A variety of studies have reported distinct neural signatures for familiarity and recollection, supporting dual-process theory. One outstanding question is whether these signatures reflect the activation of distinct memory traces or the operation of different retrieval mechanisms on a single memory trace. We present a computational model that uses a single neuronal network to store memory traces, but two distinct and independent retrieval processes access the memory. The model is capable of performing familiarity and recollection-based discrimination between old and new patterns, demonstrating that dual-process models need not to rely on multiple independent memory traces, but can use a single trace. Importantly, our putative familiarity and recollection processes exhibit distinct characteristics analogous to those found in empirical data; they diverge in capacity and sensitivity to sparse and correlated patterns, exhibit distinct ROC curves, and account for performance on both item and associative recognition tests. The demonstration that a single-trace, dual-process model can account for a range of empirical findings highlights the importance of distinguishing between neuronal processes and the neuronal representations on which they operate.

Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study

PubMed Central

Brownstone, Robert M.

2015-01-01

Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determining this distribution would provide insights into how sensory inputs are integrated to adjust ongoing locomotor activity. We asked whether a sparsely distributed set of sensory inputs could modify ongoing locomotor activity. To address this question, several computational models of locomotor central pattern generators (CPGs) that were mechanistically diverse and generated locomotor-like rhythmic activity were developed. We show that sensory inputs restricted to a small subset of the network neurons can perturb locomotor activity in the same manner as seen experimentally. Furthermore, we show that an architecture with sparse sensory input improves the capacity to gate sensory information by selectively modulating sensory channels. These data demonstrate that sensory input to rhythm-generating networks need not be extensively distributed. PMID:25673740

A sparse matrix algorithm on the Boolean vector machine

NASA Technical Reports Server (NTRS)

Wagner, Robert A.; Patrick, Merrell L.

1988-01-01

VLSI technology is being used to implement a prototype Boolean Vector Machine (BVM), which is a large network of very small processors with equally small memories that operate in SIMD mode; these use bit-serial arithmetic, and communicate via cube-connected cycles network. The BVM's bit-serial arithmetic and the small memories of individual processors are noted to compromise the system's effectiveness in large numerical problem applications. Attention is presently given to the implementation of a basic matrix-vector iteration algorithm for space matrices of the BVM, in order to generate over 1 billion useful floating-point operations/sec for this iteration algorithm. The algorithm is expressed in a novel language designated 'BVM'.

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

PubMed

Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

PubMed Central

Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947

Sparsey™: event recognition via deep hierarchical sparse distributed codes

PubMed Central

Rinkus, Gerard J.

2014-01-01

The visual cortex's hierarchical, multi-level organization is captured in many biologically inspired computational vision models, the general idea being that progressively larger scale (spatially/temporally) and more complex visual features are represented in progressively higher areas. However, most earlier models use localist representations (codes) in each representational field (which we equate with the cortical macrocolumn, “mac”), at each level. In localism, each represented feature/concept/event (hereinafter “item”) is coded by a single unit. The model we describe, Sparsey, is hierarchical as well but crucially, it uses sparse distributed coding (SDC) in every mac in all levels. In SDC, each represented item is coded by a small subset of the mac's units. The SDCs of different items can overlap and the size of overlap between items can be used to represent their similarity. The difference between localism and SDC is crucial because SDC allows the two essential operations of associative memory, storing a new item and retrieving the best-matching stored item, to be done in fixed time for the life of the model. Since the model's core algorithm, which does both storage and retrieval (inference), makes a single pass over all macs on each time step, the overall model's storage/retrieval operation is also fixed-time, a criterion we consider essential for scalability to the huge (“Big Data”) problems. A 2010 paper described a nonhierarchical version of this model in the context of purely spatial pattern processing. Here, we elaborate a fully hierarchical model (arbitrary numbers of levels and macs per level), describing novel model principles like progressive critical periods, dynamic modulation of principal cells' activation functions based on a mac-level familiarity measure, representation of multiple simultaneously active hypotheses, a novel method of time warp invariant recognition, and we report results showing learning/recognition of spatiotemporal patterns. PMID:25566046

DISTRIBUTIONAL CHANGES AND POPULATION STATUS FOR AMPHIBIANS IN THE EASTERN MOJAVE DESERT

EPA Science Inventory

A number of amphibian species historically inhabited sparsely distributed wetlands in the Mojave Desert of western North America, habitats that have been dramatically altered or eliminated as a result of human activities. The population status and distributional changes for amphi...

Discrete-state phasor neural networks

NASA Astrophysics Data System (ADS)

Noest, André J.

1988-08-01

An associative memory network with local variables assuming one of q equidistant positions on the unit circle (q-state phasors) is introduced, and its recall behavior is solved exactly for any q when the interactions are sparse and asymmetric. Such models can describe natural or artifical networks of (neuro-)biological, chemical, or electronic limit-cycle oscillators with q-fold instead of circular symmetry, or similar optical computing devices using a phase-encoded data representation.

When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity

DTIC Science & Technology

2013-08-14

Communications and Computing, Electrical Engineering and Computer Science Dept., University of California, Irvine, USA 92697. Email : a.anandkumar...uci.edu,mjanzami@uci.edu. Daniel Hsu and Sham Kakade are with Microsoft Research New England, 1 Memorial Drive, Cambridge, MA 02142. Email : dahsu...Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes. Sparse coding for multitask and transfer learning. ArxXiv preprint, abs/1209.0738, 2012

spammpack, Version 2013-06-18

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

2014-01-17

This library is an implementation of the Sparse Approximate Matrix Multiplication (SpAMM) algorithm introduced. It provides a matrix data type, and an approximate matrix product, which exhibits linear scaling computational complexity for matrices with decay. The product error and the performance of the multiply can be tuned by choosing an appropriate tolerance. The library can be compiled for serial execution or parallel execution on shared memory systems with an OpenMP capable compiler

Attractor neural networks with resource-efficient synaptic connectivity

NASA Astrophysics Data System (ADS)

Pehlevan, Cengiz; Sengupta, Anirvan

Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

Methylphenidate produces selective enhancement of declarative memory consolidation in healthy volunteers.

PubMed

Linssen, A M W; Vuurman, E F P M; Sambeth, A; Riedel, W J

2012-06-01

Methylphenidate inhibits the reuptake of dopamine and noradrenaline and is used to treat children with attention deficit hyperactivity disorder (ADHD). Besides reducing behavioral symptoms, it improves their cognitive function. There are also observations of methylphenidate-induced cognition enhancement in healthy adults, although studies in this area are relatively sparse. We assessed the possible memory-enhancing properties of methylphenidate. In the current study, the possible enhancing effects of three doses of methylphenidate on declarative and working memory, attention, response inhibition and planning were investigated in healthy volunteers. In a double blind placebo-controlled crossover study, 19 healthy young male volunteers were tested after a single dose of placebo or 10, 20 or 40 mg of methylphenidate. Cognitive performance testing included a word learning test as a measure of declarative memory, a spatial working memory test, a set-shifting test, a stop signal test and a computerized version of the Tower of London planning test. Declarative memory consolidation was significantly improved relative to placebo after 20 and 40 mg of methylphenidate. Methylphenidate also improved set shifting and stopped signal task performance but did not affect spatial working memory or planning. To the best of our knowledge, this is the first study reporting enhanced declarative memory consolidation after methylphenidate in a dose-related fashion over a dose range that is presumed to reflect a wide range of dopamine reuptake inhibition.

Effect of distribution of striated laser hardening tracks on dry sliding wear resistance of biomimetic surface

NASA Astrophysics Data System (ADS)

Su, Wei; Zhou, Ti; Zhang, Peng; Zhou, Hong; Li, Hui

2018-01-01

Some biological surfaces were proved to have excellent anti-wear performance. Being inspired, Nd:YAG pulsed laser was used to create striated biomimetic laser hardening tracks on medium carbon steel samples. Dry sliding wear tests biomimetic samples were performed to investigate specific influence of distribution of laser hardening tracks on sliding wear resistance of biomimetic samples. After comparing wear weight loss of biomimetic samples, quenched sample and untreated sample, it can be suggested that the sample covered with dense laser tracks (3.5 mm spacing) has lower wear weight loss than the one covered with sparse laser tracks (4.5 mm spacing); samples distributed with only dense laser tracks or sparse laser tracks (even distribution) were proved to have better wear resistance than samples distributed with both dense and sparse tracks (uneven distribution). Wear mechanisms indicate that laser track and exposed substrate of biomimetic sample can be regarded as hard zone and soft zone respectively. Inconsecutive striated hard regions, on the one hand, can disperse load into small branches, on the other hand, will hinder sliding abrasives during wear. Soft regions with small range are beneficial in consuming mechanical energy and storing lubricative oxides, however, soft zone with large width (>0.5 mm) will be harmful to abrasion resistance of biomimetic sample because damages and material loss are more obvious on surface of soft phase. As for the reason why samples with even distributed bionic laser tracks have better wear resistance, it can be explained by the fact that even distributed laser hardening tracks can inhibit severe worn of local regions, thus sliding process can be more stable and wear extent can be alleviated as well.

AZTEC. Parallel Iterative method Software for Solving Linear Systems

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

Hutchinson, S.; Shadid, J.; Tuminaro, R.

1995-07-01

AZTEC is an interactive library that greatly simplifies the parrallelization process when solving the linear systems of equations Ax=b where A is a user supplied n X n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. AZTEC is intended as a software tool for users who want to avoid cumbersome parallel programming details but who have large sparse linear systems which require an efficiently utilized parallel processing system. A collection of data transformation tools are provided that allow for easy creation of distributed sparse unstructured matricesmore » for parallel solutions.« less

On Edge Exchangeable Random Graphs

NASA Astrophysics Data System (ADS)

Janson, Svante

2017-06-01

We study a recent model for edge exchangeable random graphs introduced by Crane and Dempsey; in particular we study asymptotic properties of the random simple graph obtained by merging multiple edges. We study a number of examples, and show that the model can produce dense, sparse and extremely sparse random graphs. One example yields a power-law degree distribution. We give some examples where the random graph is dense and converges a.s. in the sense of graph limit theory, but also an example where a.s. every graph limit is the limit of some subsequence. Another example is sparse and yields convergence to a non-integrable generalized graphon defined on (0,∞).

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network.

PubMed

Han, Changcai; Yang, Jinsheng

2017-10-30

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network

PubMed Central

Han, Changcai; Yang, Jinsheng

2017-01-01

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes. PMID:29084155

The effects of missing data on global ozone estimates

NASA Technical Reports Server (NTRS)

Drewry, J. W.; Robbins, J. L.

1981-01-01

The effects of missing data and model truncation on estimates of the global mean, zonal distribution, and global distribution of ozone are considered. It is shown that missing data can introduce biased estimates with errors that are not accounted for in the accuracy calculations of empirical modeling techniques. Data-fill techniques are introduced and used for evaluating error bounds and constraining the estimate in areas of sparse and missing data. It is found that the accuracy of the global mean estimate is more dependent on data distribution than model size. Zonal features can be accurately described by 7th order models over regions of adequate data distribution. Data variance accounted for by higher order models appears to represent climatological features of columnar ozone rather than pure error. Data-fill techniques can prevent artificial feature generation in regions of sparse or missing data without degrading high order estimates over dense data regions.

Pole-Like Road Furniture Detection in Sparse and Unevenly Distributed Mobile Laser Scanning Data

NASA Astrophysics Data System (ADS)

Li, F.; Lehtomäki, M.; Oude Elberink, S.; Vosselman, G.; Puttonen, E.; Kukko, A.; Hyyppä, J.

2018-05-01

Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.

Reconstructing cortical current density by exploring sparseness in the transform domain

NASA Astrophysics Data System (ADS)

Ding, Lei

2009-05-01

In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.

Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

DTIC Science & Technology

2016-05-01

large but correlated noise and signal interference (i.e., low -rank interference). Another contribution is the implementation of deep learning...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...Classification of Acoustic Transients 6 3.2 Joint Sparse Representation with Low -Rank Interference 7 3.3 Simultaneous Group-and-Joint Sparse Representation

Brain region-specific activity patterns after recent or remote memory retrieval of auditory conditioned fear.

PubMed

Kwon, Jeong-Tae; Jhang, Jinho; Kim, Hyung-Su; Lee, Sujin; Han, Jin-Hee

2012-09-19

Memory is thought to be sparsely encoded throughout multiple brain regions forming unique memory trace. Although evidence has established that the amygdala is a key brain site for memory storage and retrieval of auditory conditioned fear memory, it remains elusive whether the auditory brain regions may be involved in fear memory storage or retrieval. To investigate this possibility, we systematically imaged the brain activity patterns in the lateral amygdala, MGm/PIN, and AuV/TeA using activity-dependent induction of immediate early gene zif268 after recent and remote memory retrieval of auditory conditioned fear. Consistent with the critical role of the amygdala in fear memory, the zif268 activity in the lateral amygdala was significantly increased after both recent and remote memory retrieval. Interesting, however, the density of zif268 (+) neurons in both MGm/PIN and AuV/TeA, particularly in layers IV and VI, was increased only after remote but not recent fear memory retrieval compared to control groups. Further analysis of zif268 signals in AuV/TeA revealed that conditioned tone induced stronger zif268 induction compared to familiar tone in each individual zif268 (+) neuron after recent memory retrieval. Taken together, our results support that the lateral amygdala is a key brain site for permanent fear memory storage and suggest that MGm/PIN and AuV/TeA might play a role for remote memory storage or retrieval of auditory conditioned fear, or, alternatively, that these auditory brain regions might have a different way of processing for familiar or conditioned tone information at recent and remote time phases.

Compressive sampling by artificial neural networks for video

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Jenkins, Jeffrey; Reinhardt, Kitt

2011-06-01

We describe a smart surveillance strategy for handling novelty changes. Current sensors seem to keep all, redundant or not. The Human Visual System's Hubel-Wiesel (wavelet) edge detection mechanism pays attention to changes in movement, which naturally produce organized sparseness because a stagnant edge is not reported to the brain's visual cortex by retinal neurons. Sparseness is defined as an ordered set of ones (movement or not) relative to zeros that could be pseudo-orthogonal among themselves; then suited for fault tolerant storage and retrieval by means of Associative Memory (AM). The firing is sparse at the change locations. Unlike purely random sparse masks adopted in medical Compressive Sensing, these organized ones have an additional benefit of using the image changes to make retrievable graphical indexes. We coined this organized sparseness as Compressive Sampling; sensing but skipping over redundancy without altering the original image. Thus, we turn illustrate with video the survival tactics which animals that roam the Earth use daily. They acquire nothing but the space-time changes that are important to satisfy specific prey-predator relationships. We have noticed a similarity between the mathematical Compressive Sensing and this biological mechanism used for survival. We have designed a hardware implementation of the Human Visual System's Compressive Sampling scheme. To speed up further, our mixedsignal circuit design of frame differencing is built in on-chip processing hardware. A CMOS trans-conductance amplifier is designed here to generate a linear current output using a pair of differential input voltages from 2 photon detectors for change detection---one for the previous value and the other the subsequent value, ("write" synaptic weight by Hebbian outer products; "read" by inner product & pt. NL threshold) to localize and track the threat targets.

The parietal memory network activates similarly for true and associative false recognition elicited via the DRM procedure.

PubMed

McDermott, Kathleen B; Gilmore, Adrian W; Nelson, Steven M; Watson, Jason M; Ojemann, Jeffrey G

2017-02-01

Neuroimaging investigations of human memory encoding and retrieval have revealed that multiple regions of parietal cortex contribute to memory. Recently, a sparse network of regions within parietal cortex has been identified using resting state functional connectivity (MRI techniques). The regions within this network exhibit consistent task-related responses during memory formation and retrieval, leading to its being called the parietal memory network (PMN). Among its signature patterns are: deactivation during initial experience with an item (e.g., encoding); activation during subsequent repetitions (e.g., at retrieval); greater activation for successfully retrieved familiar words than novel words (e.g., hits relative to correctly-rejected lures). The question of interest here is whether novel words that are subjectively experienced as having been recently studied would elicit PMN activation similar to that of hits. That is, we compared old items correctly recognized to two types of novel items on a recognition test: those correctly identified as new and those incorrectly labeled as old due to their strong associative relation to the studied words (in the DRM false memory protocol). Subjective oldness plays a strong role in driving activation, as hits and false alarms activated similarly (and greater than correctly-rejected lures). Copyright © 2016 Elsevier Ltd. All rights reserved.

Nocturnal Mnemonics: Sleep and Hippocampal Memory Processing

PubMed Central

Saletin, Jared M.; Walker, Matthew P.

2012-01-01

As critical as waking brain function is to learning and memory, an established literature now describes an equally important yet complementary role for sleep in information processing. This overview examines the specific contribution of sleep to human hippocampal memory processing; both the detriments caused by a lack of sleep, and conversely, the proactive benefits that develop following the presence of sleep. First, a role for sleep before learning is discussed, preparing the hippocampus for initial memory encoding. Second, a role for sleep after learning is considered, modulating the post-encoding consolidation of hippocampal-dependent memory. Third, a model is outlined in which these encoding and consolidation operations are symbiotically accomplished, associated with specific NREM sleep physiological oscillations. As a result, the optimal network outcome is achieved: increasing hippocampal independence and hence overnight consolidation, while restoring next-day sparse hippocampal encoding capacity for renewed learning ability upon awakening. Finally, emerging evidence is considered suggesting that, unlike previous conceptions, sleep does not universally consolidate all information. Instead, and based on explicit as well as saliency cues during initial encoding, sleep executes the discriminatory offline consolidation only of select information. Consequently, sleep promotes the targeted strengthening of some memories while actively forgetting others; a proposal with significant theoretical and clinical ramifications. PMID:22557988

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.

PubMed

Parisot, Sarah; Wells, William; Chemouny, Stéphane; Duffau, Hugues; Paragios, Nikos

2014-05-01

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. Copyright © 2014 Elsevier B.V. All rights reserved.

The global distribution and dynamics of surface soil moisture

NASA Astrophysics Data System (ADS)

McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara

2017-01-01

Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA's Soil Moisture Active Passive mission to show that surface soil moisture--a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces--plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.

When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints

DTIC Science & Technology

2013-06-16

Science Dept., University of California, Irvine, USA 92697. Email : a.anandkumar@uci.edu,mjanzami@uci.edu. Daniel Hsu and Sham Kakade are with...Microsoft Research New England, 1 Memorial Drive, Cambridge, MA 02142. Email : dahsu@microsoft.com, skakade@microsoft.com 1 a latent space dimensionality...Sparse coding for multitask and transfer learning. ArxXiv preprint, abs/1209.0738, 2012. [34] G.H. Golub and C.F. Van Loan. Matrix Computations. The

Application distribution model and related security attacks in VANET

NASA Astrophysics Data System (ADS)

Nikaein, Navid; Kanti Datta, Soumya; Marecar, Irshad; Bonnet, Christian

2013-03-01

In this paper, we present a model for application distribution and related security attacks in dense vehicular ad hoc networks (VANET) and sparse VANET which forms a delay tolerant network (DTN). We study the vulnerabilities of VANET to evaluate the attack scenarios and introduce a new attacker`s model as an extension to the work done in [6]. Then a VANET model has been proposed that supports the application distribution through proxy app stores on top of mobile platforms installed in vehicles. The steps of application distribution have been studied in detail. We have identified key attacks (e.g. malware, spamming and phishing, software attack and threat to location privacy) for dense VANET and two attack scenarios for sparse VANET. It has been shown that attacks can be launched by distributing malicious applications and injecting malicious codes to On Board Unit (OBU) by exploiting OBU software security holes. Consequences of such security attacks have been described. Finally, countermeasures including the concepts of sandbox have also been presented in depth.

Meeting the memory challenges of brain-scale network simulation.

PubMed

Kunkel, Susanne; Potjans, Tobias C; Eppler, Jochen M; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

2011-01-01

The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10(5) neurons with up to 10(9) synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less expensive. Applying the model to our freely available Neural Simulation Tool (NEST), we identify the software components dominant at different scales, and develop general strategies for reducing the memory consumption, in particular by using data structures that exploit the sparseness of the local representation of the network. We show that these adaptations enable our simulation software to scale up to the order of 10,000 processors and beyond. As memory consumption issues are likely to be relevant for any software dealing with complex connectome data on such architectures, our approach and our findings should be useful for researchers developing novel neuroinformatics solutions to the challenges posed by the connectome project.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

2016-12-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

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

Spotting and tracking good biometrics with the human visual system

NASA Astrophysics Data System (ADS)

Szu, Harold; Jenkins, Jeffrey; Hsu, Charles

2011-06-01

We mathematically model the mammalian Visual System's (VS) capability of spotting objects. How can a hawk see a tiny running rabbit from miles above ground? How could that rabbit see the approaching hawk? This predatorprey interaction draws parallels with spotting a familiar person in a crowd. We assume that mammal eyes use peripheral vision to perceive unexpected changes from our memory, and then use our central vision (fovea) to pay attention. The difference between an image and our memory of that image is usually small, mathematically known as a 'sparse representation'. The VS communicates with the brain using a finite reservoir of neurotransmittents, which produces an on-center and thus off-surround Hubel/Wiesel Mexican hat receptive field. This is the basis of our model. This change detection mechanism could drive our attention, allowing us to hit a curveball. If we are about to hit a baseball, what information extracted by our HVS tells us where to swing? Physical human features such as faces, irises, and fingerprints have been successfully used for identification (Biometrics) for decades, recently including voice and walking style for identification from further away. Biologically, humans must use a change detection strategy to achieve an ordered sparseness and use a sigmoid threshold for noisy measurements in our Hetero-Associative Memory [HAM] classifier for fault tolerant recall. Human biometrics is dynamic, and therefore involves more than just the surface, requiring a 3 dimensional measurement (i.e. Daugman/Gabor iris features). Such a measurement can be achieved using the partial coherence of a laser's reflection from a 3-D biometric surface, creating more degrees of freedom (d.o.f.) to meet the Army's challenge of distant Biometrics. Thus, one might be able to increase the standoff loss of less distinguished degrees of freedom (DOF).

Memory in autistic spectrum disorder.

PubMed

Boucher, Jill; Mayes, Andrew; Bigham, Sally

2012-05-01

Behavioral evidence concerning memory in forms of high-functioning autism (HFA) and in moderately low-functioning autism (M-LFA) is reviewed and compared. Findings on M-LFA are sparse. However, it is provisionally concluded that memory profiles in HFA and M-LFA (relative to ability-matched controls) are similar but that declarative memory impairments are more extensive in M-LFA than in HFA. Specifically, both groups have diminished memory for emotion- or person-related stimuli. Regarding memory for nonsocial stimuli, both groups probably have mental-age-appropriate nondeclarative memory, and within declarative memory, both groups have mental-age-appropriate immediate free recall of within-span or supraspan lists of unrelated items, as well as cued recall and paired associate learning. By contrast, recognition is largely unimpaired in HFA but moderately impaired in M-LFA, and free recall of meaningful or structured stimuli is moderately impaired in HFA but more severely impaired in M-LFA. Theoretical explanations of data on declarative memory in HFA identify problems in the integrative processing, or the consolidation and storage, of complex stimuli or a specific problem of recollection. Proposed neural substrates include the following: disconnectivity of primary sensory and association areas; dysfunctions of medial prefrontal cortex, hippocampus, or posterior parietal lobe; or combinations of these associated with neural disconnectivity. Hypothetically, perirhinal dysfunction might explain the more extensive declarative memory impairments in M-LFA. Foreseeable consequences of uneven memory abilities in HFA and M-LFA are outlined, including possible effects on language and learning in M-LFA. Finally, priorities for future research are identified, highlighting the urgent need for research on memory in lower functioning individuals. 2012 APA, all rights reserved

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

DTIC Science & Technology

2016-09-27

16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 Signal recovery, Sparse learning , Subspace modeling REPORT DOCUMENTATION PAGE 11...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world

Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise

NASA Astrophysics Data System (ADS)

Orović, Irena; Stanković, Srdjan; Amin, Moeness

2013-05-01

A modified robust two-dimensional compressive sensing algorithm for reconstruction of sparse time-frequency representation (TFR) is proposed. The ambiguity function domain is assumed to be the domain of observations. The two-dimensional Fourier bases are used to linearly relate the observations to the sparse TFR, in lieu of the Wigner distribution. We assume that a set of available samples in the ambiguity domain is heavily corrupted by an impulsive type of noise. Consequently, the problem of sparse TFR reconstruction cannot be tackled using standard compressive sensing optimization algorithms. We introduce a two-dimensional L-statistics based modification into the transform domain representation. It provides suitable initial conditions that will produce efficient convergence of the reconstruction algorithm. This approach applies sorting and weighting operations to discard an expected amount of samples corrupted by noise. The remaining samples serve as observations used in sparse reconstruction of the time-frequency signal representation. The efficiency of the proposed approach is demonstrated on numerical examples that comprise both cases of monocomponent and multicomponent signals.

Solving very large, sparse linear systems on mesh-connected parallel computers

NASA Technical Reports Server (NTRS)

Opsahl, Torstein; Reif, John

1987-01-01

The implementation of Pan and Reif's Parallel Nested Dissection (PND) algorithm on mesh connected parallel computers is described. This is the first known algorithm that allows very large, sparse linear systems of equations to be solved efficiently in polylog time using a small number of processors. How the processor bound of PND can be matched to the number of processors available on a given parallel computer by slowing down the algorithm by constant factors is described. Also, for the important class of problems where G(A) is a grid graph, a unique memory mapping that reduces the inter-processor communication requirements of PND to those that can be executed on mesh connected parallel machines is detailed. A description of an implementation on the Goodyear Massively Parallel Processor (MPP), located at Goddard is given. Also, a detailed discussion of data mappings and performance issues is given.

Dense and Sparse Matrix Operations on the Cell Processor

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

Williams, Samuel W.; Shalf, John; Oliker, Leonid

2005-05-01

The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. Therefore, the high performance computing community is examining alternative architectures that address the limitations of modern superscalar designs. In this work, we examine STI's forthcoming Cell processor: a novel, low-power architecture that combines a PowerPC core with eight independent SIMD processing units coupled with a software-controlled memory to offer high FLOP/s/Watt. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop an analytic framework to predict Cell performance on dense and sparse matrix operations, usingmore » a variety of algorithmic approaches. Results demonstrate Cell's potential to deliver more than an order of magnitude better GFLOP/s per watt performance, when compared with the Intel Itanium2 and Cray X1 processors.« less

ROPE: Recoverable Order-Preserving Embedding of Natural Language

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

Widemann, David P.; Wang, Eric X.; Thiagarajan, Jayaraman J.

We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and themore » IMDB Movie Review dataset.« less

Occupational exposure to aluminum and its biomonitoring in perspective.

PubMed

Riihimäki, Vesa; Aitio, Antero

2012-11-01

Exposure to aluminum at work is widespread, and people are exposed to several species of aluminum, which differ markedly as to the kinetics and toxicity. Especially welding of aluminum is widely applied and continuously expanding. Inhalation of fine particles of sparsely soluble aluminum results in the retention of deposited particles in the lungs. From the lungs, aluminum is released to the blood and distributed to bones and the brain, and excreted to urine. Soluble aluminum compounds are not accumulated in the lungs. Neurotoxicity is the critical effect of exposure to sparsely soluble aluminum compounds. Studies on workers exposed to aluminum welding fumes have revealed disturbances of cognitive processes, memory and concentration, and changes in mood and EEG. Early pulmonary effects have been observed among aluminum powder-production workers using high-resolution computed tomography. The primary objective of aluminum biomonitoring (BM) is to help prevent the formation of aluminum burden in the lungs and thereby to prevent harmful accumulation of aluminum in target organs. BM of aluminum can be effectively used for this purpose in the production/use of aluminum powders, aluminum welding, as well as plasma cutting, grinding, polishing and thermal spraying of aluminum. BM of aluminum may also be similarly useful in the smelting of aluminum and probably in the production of corundum. BM can help identify exposed individuals and roughly quantitate transient exposure but cannot predict health effects in the production/use of soluble aluminum salts. For urinary aluminum (U-Al) we propose an action limit of 3 µmol/L, corrected to a relative density of 1.021, in a sample collected preshift after two days without occupational exposure, and without use of aluminum-containing drugs. This value corresponds roughly to 2.3 µmol/g creatinine. Compliance with this limit is expected to protect the worker against the critical effect of aluminum in exposure to sparsely soluble aluminum dusts, the cognitive function of the central nervous system. For serum aluminum (S-Al), we do not propose an action limit because S-Al is less sensitive as an indicator of aluminum load.

Automatic Management of Parallel and Distributed System Resources

NASA Technical Reports Server (NTRS)

Yan, Jerry; Ngai, Tin Fook; Lundstrom, Stephen F.

1990-01-01

Viewgraphs on automatic management of parallel and distributed system resources are presented. Topics covered include: parallel applications; intelligent management of multiprocessing systems; performance evaluation of parallel architecture; dynamic concurrent programs; compiler-directed system approach; lattice gaseous cellular automata; and sparse matrix Cholesky factorization.

AMPHIBIAN DECLINES AND ENVIRONMENTAL CHANGE IN THE EASTERN "MOJAVE DESERT"

EPA Science Inventory

A number of amphibian species historically inhabited sparsely distributed wetlands in the Mojave Desert, USA, habitats that have been dramatically altered or eliminated as a result of human activities. The population status and distribution of amphibians were investigated in a 20...

Distributed representations in memory: Insights from functional brain imaging

PubMed Central

Rissman, Jesse; Wagner, Anthony D.

2015-01-01

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly leveraged powerful analytical tools (e.g., multi-voxel pattern analysis) to decode the information represented within distributed fMRI activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content, and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses. PMID:21943171

beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types

PubMed Central

Pagès, Hervé

2018-01-01

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set. PMID:29723188

beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.

PubMed

Lun, Aaron T L; Pagès, Hervé; Smith, Mike L

2018-05-01

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set.

A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

PubMed Central

Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

2016-01-01

In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298

Fraction of the global water cycle observed by SMAP

NASA Astrophysics Data System (ADS)

Mccoll, K. A.; Entekhabi, D.; Alemohammad, S. H.; Akbar, R.; Konings, A. G.; Yueh, S. H.

2016-12-01

Sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture (SSM). Using a full year of global observations from NASA's Soil Moisture Active Passive (SMAP) mission, we show here that SSM - a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces - plays a very significant role in the water cycle, retaining a median 16% of precipitation falling on land after 3 days. Furthermore, the retained fraction of the SSM storage after 3 days is highest (lowest) over arid (wet) regions, and in regions where drainage to groundwater storage is lowest (highest). The retained fraction decreases monotonically with increasing mean SSM. Regions of low retained fraction broadly correspond spatially with regions where groundwater recharge and groundwater storage are both largest. These analyses are the first global estimates - derived from measurements rather than models - of both the mean magnitude and memory time scales of the SSM storage. Beyond the fundamental importance of characterizing the magnitude and response time scales of Earth's water storages, a key application of these results is in identifying regions with strong land-atmosphere coupling. Significant soil moisture memory is a necessary condition for land-atmosphere feedbacks. These results may therefore have particularly important implications for short-term weather forecasting of extreme precipitation events and floods.

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

PubMed Central

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches. PMID:25110741

Multispectral image compression based on DSC combined with CCSDS-IDC.

PubMed

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

Mnemonic transmission, social contagion, and emergence of collective memory: Influence of emotional valence, group structure, and information distribution.

PubMed

Choi, Hae-Yoon; Kensinger, Elizabeth A; Rajaram, Suparna

2017-09-01

Social transmission of memory and its consequence on collective memory have generated enduring interdisciplinary interest because of their widespread significance in interpersonal, sociocultural, and political arenas. We tested the influence of 3 key factors-emotional salience of information, group structure, and information distribution-on mnemonic transmission, social contagion, and collective memory. Participants individually studied emotionally salient (negative or positive) and nonemotional (neutral) picture-word pairs that were completely shared, partially shared, or unshared within participant triads, and then completed 3 consecutive recalls in 1 of 3 conditions: individual-individual-individual (control), collaborative-collaborative (identical group; insular structure)-individual, and collaborative-collaborative (reconfigured group; diverse structure)-individual. Collaboration enhanced negative memories especially in insular group structure and especially for shared information, and promoted collective forgetting of positive memories. Diverse group structure reduced this negativity effect. Unequally distributed information led to social contagion that creates false memories; diverse structure propagated a greater variety of false memories whereas insular structure promoted confidence in false recognition and false collective memory. A simultaneous assessment of network structure, information distribution, and emotional valence breaks new ground to specify how network structure shapes the spread of negative memories and false memories, and the emergence of collective memory. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A robust holographic autofocusing criterion based on edge sparsity: comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront

NASA Astrophysics Data System (ADS)

Tamamitsu, Miu; Zhang, Yibo; Wang, Hongda; Wu, Yichen; Ozcan, Aydogan

2018-02-01

The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.

FPGA architecture and implementation of sparse matrix vector multiplication for the finite element method

NASA Astrophysics Data System (ADS)

Elkurdi, Yousef; Fernández, David; Souleimanov, Evgueni; Giannacopoulos, Dennis; Gross, Warren J.

2008-04-01

The Finite Element Method (FEM) is a computationally intensive scientific and engineering analysis tool that has diverse applications ranging from structural engineering to electromagnetic simulation. The trends in floating-point performance are moving in favor of Field-Programmable Gate Arrays (FPGAs), hence increasing interest has grown in the scientific community to exploit this technology. We present an architecture and implementation of an FPGA-based sparse matrix-vector multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations arising from FEM applications. FEM matrices display specific sparsity patterns that can be exploited to improve the efficiency of hardware designs. Our architecture exploits FEM matrix sparsity structure to achieve a balance between performance and hardware resource requirements by relying on external SDRAM for data storage while utilizing the FPGAs computational resources in a stream-through systolic approach. The architecture is based on a pipelined linear array of processing elements (PEs) coupled with a hardware-oriented matrix striping algorithm and a partitioning scheme which enables it to process arbitrarily big matrices without changing the number of PEs in the architecture. Therefore, this architecture is only limited by the amount of external RAM available to the FPGA. The implemented SMVM-pipeline prototype contains 8 PEs and is clocked at 110 MHz obtaining a peak performance of 1.76 GFLOPS. For 8 GB/s of memory bandwidth typical of recent FPGA systems, this architecture can achieve 1.5 GFLOPS sustained performance. Using multiple instances of the pipeline, linear scaling of the peak and sustained performance can be achieved. Our stream-through architecture provides the added advantage of enabling an iterative implementation of the SMVM computation required by iterative solution techniques such as the conjugate gradient method, avoiding initialization time due to data loading and setup inside the FPGA internal memory.

Three Types of Memory in Emergency Medical Services Communication

ERIC Educational Resources Information Center

Angeli, Elizabeth L.

2015-01-01

This article examines memory and distributed cognition involved in the writing practices of emergency medical services (EMS) professionals. Results from a 16-month study indicate that EMS professionals rely on distributed cognition and three kinds of memory: individual, collaborative, and professional. Distributed cognition and the three types of…

Statistics of single unit responses in the human medial temporal lobe: A sparse and overdispersed code

NASA Astrophysics Data System (ADS)

Magyar, Andrew

The recent discovery of cells that respond to purely conceptual features of the environment (particular people, landmarks, objects, etc) in the human medial temporal lobe (MTL), has raised many questions about the nature of the neural code in humans. The goal of this dissertation is to develop a novel statistical method based upon maximum likelihood regression which will then be applied to these experiments in order to produce a quantitative description of the coding properties of the human MTL. In general, the method is applicable to any experiments in which a sequence of stimuli are presented to an organism while the binary responses of a large number of cells are recorded in parallel. The central concept underlying the approach is the total probability that a neuron responds to a random stimulus, called the neuronal sparsity. The model then estimates the distribution of response probabilities across the population of cells. Applying the method to single-unit recordings from the human medial temporal lobe, estimates of the sparsity distributions are acquired in four regions: the hippocampus, the entorhinal cortex, the amygdala, and the parahippocampal cortex. The resulting distributions are found to be sparse (large fraction of cells with a low response probability) and highly non-uniform, with a large proportion of ultra-sparse neurons that possess a very low response probability, and a smaller population of cells which respond much more frequently. Rammifications of the results are discussed in relation to the sparse coding hypothesis, and comparisons are made between the statistics of the human medial temporal lobe cells and place cells observed in the rodent hippocampus.

Thresholding functional connectomes by means of mixture modeling.

PubMed

Bielczyk, Natalia Z; Walocha, Fabian; Ebel, Patrick W; Haak, Koen V; Llera, Alberto; Buitelaar, Jan K; Glennon, Jeffrey C; Beckmann, Christian F

2018-05-01

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

NASA Astrophysics Data System (ADS)

Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun

2017-12-01

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Structured networks support sparse traveling waves in rodent somatosensory cortex.

PubMed

Moldakarimov, Samat; Bazhenov, Maxim; Feldman, Daniel E; Sejnowski, Terrence J

2018-05-15

Neurons responding to different whiskers are spatially intermixed in the superficial layer 2/3 (L2/3) of the rodent barrel cortex, where a single whisker deflection activates a sparse, distributed neuronal population that spans multiple cortical columns. How the superficial layer of the rodent barrel cortex is organized to support such distributed sensory representations is not clear. In a computer model, we tested the hypothesis that sensory representations in L2/3 of the rodent barrel cortex are formed by activity propagation horizontally within L2/3 from a site of initial activation. The model explained the observed properties of L2/3 neurons, including the low average response probability in the majority of responding L2/3 neurons, and the existence of a small subset of reliably responding L2/3 neurons. Sparsely propagating traveling waves similar to those observed in L2/3 of the rodent barrel cortex occurred in the model only when a subnetwork of strongly connected neurons was immersed in a much larger network of weakly connected neurons.

Running-Induced Systemic Cathepsin B Secretion Is Associated with Memory Function.

PubMed

Moon, Hyo Youl; Becke, Andreas; Berron, David; Becker, Benjamin; Sah, Nirnath; Benoni, Galit; Janke, Emma; Lubejko, Susan T; Greig, Nigel H; Mattison, Julie A; Duzel, Emrah; van Praag, Henriette

2016-08-09

Peripheral processes that mediate beneficial effects of exercise on the brain remain sparsely explored. Here, we show that a muscle secretory factor, cathepsin B (CTSB) protein, is important for the cognitive and neurogenic benefits of running. Proteomic analysis revealed elevated levels of CTSB in conditioned medium derived from skeletal muscle cell cultures treated with AMP-kinase agonist AICAR. Consistently, running increased CTSB levels in mouse gastrocnemius muscle and plasma. Furthermore, recombinant CTSB application enhanced expression of brain-derived neurotrophic factor (BDNF) and doublecortin (DCX) in adult hippocampal progenitor cells through a mechanism dependent on the multifunctional protein P11. In vivo, in CTSB knockout (KO) mice, running did not enhance adult hippocampal neurogenesis and spatial memory function. Interestingly, in Rhesus monkeys and humans, treadmill exercise elevated CTSB in plasma. In humans, changes in CTSB levels correlated with fitness and hippocampus-dependent memory function. Our findings suggest CTSB as a mediator of effects of exercise on cognition. Published by Elsevier Inc.

The role of maternal elaborative structure and control in children's memory and suggestibility for a past event.

PubMed

Principe, Gabrielle F; Trumbull, Jacqueline; Gardner, Grace; Van Horn, Elizabeth; Dean, Abigail M

2017-11-01

In this investigation, preschool-aged children experienced a staged event about which their mothers received misinformation suggesting that their children witnessed an activity that did not occur. Later, mothers were asked to talk about this event with their children. Consistent with previous research, mothers' provision of structure (defined as elaborative questions and statements) and degree of control (defined in terms of functional control of conversational turns) emerged as separate dimensions of maternal memory sharing style. When later interviewed by an unfamiliar examiner about the event, children whose mothers demonstrated both high structure and high control provided the highest levels of false reports of the activity suggested to mothers and generously embellished their accounts of this activity with nonoccurring details. In contrast, children with mothers who provided low structure, regardless of their degree of control, made few false reports and used sparse narrative detail. The implications of these findings for children's memory and suggestibility are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Image superresolution by midfrequency sparse representation and total variation regularization

NASA Astrophysics Data System (ADS)

Xu, Jian; Chang, Zhiguo; Fan, Jiulun; Zhao, Xiaoqiang; Wu, Xiaomin; Wang, Yanzi

2015-01-01

Machine learning has provided many good tools for superresolution, whereas existing methods still need to be improved in many aspects. On one hand, the memory and time cost should be reduced. On the other hand, the step edges of the results obtained by the existing methods are not clear enough. We do the following work. First, we propose a method to extract the midfrequency features for dictionary learning. This method brings the benefit of a reduction of the memory and time complexity without sacrificing the performance. Second, we propose a detailed wiping-off total variation (DWO-TV) regularization model to reconstruct the sharp step edges. This model adds a novel constraint on the downsampling version of the high-resolution image to wipe off the details and artifacts and sharpen the step edges. Finally, step edges produced by the DWO-TV regularization and the details provided by learning are fused. Experimental results show that the proposed method offers a desirable compromise between low time and memory cost and the reconstruction quality.

A connectome of a learning and memory center in the adult Drosophila brain

PubMed Central

Takemura, Shin-ya; Aso, Yoshinori; Hige, Toshihide; Wong, Allan; Lu, Zhiyuan; Xu, C Shan; Rivlin, Patricia K; Hess, Harald; Zhao, Ting; Parag, Toufiq; Berg, Stuart; Huang, Gary; Katz, William; Olbris, Donald J; Plaza, Stephen; Umayam, Lowell; Aniceto, Roxanne; Chang, Lei-Ann; Lauchie, Shirley; Ogundeyi, Omotara; Ordish, Christopher; Shinomiya, Aya; Sigmund, Christopher; Takemura, Satoko; Tran, Julie; Turner, Glenn C; Rubin, Gerald M; Scheffer, Louis K

2017-01-01

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall. DOI: http://dx.doi.org/10.7554/eLife.26975.001 PMID:28718765

Does rural generalist focused medical school and family medicine training make a difference? Memorial University of Newfoundland outcomes.

PubMed

Rourke, James; Asghari, Shabnam; Hurley, Oliver; Ravalia, Mohamed; Jong, Michael; Graham, Wendy; Parsons, Wanda; Duggan, Norah; O'Keefe, Danielle; Moffatt, Scott; Stringer, Katherine; Sturge Sparkes, Carolyn; Hippe, Janelle; Harris Walsh, Kristin; McKay, Donald; Samarasena, Asoka

2018-03-01

Rural recruitment and retention of physicians is a global issue. The Faculty of Medicine at Memorial University of Newfoundland, Canada, was established as a rural-focused medical school with a social accountability mandate that aimed to meet the healthcare needs of a sparse population distributed over a large landmass as well as the needs of other rural and remote areas of Canada. This study aimed to assess whether Memorial medical degree (MD) and postgraduate (PG) programs were effective at producing physicians for their province and rural physicians for Canada compared with other Canadian medical schools. This retrospective cohort study included medical school graduates who completed their PG training between 2004 and 2013 in Canada. Practice locations of study subjects were georeferenced and assigned to three geographic classes: Large Urban; Small City/Town; and Rural. Analyses were performed at two levels. (1) Provincial level analysis compared Memorial PG graduates practicing where they received their MD and/or PG training with other medical schools who are the only medical school in their province (n=4). (2) National-level analysis compared Memorial PG graduates practicing in rural Canada with all other Canadian medical schools (n=16). Descriptive and bivariate analyses were performed. Overall, 18 766 physicians practicing in Canada completed Canadian PG training (2004-2013), and of those, 8091 (43%) completed Family Medicine (FM) training. Of all physicians completing Canadian PG training, 1254 (7%) physicians were practicing rurally and of those, 1076 were family physicians. There were 379 Memorial PG graduates and of those, 208 (55%) completed FM training and 72 (19%) were practicing rurally, and of those practicing rurally, 56 were family physicians. At the national level, the percentage of all Memorial PG graduates (19.0%) and FM PG graduates (26.9%) practicing rurally was significantly better than the national average for PG (6.4%, p<0.000) and FM (12.9%, p<0.000). Among 391 physicians practicing in Newfoundland and Labrador (NL), 257 (65.7%) were Memorial PG graduates and 247 (63.2%) were Memorial MD graduates. Of the 163 FM graduates, 148 (90.8%) were Memorial FM graduates and 118 (72.4%) were Memorial MD graduates. Of the 68 in rural practice, 51 (75.0%) were Memorial PG graduates and 31 (45.6%) were Memorial MD graduates. Of the 41 FM graduates in rural practice, 39 (95.1%) were Memorial FM graduates and 22 (53.7%) were Memorial MD graduates. Two-sample proportion tests demonstrated Memorial University provided a larger proportion of its provincial physician resource supply than the other four single provincial medical schools, by medical school MD for FM (72.4% vs 44.3%, p<0.000) and for overall (63.2% vs 43.5% p<0.000), and by medical school PG for FM (90.8 % vs 72.0%, p<0.000). This study found Memorial University graduates were more likely to establish practice in rural areas compared with the national average for most program types as well as more likely to establish practice in NL compared with other single medical schools' graduates in their provinces. This study highlights the impact a comprehensive rural-focused social accountability approach can have at supplying the needs of a population both at the regional and rural national levels.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE PAGES

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

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

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition.

PubMed

James, Alex Pappachen; Fedorova, Irina; Ibrayev, Timur; Kudithipudi, Dhireesha

2017-06-01

Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.

Aerobic fitness relates to learning on a virtual Morris Water Task and hippocampal volume in adolescents.

PubMed

Herting, Megan M; Nagel, Bonnie J

2012-08-01

In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence--a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. Published by Elsevier B.V.

Aerobic fitness relates to learning on a virtual morris water task and hippocampal volume in adolescents

PubMed Central

Herting, Megan M.; Nagel, Bonnie J.

2012-01-01

In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence – a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume, or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. PMID:22610054

Distributed-Memory Fast Maximal Independent Set

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

Kanewala Appuhamilage, Thejaka Amila J.; Zalewski, Marcin J.; Lumsdaine, Andrew

The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those algorithms are designed focusing on shared-memory machines and are analyzed using the PRAM model. These algorithms do not have direct efficient distributed-memory implementations. In this paper, we extend two of Luby’s seminal MIS algorithms, “Luby(A)” and “Luby(B),” to distributed-memory execution, and we evaluatemore » their performance. We compare our results with the “Filtered MIS” implementation in the Combinatorial BLAS library for two types of synthetic graph inputs.« less

Current progress in multiple-image blind demixing algorithms

NASA Astrophysics Data System (ADS)

Szu, Harold H.

2000-06-01

Imagery edges occur naturally in human visual systems as a consequence of redundancy reduction towards `sparse and orthogonality feature maps,' which have been recently derived from the maximum entropy information-theoretical first principle of artificial neural networks. After a brief match review of such an Independent Component Analysis or Blind Source Separation of edge maps, we explore the de- mixing condition for more than two imagery objects recognizable by an intelligent pair of cameras with memory in a time-multiplex fashion.

Real-Time Characterization of Aerospace Structures Using Onboard Strain Measurement Technologies and Inverse Finite Element Method

DTIC Science & Technology

2011-09-01

strain data provided by in-situ strain sensors. The application focus is on the stain data obtained from FBG (Fiber Bragg Grating) sensor arrays...sparsely distributed lines to simulate strain data from FBG (Fiber Bragg Grating) arrays that provide either single-core (axial) or rosette (tri...when the measured strain data are sparse, as it is often the case when FBG sensors are used. For an inverse element without strain-sensor data, the

DEM generation from contours and a low-resolution DEM

NASA Astrophysics Data System (ADS)

Li, Xinghua; Shen, Huanfeng; Feng, Ruitao; Li, Jie; Zhang, Liangpei

2017-12-01

A digital elevation model (DEM) is a virtual representation of topography, where the terrain is established by the three-dimensional co-ordinates. In the framework of sparse representation, this paper investigates DEM generation from contours. Since contours are usually sparsely distributed and closely related in space, sparse spatial regularization (SSR) is enforced on them. In order to make up for the lack of spatial information, another lower spatial resolution DEM from the same geographical area is introduced. In this way, the sparse representation implements the spatial constraints in the contours and extracts the complementary information from the auxiliary DEM. Furthermore, the proposed method integrates the advantage of the unbiased estimation of kriging. For brevity, the proposed method is called the kriging and sparse spatial regularization (KSSR) method. The performance of the proposed KSSR method is demonstrated by experiments in Shuttle Radar Topography Mission (SRTM) 30 m DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m global digital elevation model (GDEM) generation from the corresponding contours and a 90 m DEM. The experiments confirm that the proposed KSSR method outperforms the traditional kriging and SSR methods, and it can be successfully used for DEM generation from contours.

Failure to pop out: Feature singletons do not capture attention under low signal-to-noise ratio conditions.

PubMed

Rangelov, Dragan; Müller, Hermann J; Zehetleitner, Michael

2017-05-01

Pop-out search implies that the target is always the first item selected, no matter how many distractors are presented. However, increasing evidence indicates that search is not entirely independent of display density even for pop-out targets: search is slower with sparse (few distractors) than with dense displays (many distractors). Despite its significance, the cause of this anomaly remains unclear. We investigated several mechanisms that could slow down search for pop-out targets. Consistent with the assumption that pop-out targets frequently fail to pop out in sparse displays, we observed greater variability of search duration for sparse displays relative to dense. Computational modeling of the response time distributions also supported the view that pop-out targets fail to pop out in sparse displays. Our findings strongly question the classical assumption that early processing of pop-out targets is independent of the distractors. Rather, the density of distractors critically influences whether or not a stimulus pops out. These results call for new, more reliable measures of pop-out search and potentially a reinterpretation of studies that used relatively sparse displays. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data

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

Lin, Youzuo; Huang, Lianjie

2015-01-28

Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversionmore » method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.« less

Target Transformation Constrained Sparse Unmixing (ttcsu) Algorithm for Retrieving Hydrous Minerals on Mars: Application to Southwest Melas Chasma

NASA Astrophysics Data System (ADS)

Lin, H.; Zhang, X.; Wu, X.; Tarnas, J. D.; Mustard, J. F.

2018-04-01

Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has been proposed to be applied to CRISM data on Mars. However, it's challenge when the endmember library increases dramatically. Here, we proposed a new methodology termed Target Transformation Constrained Sparse Unmixing (TTCSU) to accurately detect hydrous minerals on Mars. A new version of target transformation technique proposed in our recent work was used to obtain the potential detections from CRISM data. Sparse unmixing constrained with these detections as prior information was applied to CRISM single-scattering albedo images, which were calculated using a Hapke radiative transfer model. This methodology increases success rate of the automatic endmember selection of sparse unmixing and could get more accurate abundances. CRISM images with well analyzed in Southwest Melas Chasma was used to validate our methodology in this study. The sulfates jarosite was detected from Southwest Melas Chasma, the distribution is consistent with previous work and the abundance is comparable. More validations will be done in our future work.

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

Continuous Time Random Walks with memory and financial distributions

NASA Astrophysics Data System (ADS)

Montero, Miquel; Masoliver, Jaume

2017-11-01

We study financial distributions from the perspective of Continuous Time Random Walks with memory. We review some of our previous developments and apply them to financial problems. We also present some new models with memory that can be useful in characterizing tendency effects which are inherent in most markets. We also briefly study the effect on return distributions of fractional behaviors in the distribution of pausing times between successive transactions.

A quantitative theory of the functions of the hippocampal CA3 network in memory

PubMed Central

Rolls, Edmund T.

2013-01-01

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber (MF) connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory. PMID:23805074

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

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

How can individual differences in autobiographical memory distributions of older adults be explained?

PubMed

Wolf, Tabea; Zimprich, Daniel

2016-10-01

The reminiscence bump phenomenon has frequently been reported for the recall of autobiographical memories. The present study complements previous research by examining individual differences in the distribution of word-cued autobiographical memories. More importantly, we introduce predictor variables that might account for individual differences in the mean (location) and the standard deviation (scale) of individual memory distributions. All variables were derived from different theoretical accounts for the reminiscence bump phenomenon. We used a mixed location-scale logitnormal model, to analyse the 4602 autobiographical memories reported by 118 older participants. Results show reliable individual differences in the location and the scale. After controlling for age and gender, individual proportions of first-time experiences and individual proportions of positive memories, as well as the ratings on Openness to new Experiences and Self-Concept Clarity accounted for 29% of individual differences in location and 42% of individual differences in scale of autobiographical memory distributions. Results dovetail with a life-story account for the reminiscence bump which integrates central components of previous accounts.

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2012-09-30

Estimation Methods for Underwater OFDM 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. 6) Asynchronous Multiuser...multi-input multi-output ( MIMO ) OFDM is also pursued, where it is shown that the proposed hybrid initialization enables drastically improved receiver...are investigated. 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. This work studies a distributed system with

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

PubMed

Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

Measuring Sparseness in the Brain: Comment on Bowers (2009)

ERIC Educational Resources Information Center

Quian Quiroga, Rodrigo; Kreiman, Gabriel

2010-01-01

Bowers challenged the common view in favor of distributed representations in psychological modeling and the main arguments given against localist and grandmother cell coding schemes. He revisited the results of several single-cell studies, arguing that they do not support distributed representations. We praise the contribution of Bowers (2009) for…

Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

PubMed

Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

2017-03-22

Cues that predict the availability of food rewards influence motivational states and elicit food-seeking behaviors. If a cue no longer predicts food availability, then animals may adapt accordingly by inhibiting food-seeking responses. Sparsely activated sets of neurons, coined "neuronal ensembles," have been shown to encode the strength of reward-cue associations. Although alterations in intrinsic excitability have been shown to underlie many learning and memory processes, little is known about these properties specifically on cue-activated neuronal ensembles. We examined the activation patterns of cue-activated orbitofrontal cortex (OFC) and nucleus accumbens (NAc) shell ensembles using wild-type and Fos-GFP mice, which express green fluorescent protein (GFP) in activated neurons, after appetitive conditioning with sucrose and extinction learning. We also investigated the neuronal excitability of recently activated, GFP+ neurons in these brain areas using whole-cell electrophysiology in brain slices. Exposure to a sucrose cue elicited activation of neurons in both the NAc shell and OFC. In the NAc shell, but not the OFC, these activated GFP+ neurons were more excitable than surrounding GFP- neurons. After extinction, the number of neurons activated in both areas was reduced and activated ensembles in neither area exhibited altered excitability. These data suggest that learning-induced alterations in the intrinsic excitability of neuronal ensembles is regulated dynamically across different brain areas. Furthermore, we show that changes in associative strength modulate the excitability profile of activated ensembles in the NAc shell. SIGNIFICANCE STATEMENT Sparsely distributed sets of neurons called "neuronal ensembles" encode learned associations about food and cues predictive of its availability. Widespread changes in neuronal excitability have been observed in limbic brain areas after associative learning, but little is known about the excitability changes that occur specifically on neuronal ensembles that encode appetitive associations. Here, we reveal that sucrose cue exposure recruited a more excitable ensemble in the nucleus accumbens, but not orbitofrontal cortex, compared with their surrounding neurons. This excitability difference was not observed when the cue's salience was diminished after extinction learning. These novel data provide evidence that the intrinsic excitability of appetitive memory-encoding ensembles is regulated differentially across brain areas and adapts dynamically to changes in associative strength. Copyright © 2017 the authors 0270-6474/17/373160-11$15.00/0.

Sparsely-distributed organization of face and limb activations in human ventral temporal cortex

PubMed Central

Weiner, Kevin S.; Grill-Spector, Kalanit

2011-01-01

Functional magnetic resonance imaging (fMRI) has identified face- and body part-selective regions, as well as distributed activation patterns for object categories across human ventral temporal cortex (VTC), eliciting a debate regarding functional organization in VTC and neural coding of object categories. Using high-resolution fMRI, we illustrate that face- and limb-selective activations alternate in a series of largely nonoverlapping clusters in lateral VTC along the inferior occipital gyrus (IOG), fusiform gyrus (FG), and occipitotemporal sulcus (OTS). Both general linear model (GLM) and multivoxel pattern (MVP) analyses show that face- and limb-selective activations minimally overlap and that this organization is consistent across experiments and days. We provide a reliable method to separate two face-selective clusters on the middle and posterior FG (mFus and pFus), and another on the IOG using their spatial relation to limb-selective activations and retinotopic areas hV4, VO-1/2, and hMT+. Furthermore, these activations show a gradient of increasing face selectivity and decreasing limb selectivity from the IOG to the mFus. Finally, MVP analyses indicate that there is differential information for faces in lateral VTC (containing weakly- and highly-selective voxels) relative to non-selective voxels in medial VTC. These findings suggest a sparsely-distributed organization where sparseness refers to the presence of several face- and limb-selective clusters in VTC, and distributed refers to the presence of different amounts of information in highly-, weakly-, and non-selective voxels. Consequently, theories of object recognition should consider the functional and spatial constraints of neural coding across a series of nonoverlapping category-selective clusters that are themselves distributed. PMID:20457261

Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.

PubMed

Harada, Ryuhei; Nakamura, Tomotake; Shigeta, Yasuteru

2016-03-30

As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules. © 2015 Wiley Periodicals, Inc.

Correlates of self-reported, autobiographical, and mini-mental status examination defined memory deficits following electroconvulsive therapy in South India.

PubMed

Rajkumar, Anto P; Petit, Cheryl P; Rachana, Arun; Deinde, Funmi; Shyamsundar, G; Thangadurai, P; Jacob, Kuruthukulangara S

2018-04-01

Cognitive deficits, self-reported or found following electroconvulsive therapy (ECT), and their correlates are diverse. Despite the characteristics of people receiving ECT in Asia differ widely from the west, pertinent research from Asia remains sparse. We investigated the correlates of self-reported, mini-mental status examination (MMSE) defined, and autobiographical memory deficits in a cohort that received ECT in a south Indian tertiary-care setting. 76 consecutive consenting people were recruited within seven days of completing their ECT course. Memory was assessed by a subjective Likert scale, MMSE, and an autobiographical memory scale (AMS). Psychopathology was assessed by brief psychiatric rating scale, and serum cortisol levels were estimated by chemi-luminescence immunoassays. Relevant sociodemographic and clinical data were collected from the participants, and their medical records. The correlates were analysed using generalised linear models after adjusting for the effects of potential confounders. Self-reported, MMSE-defined, and autobiographical memory deficits were present in 27.6% (95%CI 17.6-37.7%), 42.1% (95%CI 31.0-53.2%), and 36.8% (95%CI 26.0-47.7%) of participants, respectively. Agreement between the memory deficits was poor. Age, less education, duration of illness, hypothyroidism, and past history of another ECT course were significantly associated with MMSE-defined deficits. Age, anaemia, past ECT course, and pre-ECT blood pressure were significantly associated with autobiographical memory deficits, while residual psychopathology and cortisol levels were significantly associated with self-reported memory deficits. Self-reported, MMSE-defined, and autobiographical memory deficits are common at the completion of ECT course, and their correlates differ. All service users receiving ECT need periodic cognitive assessments evaluating multiple cognitive domains. Copyright © 2018 Elsevier B.V. All rights reserved.

Parvalbumin interneurons constrain the size of the lateral amygdala engram.

PubMed

Morrison, Dano J; Rashid, Asim J; Yiu, Adelaide P; Yan, Chen; Frankland, Paul W; Josselyn, Sheena A

2016-11-01

Memories are thought to be represented by discrete physiological changes in the brain, collectively referred to as an engram, that allow patterns of activity present during learning to be reactivated in the future. During the formation of a conditioned fear memory, a subset of principal (excitatory) neurons in the lateral amygdala (LA) are allocated to a neuronal ensemble that encodes an association between an initially neutral stimulus and a threatening aversive stimulus. Previous experimental and computational work suggests that this subset consists of only a small proportion of all LA neurons, and that this proportion remains constant across different memories. Here we examine the mechanisms that contribute to the stability of the size of the LA component of an engram supporting a fear memory. Visualizing expression of the activity-dependent gene Arc following memory retrieval to identify neurons allocated to an engram, we first show that the overall size of the LA engram remains constant across conditions of different memory strength. That is, the strength of a memory was not correlated with the number of LA neurons allocated to the engram supporting that memory. We then examine potential mechanisms constraining the size of the LA engram by expressing inhibitory DREADDS (designer receptors exclusively activated by designer drugs) in parvalbumin-positive (PV + ) interneurons of the amygdala. We find that silencing PV + neurons during conditioning increases the size of the engram, especially in the dorsal subnucleus of the LA. These results confirm predictions from modeling studies regarding the role of inhibition in shaping the size of neuronal memory ensembles and provide additional support for the idea that neurons in the LA are sparsely allocated to the engram based on relative neuronal excitability. Copyright © 2016 Elsevier Inc. All rights reserved.

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2013-12-01

Climate change may alter the spatial distribution, composition, structure, and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate solar radiation absorbed by individual plants for understanding and predicting their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the analytical solutions of random distributions of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and is suitable for ecological models to simulate long-term transient responses of plant communities to climate change.

Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

DOE PAGES

Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...

2015-07-14

In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

A sparse matrix-vector multiplication based algorithm for accurate density matrix computations on systems of millions of atoms

NASA Astrophysics Data System (ADS)

Ghale, Purnima; Johnson, Harley T.

2018-06-01

We present an efficient sparse matrix-vector (SpMV) based method to compute the density matrix P from a given Hamiltonian in electronic structure computations. Our method is a hybrid approach based on Chebyshev-Jackson approximation theory and matrix purification methods like the second order spectral projection purification (SP2). Recent methods to compute the density matrix scale as O(N) in the number of floating point operations but are accompanied by large memory and communication overhead, and they are based on iterative use of the sparse matrix-matrix multiplication kernel (SpGEMM), which is known to be computationally irregular. In addition to irregularity in the sparse Hamiltonian H, the nonzero structure of intermediate estimates of P depends on products of H and evolves over the course of computation. On the other hand, an expansion of the density matrix P in terms of Chebyshev polynomials is straightforward and SpMV based; however, the resulting density matrix may not satisfy the required constraints exactly. In this paper, we analyze the strengths and weaknesses of the Chebyshev-Jackson polynomials and the second order spectral projection purification (SP2) method, and propose to combine them so that the accurate density matrix can be computed using the SpMV computational kernel only, and without having to store the density matrix P. Our method accomplishes these objectives by using the Chebyshev polynomial estimate as the initial guess for SP2, which is followed by using sparse matrix-vector multiplications (SpMVs) to replicate the behavior of the SP2 algorithm for purification. We demonstrate the method on a tight-binding model system of an oxide material containing more than 3 million atoms. In addition, we also present the predicted behavior of our method when applied to near-metallic Hamiltonians with a wide energy spectrum.

The Generalized Quantum Episodic Memory Model.

PubMed

Trueblood, Jennifer S; Hemmer, Pernille

2017-11-01

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over-distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over-distribution effect and subadditivity have been widely observed in item and source-memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal-detection theory. A dual-trace model called the over-distribution (OD) model (Brainerd & Reyna, 2008) can explain the episodic over-distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory (GQEM) model, which extends the Quantum Episodic Memory (QEM) model developed by Brainerd, Wang, and Reyna (2013). We test GQEM by comparing it to the OD model using data from a novel item-memory experiment and a previously published source-memory experiment (Kellen, Singmann, & Klauer, 2014) examining the over-distribution effect. Using the best-fit parameters from the over-distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory. Copyright © 2016 Cognitive Science Society, Inc.

Differentiation and Response Bias in Episodic Memory: Evidence from Reaction Time Distributions

ERIC Educational Resources Information Center

Criss, Amy H.

2010-01-01

In differentiation models, the processes of encoding and retrieval produce an increase in the distribution of memory strength for targets and a decrease in the distribution of memory strength for foils as the amount of encoding increases. This produces an increase in the hit rate and decrease in the false-alarm rate for a strongly encoded compared…

Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard

2016-10-01

A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

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

Nagasaka, Y; Matsuoka, S; Azad, A

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. Wemore » examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.« less

Silent pauses in aphasia.

PubMed

Angelopoulou, Georgia; Kasselimis, Dimitrios; Makrydakis, George; Varkanitsa, Maria; Roussos, Petros; Goutsos, Dionysis; Evdokimidis, Ioannis; Potagas, Constantin

2018-06-01

Pauses may be studied as an aspect of the temporal organization of speech, as well as an index of internal cognitive processes, such as word access, selection and retrieval, monitoring, articulatory planning, and memory. Several studies have demonstrated specific distributional patterns of pauses in typical speech. However, evidence from patients with language impairment is sparse and restricted to small-scale studies. The aim of the present study is to investigate empty pause distribution and associations between pause variables and linguistic elements in aphasia. Eighteen patients with chronic aphasia following a left hemisphere stroke were recruited. The control group consisted of 19 healthy adults matched for age, gender, and years of formal schooling. Speech samples from both groups were transcribed, and silent pauses were annotated using ELAN. Our results indicate that in both groups, pause duration distribution follows a log-normal bimodal model with significantly different thresholds between the two populations, yet specific enough for each distribution to justify classification into two distinct groups of pauses for each population: short and long. Moreover, we found differences between the patient and control group, prominently with regard to long pause duration and rate. Long pause indices were also associated with fundamental linguistics elements, such as mean length of utterance. Overall, we argue that post-stroke aphasia may induce quantitative but not qualitative alterations of pause patterns during speech, and further suggest that long pauses may serve as an index of internal cognitive processes supporting sentence planning. Our findings are discussed within the context of pause pattern quantification strategies as potential markers of cognitive changes in aphasia, further stressing the importance of such measures as an integral part of language assessment in clinical populations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Shared versus distributed memory multiprocessors

NASA Technical Reports Server (NTRS)

Jordan, Harry F.

1991-01-01

The question of whether multiprocessors should have shared or distributed memory has attracted a great deal of attention. Some researchers argue strongly for building distributed memory machines, while others argue just as strongly for programming shared memory multiprocessors. A great deal of research is underway on both types of parallel systems. Special emphasis is placed on systems with a very large number of processors for computation intensive tasks and considers research and implementation trends. It appears that the two types of systems will likely converge to a common form for large scale multiprocessors.

Optimization of sparse matrix-vector multiplication on emerging multicore platforms

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

Williams, Samuel; Oliker, Leonid; Vuduc, Richard

2007-01-01

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientificmore » study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.« less

Multitasking the Davidson algorithm for the large, sparse eigenvalue problem

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

Umar, V.M.; Fischer, C.F.

1989-01-01

The authors report how the Davidson algorithm, developed for handling the eigenvalue problem for large and sparse matrices arising in quantum chemistry, was modified for use in atomic structure calculations. To date these calculations have used traditional eigenvalue methods, which limit the range of feasible calculations because of their excessive memory requirements and unsatisfactory performance attributed to time-consuming and costly processing of zero valued elements. The replacement of a traditional matrix eigenvalue method by the Davidson algorithm reduced these limitations. Significant speedup was found, which varied with the size of the underlying problem and its sparsity. Furthermore, the range ofmore » matrix sizes that can be manipulated efficiently was expended by more than one order or magnitude. On the CRAY X-MP the code was vectorized and the importance of gather/scatter analyzed. A parallelized version of the algorithm obtained an additional 35% reduction in execution time. Speedup due to vectorization and concurrency was also measured on the Alliant FX/8.« less

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect

PubMed Central

Knoblauch, Andreas; Körner, Edgar; Körner, Ursula; Sommer, Friedrich T.

2014-01-01

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect. PMID:24858841

Inhibition of hippocampal aromatization impairs spatial memory performance in a male songbird.

PubMed

Bailey, David J; Ma, Chunqi; Soma, Kiran K; Saldanha, Colin J

2013-12-01

Recent studies have revealed the presence and regulation of aromatase at the vertebrate synapse, and identified a critical role played by presynaptic estradiol synthesis in the electrophysiological response to auditory and other social cues. However, if and how synaptic aromatization affects behavior remains to be directly tested. We have exploited 3 characteristics of the zebra finch hippocampus (HP) to test the role of synaptocrine estradiol provision on spatial memory function. Although the zebra finch HP contains abundant aromatase transcripts and enzyme activity, immunocytochemical studies reveal widespread pre- and postsynaptic, but sparse to undetectable somal, localization of this enzyme. Further, the superficial location of the avian HP makes possible the more exclusive manipulation of its neurochemical characteristics without perturbation of the neuropil and the resultant induction of astroglial aromatase. Last, as in other vertebrates, the HP is critical for spatial memory performance in this species. Here we report that local inhibition of hippocampal aromatization impairs spatial memory performance in an ecologically valid food-finding task. Local aromatase inhibition also resulted in lower levels of estradiol in the HP, but not in adjacent brain areas, and was achieved without the induction of astroglial aromatase. The observed decrement in acquisition and subsequent memory performance as a consequence of lowered aromatization was similar to that achieved by lesioning this locus. Thus, hippocampal aromatization, much of which is achieved at the synapse in this species, is critical for spatial memory performance.

Reinforcement learning and episodic memory in humans and animals: an integrative framework

PubMed Central

Gershman, Samuel J.; Daw, Nathaniel D.

2018-01-01

We review the psychology and neuroscience of reinforcement learning (RL), which has witnessed significant progress in the last two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, the simplicity of these tasks misses important aspects of reinforcement learning in the real world: (i) State spaces are high-dimensional, continuous, and partially observable; this implies that (ii) data are relatively sparse: indeed precisely the same situation may never be encountered twice; and also that (iii) rewards depend on long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, these theories have largely connected with procedural and semantic memory: how knowledge about action values or world models extracted gradually from many experiences can drive choice. This misses many aspects of memory related to traces of individual events, such as episodic memory. We suggest that these two gaps are related. In particular, the computational challenges can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (i) efficiently approximate value functions over complex state spaces, (ii) learn with very little data, and (iii) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system. PMID:27618944

Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.

PubMed

Gershman, Samuel J; Daw, Nathaniel D

2017-01-03

We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, one challenge in the study of RL is computational: The simplicity of these tasks ignores important aspects of reinforcement learning in the real world: (a) State spaces are high-dimensional, continuous, and partially observable; this implies that (b) data are relatively sparse and, indeed, precisely the same situation may never be encountered twice; furthermore, (c) rewards depend on the long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, theories of RL have largely involved procedural and semantic memory, the way in which knowledge about action values or world models extracted gradually from many experiences can drive choice. This focus on semantic memory leaves out many aspects of memory, such as episodic memory, related to the traces of individual events. We suggest that these two challenges are related. The computational challenge can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (a) efficiently approximate value functions over complex state spaces, (b) learn with very little data, and (c) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system.

Parallel pivoting combined with parallel reduction

NASA Technical Reports Server (NTRS)

Alaghband, Gita

1987-01-01

Parallel algorithms for triangularization of large, sparse, and unsymmetric matrices are presented. The method combines the parallel reduction with a new parallel pivoting technique, control over generations of fill-ins and a check for numerical stability, all done in parallel with the work being distributed over the active processes. The parallel technique uses the compatibility relation between pivots to identify parallel pivot candidates and uses the Markowitz number of pivots to minimize fill-in. This technique is not a preordering of the sparse matrix and is applied dynamically as the decomposition proceeds.

Supporting shared data structures on distributed memory architectures

NASA Technical Reports Server (NTRS)

Koelbel, Charles; Mehrotra, Piyush; Vanrosendale, John

1990-01-01

Programming nonshared memory systems is more difficult than programming shared memory systems, since there is no support for shared data structures. Current programming languages for distributed memory architectures force the user to decompose all data structures into separate pieces, with each piece owned by one of the processors in the machine, and with all communication explicitly specified by low-level message-passing primitives. A new programming environment is presented for distributed memory architectures, providing a global name space and allowing direct access to remote parts of data values. The analysis and program transformations required to implement this environment are described, and the efficiency of the resulting code on the NCUBE/7 and IPSC/2 hypercubes are described.

Mechanical Network in Titin Immunoglobulin from Force Distribution Analysis

PubMed Central

Wilmanns, Matthias; Gräter, Frauke

2009-01-01

The role of mechanical force in cellular processes is increasingly revealed by single molecule experiments and simulations of force-induced transitions in proteins. How the applied force propagates within proteins determines their mechanical behavior yet remains largely unknown. We present a new method based on molecular dynamics simulations to disclose the distribution of strain in protein structures, here for the newly determined high-resolution crystal structure of I27, a titin immunoglobulin (IG) domain. We obtain a sparse, spatially connected, and highly anisotropic mechanical network. This allows us to detect load-bearing motifs composed of interstrand hydrogen bonds and hydrophobic core interactions, including parts distal to the site to which force was applied. The role of the force distribution pattern for mechanical stability is tested by in silico unfolding of I27 mutants. We then compare the observed force pattern to the sparse network of coevolved residues found in this family. We find a remarkable overlap, suggesting the force distribution to reflect constraints for the evolutionary design of mechanical resistance in the IG family. The force distribution analysis provides a molecular interpretation of coevolution and opens the road to the study of the mechanism of signal propagation in proteins in general. PMID:19282960

Differential memory in the earth's magnetotail

NASA Technical Reports Server (NTRS)

Burkhart, G. R.; Chen, J.

1991-01-01

The process of 'differential memory' in the earth's magnetotail is studied in the framework of the modified Harris magnetotail geometry. It is verified that differential memory can generate non-Maxwellian features in the modified Harris field model. The time scales and the potentially observable distribution functions associated with the process of differential memory are investigated, and it is shown that non-Maxwelllian distributions can evolve as a test particle response to distribution function boundary conditions in a Harris field magnetotail model. The non-Maxwellian features which arise from distribution function mapping have definite time scales associated with them, which are generally shorter than the earthward convection time scale but longer than the typical Alfven crossing time.

Low Latency Messages on Distributed Memory Multiprocessors

DOE PAGES

Rosing, Matt; Saltz, Joel

1995-01-01

This article describes many of the issues in developing an efficient interface for communication on distributed memory machines. Although the hardware component of message latency is less than 1 ws on many distributed memory machines, the software latency associated with sending and receiving typed messages is on the order of 50 μs. The reason for this imbalance is that the software interface does not match the hardware. By changing the interface to match the hardware more closely, applications with fine grained communication can be put on these machines. This article describes several tests performed and many of the issues involvedmore » in supporting low latency messages on distributed memory machines.« less

Limits of the memory coefficient in measuring correlated bursts

NASA Astrophysics Data System (ADS)

Jo, Hang-Hyun; Hiraoka, Takayuki

2018-03-01

Temporal inhomogeneities in event sequences of natural and social phenomena have been characterized in terms of interevent times and correlations between interevent times. The inhomogeneities of interevent times have been extensively studied, while the correlations between interevent times, often called correlated bursts, are far from being fully understood. For measuring the correlated bursts, two relevant approaches were suggested, i.e., memory coefficient and burst size distribution. Here a burst size denotes the number of events in a bursty train detected for a given time window. Empirical analyses have revealed that the larger memory coefficient tends to be associated with the heavier tail of the burst size distribution. In particular, empirical findings in human activities appear inconsistent, such that the memory coefficient is close to 0, while burst size distributions follow a power law. In order to comprehend these observations, by assuming the conditional independence between consecutive interevent times, we derive the analytical form of the memory coefficient as a function of parameters describing interevent time and burst size distributions. Our analytical result can explain the general tendency of the larger memory coefficient being associated with the heavier tail of burst size distribution. We also find that the apparently inconsistent observations in human activities are compatible with each other, indicating that the memory coefficient has limits to measure the correlated bursts.

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

PubMed

Clark, Kevin B

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics

PubMed Central

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts. PMID:22482001

LSRN: A PARALLEL ITERATIVE SOLVER FOR STRONGLY OVER- OR UNDERDETERMINED SYSTEMS*

PubMed Central

Meng, Xiangrui; Saunders, Michael A.; Mahoney, Michael W.

2014-01-01

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to minx∈ℝn ‖Ax − b‖2, where A ∈ ℝm × n with m ≫ n or m ≪ n, and where A may be rank-deficient. Tikhonov regularization may also be included. Since A is involved only in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and LSRN automatically speeds up when A is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size ⌈γ min(m, n)⌉ × min(m, n), where γ is moderately larger than 1, e.g., γ = 2. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results show that on a shared-memory machine, LSRN is very competitive with LAPACK’s DGELSD and a fast randomized least squares solver called Blendenpik on large dense problems, and it outperforms the least squares solver from SuiteSparseQR on sparse problems without sparsity patterns that can be exploited to reduce fill-in. Further experiments show that LSRN scales well on an Amazon Elastic Compute Cloud cluster. PMID:25419094

Non-convex Statistical Optimization for Sparse Tensor Graphical Model

PubMed Central

Sun, Wei; Wang, Zhaoran; Liu, Han; Cheng, Guang

2016-01-01

We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. The penalized maximum likelihood estimation of this model involves minimizing a non-convex objective function. In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with the optimal statistical rate of convergence as well as consistent graph recovery. Notably, such an estimator achieves estimation consistency with only one tensor sample, which is unobserved in previous work. Our theoretical results are backed by thorough numerical studies. PMID:28316459

Particle Size Distributions in Atmospheric Clouds

NASA Technical Reports Server (NTRS)

Paoli, Roberto; Shariff, Karim

2003-01-01

In this note, we derive a transport equation for a spatially integrated distribution function of particles size that is suitable for sparse particle systems, such as in atmospheric clouds. This is done by integrating a Boltzmann equation for a (local) distribution function over an arbitrary but finite volume. A methodology for evolving the moments of the integrated distribution is presented. These moments can be either tracked for a finite number of discrete populations ('clusters') or treated as continuum variables.

Artificial neural networks as quantum associative memory

NASA Astrophysics Data System (ADS)

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

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

Spike timing analysis in neural networks with unsupervised synaptic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

2013-01-01

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

Visuospatial working memory in children with autism: the effect of a semantic global organization.

PubMed

Mammarella, Irene C; Giofrè, David; Caviola, Sara; Cornoldi, Cesare; Hamilton, Colin

2014-06-01

It has been reported that individuals with Autism Spectrum Disorders (ASD) perceive visual scenes as a sparse set of details rather than as a congruent and meaningful unit, failing in the extraction of the global configuration of the scene. In the present study, children with ASD were compared with typically developing (TD) children, in a visuospatial working memory task, the Visual Patterns Test (VPT). The VPT array was manipulated to vary the semantic affordance of the pattern, high semantic (global) vs. low semantic; temporal parameters were also manipulated within the change detection protocol. Overall, there was no main effect associated with Group, however there was a significant effect associated with Semantics, which was further qualified by an interaction between the Group and Semantic factors; there was only a significant effect of semantics in the TD group. The findings are discussed in light of the weak central coherence theory where the ASD group are unable to make use of long term memory semantics in order to construct global representations of the array. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optical fringe-reflection deflectometry with sparse representation

NASA Astrophysics Data System (ADS)

Xiao, Yong-Liang; Li, Sikun; Zhang, Qican; Zhong, Jianxin; Su, Xianyu; You, Zhisheng

2018-05-01

Optical fringe-reflection deflectometry is a surprisingly attractive scratch detection technique for specular surfaces owing to its unparalleled local sensibility. Full-field surface topography is obtained from a measured normal field using gradient integration. However, there may not be an ideal measured gradient field for deflectometry reconstruction in practice. Both the non-integrability condition and various kinds of image noise distributions, which are present in the indirect measured gradient field, may lead to ambiguity about the scratches on specular surfaces. In order to reduce misjudgment of scratches, sparse representation is introduced into the Southwell curl equation for deflectometry. The curl can be represented as a linear combination of the given redundant dictionary for curl and the sparsest solution for gradient refinement. The non-integrability condition and noise permutation can be overcome with sparse representation for gradient refinement. Numerical simulations demonstrate that the accuracy rate of judgment of scratches can be enhanced with sparse representation compared to the standard least-squares integration. Preliminary experiments are performed with the application of practical measured deflectometric data to verify the validity of the algorithm.

Ordering Unstructured Meshes for Sparse Matrix Computations on Leading Parallel Systems

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Li, Xiaoye; Heber, Gerd; Biswas, Rupak

2000-01-01

The ability of computers to solve hitherto intractable problems and simulate complex processes using mathematical models makes them an indispensable part of modern science and engineering. Computer simulations of large-scale realistic applications usually require solving a set of non-linear partial differential equations (PDES) over a finite region. For example, one thrust area in the DOE Grand Challenge projects is to design future accelerators such as the SpaHation Neutron Source (SNS). Our colleagues at SLAC need to model complex RFQ cavities with large aspect ratios. Unstructured grids are currently used to resolve the small features in a large computational domain; dynamic mesh adaptation will be added in the future for additional efficiency. The PDEs for electromagnetics are discretized by the FEM method, which leads to a generalized eigenvalue problem Kx = AMx, where K and M are the stiffness and mass matrices, and are very sparse. In a typical cavity model, the number of degrees of freedom is about one million. For such large eigenproblems, direct solution techniques quickly reach the memory limits. Instead, the most widely-used methods are Krylov subspace methods, such as Lanczos or Jacobi-Davidson. In all the Krylov-based algorithms, sparse matrix-vector multiplication (SPMV) must be performed repeatedly. Therefore, the efficiency of SPMV usually determines the eigensolver speed. SPMV is also one of the most heavily used kernels in large-scale numerical simulations.

Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications

PubMed Central

Papalexakis, Evangelos E.; Faloutsos, Christos; Mitchell, Tom M.; Talukdar, Partha Pratim; Sidiropoulos, Nicholas D.; Murphy, Brian

2016-01-01

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ’edible’, ’fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we enhance any CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, produces sparse and interpretable solutions, and parallelizes any CMTF algorithm, producing sparse and interpretable solutions (up to 65 fold). Additionally, we improve upon ALS, the work-horse algorithm for CMTF, with respect to efficiency and robustness to missing values. We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. Finally, we demonstrate the generality of Turbo-SMT, by applying it on a Facebook dataset (users, ’friends’, wall-postings); there, Turbo-SMT spots spammer-like anomalies. PMID:27672406

Hadza Color Terms Are Sparse, Diverse, and Distributed, and Presage the Universal Color Categories Found in Other World Languages

PubMed Central

Lindsey, Delwin T.; Brainard, David H.; Apicella, Coren L.

2016-01-01

In our empirical and theoretical study of color naming among the Hadza, a Tanzanian hunter-gatherer group, we show that Hadza color naming is sparse (the color appearance of many stimulus tiles was not named), diverse (there was little consensus in the terms for the color appearance of most tiles), and distributed (the universal color categories of world languages are revealed in nascent form within the Hadza language community, when we analyze the patterns of how individual Hadza deploy color terms). Using our Hadza data set, Witzel shows an association between two measures of color naming performance and the chroma of the stimuli. His prediction of which colored tiles will be named with what level of consensus, while interesting, does not alter the validity of our conclusions. PMID:28781734

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2014-07-01

Climate change may alter the spatial distribution, composition, structure and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate the solar radiation absorbed by individual plants in order to understand and predict their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming that crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the results of random distribution of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and can be included in vegetation models to simulate long-term transient responses of plant communities to climate change. The code and a user's manual are provided as Supplement of the paper.

Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter

DOE PAGES

Zhao, Qiang; Du, Qizhen; Gong, Xufei; ...

2018-04-06

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion ismore » replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.« less

Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter

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

Zhao, Qiang; Du, Qizhen; Gong, Xufei

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion ismore » replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.« less

The Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements

DTIC Science & Technology

2015-06-01

of uniform- versus nonuniform -pattern reconstruction, of transform function used, and of minimum randomly distributed measurements needed to...the radiation-frequency pattern’s reconstruction using uniform and nonuniform randomly distributed samples even though the pattern error manifests...5 Fig. 3 The nonuniform compressive-sensing reconstruction of the radiation

Implementation of a Fully-Balanced Periodic Tridiagonal Solver on a Parallel Distributed Memory Architecture

DTIC Science & Technology

1994-05-01

PARALLEL DISTRIBUTED MEMORY ARCHITECTURE LTJh T. M. Eidson 0 - 8 l 9 5 " G. Erlebacher _ _ _. _ DTIe QUALITY INSPECTED a Contract NAS I - 19480 May 1994...DISTRIBUTED MEMORY ARCHITECTURE T.M. Eidson * High Technology Corporation Hampton, VA 23665 G. Erlebachert Institute for Computer Applications in Science and...developed and evaluated. Simple model calculations as well as timing results are pres.nted to evaluate the various strategies. The particular

Examining procedural working memory processing in obsessive-compulsive disorder.

PubMed

Shahar, Nitzan; Teodorescu, Andrei R; Anholt, Gideon E; Karmon-Presser, Anat; Meiran, Nachshon

2017-07-01

Previous research has suggested that a deficit in working memory might underlie the difficulty of obsessive-compulsive disorder (OCD) patients to control their thoughts and actions. However, a recent meta-analyses found only small effect sizes for working memory deficits in OCD. Recently, a distinction has been made between declarative and procedural working memory. Working memory in OCD was tested mostly using declarative measurements. However, OCD symptoms typically concerns actions, making procedural working-memory more relevant. Here, we tested the operation of procedural working memory in OCD. Participants with OCD and healthy controls performed a battery of choice reaction tasks under high and low procedural working memory demands. Reaction-times (RT) were estimated using ex-Gaussian distribution fitting, revealing no group differences in the size of the RT distribution tail (i.e., τ parameter), known to be sensitive to procedural working memory manipulations. Group differences, unrelated to working memory manipulations, were found in the leading-edge of the RT distribution and analyzed using a two-stage evidence accumulation model. Modeling results suggested that perceptual difficulties might underlie the current group differences. In conclusion, our results suggest that procedural working-memory processing is most likely intact in OCD, and raise a novel, yet untested assumption regarding perceptual deficits in OCD. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Blind beam-hardening correction from Poisson measurements

NASA Astrophysics Data System (ADS)

Gu, Renliang; Dogandžić, Aleksandar

2016-02-01

We develop a sparse image reconstruction method for Poisson-distributed polychromatic X-ray computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. We employ our mass-attenuation spectrum parameterization of the noiseless measurements and express the mass- attenuation spectrum as a linear combination of B-spline basis functions of order one. A block coordinate-descent algorithm is developed for constrained minimization of a penalized Poisson negative log-likelihood (NLL) cost function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and nonnegativity and sparsity of the density map image; the image sparsity is imposed using a convex total-variation (TV) norm penalty term. This algorithm alternates between a Nesterov's proximal-gradient (NPG) step for estimating the density map image and a limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L-BFGS-B) step for estimating the incident-spectrum parameters. To accelerate convergence of the density- map NPG steps, we apply function restart and a step-size selection scheme that accounts for varying local Lipschitz constants of the Poisson NLL. Real X-ray CT reconstruction examples demonstrate the performance of the proposed scheme.

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

NASA Astrophysics Data System (ADS)

Sheppard, Adrian; Latham, Shane; Middleton, Jill; Kingston, Andrew; Myers, Glenn; Varslot, Trond; Fogden, Andrew; Sawkins, Tim; Cruikshank, Ron; Saadatfar, Mohammad; Francois, Nicolas; Arns, Christoph; Senden, Tim

2014-04-01

This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

Real-Time Data Streaming and Storing Structure for the LHD's Fusion Plasma Experiments

NASA Astrophysics Data System (ADS)

Nakanishi, Hideya; Ohsuna, Masaki; Kojima, Mamoru; Imazu, Setsuo; Nonomura, Miki; Emoto, Masahiko; Yoshida, Masanobu; Iwata, Chie; Ida, Katsumi

2016-02-01

The LHD data acquisition and archiving system, i.e., LABCOM system, has been fully equipped with high-speed real-time acquisition, streaming, and storage capabilities. To deal with more than 100 MB/s continuously generated data at each data acquisition (DAQ) node, DAQ tasks have been implemented as multitasking and multithreaded ones in which the shared memory plays the most important role for inter-process fast and massive data handling. By introducing a 10-second time chunk named “subshot,” endless data streams can be stored into a consecutive series of fixed length data blocks so that they will soon become readable by other processes even while the write process is continuing. Real-time device and environmental monitoring are also implemented in the same way with further sparse resampling. The central data storage has been separated into two layers to be capable of receiving multiple 100 MB/s inflows in parallel. For the frontend layer, high-speed SSD arrays are used as the GlusterFS distributed filesystem which can provide max. 2 GB/s throughput. Those design optimizations would be informative for implementing the next-generation data archiving system in big physics, such as ITER.

Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

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

Romero, Vicente; Bonney, Matthew; Schroeder, Benjamin

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a classmore » of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10 -4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.« less

Working memory recall precision is a more sensitive index than span.

PubMed

Zokaei, Nahid; Burnett Heyes, Stephanie; Gorgoraptis, Nikos; Budhdeo, Sanjay; Husain, Masud

2015-09-01

Delayed adjustment tasks have recently been developed to examine working memory (WM) precision, that is, the resolution with which items maintained in memory are recalled. However, despite their emerging use in experimental studies of healthy people, evaluation of patient populations is sparse. We first investigated the validity of adjustment tasks, comparing precision with classical span measures of memory across the lifespan in 114 people. Second, we asked whether precision measures can potentially provide a more sensitive measure of WM than traditional span measures. Specifically, we tested this hypothesis examining WM in a group with early, untreated Parkinson's disease (PD) and its modulation by subsequent treatment on dopaminergic medication. Span measures correlated with precision across the lifespan: in children, young, and elderly participants. However, they failed to detect changes in WM in PD patients, either pre- or post-treatment initiation. By contrast, recall precision was sensitive enough to pick up such changes. PD patients pre-medication were significantly impaired compared to controls, but improved significantly after 3 months of being established on dopaminergic medication. These findings suggest that precision methods might provide a sensitive means to investigate WM and its modulation by interventions in clinical populations. © 2014 The Authors Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

An fMRI comparison of neural activity associated with recognition of familiar melodies in younger and older adults

PubMed Central

Sikka, Ritu; Cuddy, Lola L.; Johnsrude, Ingrid S.; Vanstone, Ashley D.

2015-01-01

Several studies of semantic memory in non-musical domains involving recognition of items from long-term memory have shown an age-related shift from the medial temporal lobe structures to the frontal lobe. However, the effects of aging on musical semantic memory remain unexamined. We compared activation associated with recognition of familiar melodies in younger and older adults. Recognition follows successful retrieval from the musical lexicon that comprises a lifetime of learned musical phrases. We used the sparse-sampling technique in fMRI to determine the neural correlates of melody recognition by comparing activation when listening to familiar vs. unfamiliar melodies, and to identify age differences. Recognition-related cortical activation was detected in the right superior temporal, bilateral inferior and superior frontal, left middle orbitofrontal, bilateral precentral, and left supramarginal gyri. Region-of-interest analysis showed greater activation for younger adults in the left superior temporal gyrus and for older adults in the left superior frontal, left angular, and bilateral superior parietal regions. Our study provides powerful evidence for these musical memory networks due to a large sample (N = 40) that includes older adults. This study is the first to investigate the neural basis of melody recognition in older adults and to compare the findings to younger adults. PMID:26500480

Episodic memory in aspects of large-scale brain networks

PubMed Central

Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

2015-01-01

Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

Memory replay in balanced recurrent networks

PubMed Central

Chenkov, Nikolay; Sprekeler, Henning; Kempter, Richard

2017-01-01

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation. PMID:28135266

Modeling Confidence and Response Time in Recognition Memory

ERIC Educational Resources Information Center

Ratcliff, Roger; Starns, Jeffrey J.

2009-01-01

A new model for confidence judgments in recognition memory is presented. In the model, the match between a single test item and memory produces a distribution of evidence, with better matches corresponding to distributions with higher means. On this match dimension, confidence criteria are placed, and the areas between the criteria under the…

An Efficient Scheme for Updating Sparse Cholesky Factors

NASA Technical Reports Server (NTRS)

Raghavan, Padma

2002-01-01

Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. Currently, EPISCOPACY is being used in an application at NASA directed by J. Newman and M. James. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project. A brief description is provided in the next section.

Combining Distributed and Shared Memory Models: Approach and Evolution of the Global Arrays Toolkit

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

Nieplocha, Jarek; Harrison, Robert J.; Kumar, Mukul

2002-07-29

Both shared memory and distributed memory models have advantages and shortcomings. Shared memory model is much easier to use but it ignores data locality/placement. Given the hierarchical nature of the memory subsystems in the modern computers this characteristic might have a negative impact on performance and scalability. Various techniques, such as code restructuring to increase data reuse and introducing blocking in data accesses, can address the problem and yield performance competitive with message passing[Singh], however at the cost of compromising the ease of use feature. Distributed memory models such as message passing or one-sided communication offer performance and scalability butmore » they compromise the ease-of-use. In this context, the message-passing model is sometimes referred to as?assembly programming for the scientific computing?. The Global Arrays toolkit[GA1, GA2] attempts to offer the best features of both models. It implements a shared-memory programming model in which data locality is managed explicitly by the programmer. This management is achieved by explicit calls to functions that transfer data between a global address space (a distributed array) and local storage. In this respect, the GA model has similarities to the distributed shared-memory models that provide an explicit acquire/release protocol. However, the GA model acknowledges that remote data is slower to access than local data and allows data locality to be explicitly specified and hence managed. The GA model exposes to the programmer the hierarchical memory of modern high-performance computer systems, and by recognizing the communication overhead for remote data transfer, it promotes data reuse and locality of reference. This paper describes the characteristics of the Global Arrays programming model, capabilities of the toolkit, and discusses its evolution.« less

Programming distributed memory architectures using Kali

NASA Technical Reports Server (NTRS)

Mehrotra, Piyush; Vanrosendale, John

1990-01-01

Programming nonshared memory systems is more difficult than programming shared memory systems, in part because of the relatively low level of current programming environments for such machines. A new programming environment is presented, Kali, which provides a global name space and allows direct access to remote data values. In order to retain efficiency, Kali provides a system on annotations, allowing the user to control those aspects of the program critical to performance, such as data distribution and load balancing. The primitives and constructs provided by the language is described, and some of the issues raised in translating a Kali program for execution on distributed memory systems are also discussed.

Distributed simulation using a real-time shared memory network

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Mattern, Duane L.; Wong, Edmond; Musgrave, Jeffrey L.

1993-01-01

The Advanced Control Technology Branch of the NASA Lewis Research Center performs research in the area of advanced digital controls for aeronautic and space propulsion systems. This work requires the real-time implementation of both control software and complex dynamical models of the propulsion system. We are implementing these systems in a distributed, multi-vendor computer environment. Therefore, a need exists for real-time communication and synchronization between the distributed multi-vendor computers. A shared memory network is a potential solution which offers several advantages over other real-time communication approaches. A candidate shared memory network was tested for basic performance. The shared memory network was then used to implement a distributed simulation of a ramjet engine. The accuracy and execution time of the distributed simulation was measured and compared to the performance of the non-partitioned simulation. The ease of partitioning the simulation, the minimal time required to develop for communication between the processors and the resulting execution time all indicate that the shared memory network is a real-time communication technique worthy of serious consideration.

Sparse matrix methods research using the CSM testbed software system

NASA Technical Reports Server (NTRS)

Chu, Eleanor; George, J. Alan

1989-01-01

Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) Testbed. The primary objective was to compare the performance of state-of-the-art techniques for solving sparse systems with those that are currently available in the CSM Testbed. Thus, one of the first tasks was to become familiar with the structure of the testbed, and to install some or all of the SPARSPAK package in the testbed. A suite of subroutines to extract from the data base the relevant structural and numerical information about the matrix equations was written, and all the demonstration problems distributed with the testbed were successfully solved. These codes were documented, and performance studies comparing the SPARSPAK technology to the methods currently in the testbed were completed. In addition, some preliminary studies were done comparing some recently developed out-of-core techniques with the performance of the testbed processor INV.

Distribution of model uncertainty across multiple data streams

NASA Astrophysics Data System (ADS)

Wutzler, Thomas

2014-05-01

When confronting biogeochemical models with a diversity of observational data streams, we are faced with the problem of weighing the data streams. Without weighing or multiple blocked cost functions, model uncertainty is allocated to the sparse data streams and possible bias in processes that are strongly constraint is exported to processes that are constrained by sparse data streams only. In this study we propose an approach that aims at making model uncertainty a factor of observations uncertainty, that is constant over all data streams. Further we propose an implementation based on Monte-Carlo Markov chain sampling combined with simulated annealing that is able to determine this variance factor. The method is exemplified both with very simple models, artificial data and with an inversion of the DALEC ecosystem carbon model against multiple observations of Howland forest. We argue that the presented approach is able to help and maybe resolve the problem of bias export to sparse data streams.

Can you perceive ensembles without perceiving individuals?: The role of statistical perception in determining whether awareness overflows access.

PubMed

Ward, Emily J; Bear, Adam; Scholl, Brian J

2016-07-01

Do we see more than we can report? Psychologists and philosophers have been hotly debating this question, in part because both possibilities are supported by suggestive evidence. On one hand, phenomena such as inattentional blindness and change blindness suggest that visual awareness is especially sparse. On the other hand, experiments relating to iconic memory suggest that our in-the-moment awareness of the world is much richer than can be reported. Recent research has attempted to resolve this debate by showing that observers can accurately report the color diversity of a quickly flashed group of letters, even for letters that are unattended. If this ability requires awareness of the individual letters' colors, then this may count as a clear case of conscious awareness overflowing cognitive access. Here we explored this requirement directly: can we perceive ensemble properties of scenes even without being aware of the relevant individual features? Across several experiments that combined aspects of iconic memory with measures of change blindness, we show that observers can accurately report the color diversity of unattended stimuli, even while their self-reported awareness of the individual elements is coarse or nonexistent-and even while they are completely blind to situations in which each individual element changes color mid-trial throughout the entire experiment. We conclude that awareness of statistical properties may occur in the absence of awareness of individual features, and that such results are fully consistent with sparse visual awareness. Copyright © 2016 Elsevier B.V. All rights reserved.

Increased anxiety-like behavior but no cognitive impairments in adult rats exposed to constant light conditions during perinatal development.

PubMed

Roman, Erika; Karlsson, Oskar

2013-11-01

Shift-work is suggested to affect fetal development negatively. In particular, maternal hormonal disturbance arising from sleep deprivation or circadian rhythm changes may disturb fetal growth or lead to complications during pregnancy. Exposure to constant light is an environmental stressor that can affect the circadian system and has been shown to induce neurochemical and behavioral changes when used during the prenatal and/or postnatal period in experimental animals. However, studies investigating long-term effects of constant light in the offspring are sparse. An accidental power outage resulted in pregnant females being housed under constant light (LL) conditions for seven days of the offspring perinatal development (embryonic day 20 to postnatal day 4). The long-term effects of constant light on the behavior in the adult offspring were assessed by means of open field, object recognition, and water maze tests. In adulthood, LL-animals displayed an intact recognition memory and no deficits in spatial learning or memory. In the open field test, LL-animals exhibited higher anxiety-like behavior, observed as significantly more thigmotaxis and less ambulation. These results were confirmed in the other behavioral tests as the LL-animals spent less time exploring the objects in the object recognition test, and showed thigmotactic behavior also in the water maze test. The results confirm that early life experience can cause changes in brain development that shape brain function and add to the sparse literature on long-term effects of constant light conditions during perinatal development on specific behaviors in adulthood.

Inference of the sparse kinetic Ising model using the decimation method

NASA Astrophysics Data System (ADS)

Decelle, Aurélien; Zhang, Pan

2015-05-01

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev. Lett. 112, 070603 (2014), 10.1103/PhysRevLett.112.070603] for the static inverse Ising problem, tries to recover the topology of the inferred system by setting the weakest couplings to zero iteratively. During the decimation process the likelihood function is maximized over the remaining couplings. Unlike the ℓ1-optimization-based methods, the decimation method does not use the Laplace distribution as a heuristic choice of prior to select a sparse solution. In our case, the whole process can be done auto-matically without fixing any parameters by hand. We show that in the dynamical inference problem, where the task is to reconstruct the couplings of an Ising model given the data, the decimation process can be applied naturally into a maximum-likelihood optimization algorithm, as opposed to the static case where pseudolikelihood method needs to be adopted. We also use extensive numerical studies to validate the accuracy of our methods in dynamical inference problems. Our results illustrate that, on various topologies and with different distribution of couplings, the decimation method outperforms the widely used ℓ1-optimization-based methods.

Frequent Statement and Dereference Elimination for Imperative and Object-Oriented Distributed Programs

PubMed Central

El-Zawawy, Mohamed A.

2014-01-01

This paper introduces new approaches for the analysis of frequent statement and dereference elimination for imperative and object-oriented distributed programs running on parallel machines equipped with hierarchical memories. The paper uses languages whose address spaces are globally partitioned. Distributed programs allow defining data layout and threads writing to and reading from other thread memories. Three type systems (for imperative distributed programs) are the tools of the proposed techniques. The first type system defines for every program point a set of calculated (ready) statements and memory accesses. The second type system uses an enriched version of types of the first type system and determines which of the ready statements and memory accesses are used later in the program. The third type system uses the information gather so far to eliminate unnecessary statement computations and memory accesses (the analysis of frequent statement and dereference elimination). Extensions to these type systems are also presented to cover object-oriented distributed programs. Two advantages of our work over related work are the following. The hierarchical style of concurrent parallel computers is similar to the memory model used in this paper. In our approach, each analysis result is assigned a type derivation (serves as a correctness proof). PMID:24892098

Choice-Supportive Misremembering: A New Taxonomy and Review

PubMed Central

Lind, Martina; Visentini, Mimì; Mäntylä, Timo; Del Missier, Fabio

2017-01-01

Although the literature on the influence of memory on decisions is well developed, research on the effects of decision making on memory is rather sparse and scattered. Choice-supportive misremembering (i.e., misremembering choice-related information that boosts the chosen option and/or demotes the foregone options) has been observed in several studies and has the potential to affect future choices. Nonetheless, no attempt has been made to review the relevant literature, categorize the different types of choice-supportive misremembering observed, and critically appraise the existing evidence and proposed explanations. Thus, starting from a new theoretically motivated and empirically grounded taxonomy, we review the current research. Our taxonomy classifies choice-supportive misremembering into four conceptually distinct types: misattribution is when information is attributed to the wrong source, fact distortion when the facts are remembered in a distorted manner, false memory when items that were not part of the original decision scenarios are remembered as presented and, finally, selective forgetting is when information is selectively forgotten. After assessing the impact of various potentially moderating factors, we evaluate the evidence for each type of misremembering and conclude that the support for the phenomenon is solid in relation to misattribution when recognition memory is assessed, but significantly weaker for the other three types, and when other memory tests are used to assess memory. Finally, we review the cognitive and emotional explanations proposed for choice-supportive misremembering in the light of the available evidence and identify the main gaps in the current knowledge and the more promising avenues for future research. PMID:29255436

Genetic control of adult neurogenesis: interplay of differentiation, proliferation and survival modulates new neurons function, and memory circuits

PubMed Central

Tirone, Felice; Farioli-Vecchioli, Stefano; Micheli, Laura; Ceccarelli, Manuela; Leonardi, Luca

2013-01-01

Within the hippocampal circuitry, the basic function of the dentate gyrus is to transform the memory input coming from the enthorinal cortex into sparse and categorized outputs to CA3, in this way separating related memory information. New neurons generated in the dentate gyrus during adulthood appear to facilitate this process, allowing a better separation between closely spaced memories (pattern separation). The evidence underlying this model has been gathered essentially by ablating the newly adult-generated neurons. This approach, however, does not allow monitoring of the integration of new neurons into memory circuits and is likely to set in motion compensatory circuits, possibly leading to an underestimation of the role of new neurons. Here we review the background of the basic function of the hippocampus and of the known properties of new adult-generated neurons. In this context, we analyze the cognitive performance in mouse models generated by us and others, with modified expression of the genes Btg2 (PC3/Tis21), Btg1, Pten, BMP4, etc., where new neurons underwent a change in their differentiation rate or a partial decrease of their proliferation or survival rate rather than ablation. The effects of these modifications are equal or greater than full ablation, suggesting that the architecture of circuits, as it unfolds from the interaction between existing and new neurons, can have a greater functional impact than the sheer number of new neurons. We propose a model which attempts to measure and correlate the set of cellular changes in the process of neurogenesis with the memory function. PMID:23734097

Implementation of a parallel unstructured Euler solver on shared and distributed memory architectures

NASA Technical Reports Server (NTRS)

Mavriplis, D. J.; Das, Raja; Saltz, Joel; Vermeland, R. E.

1992-01-01

An efficient three dimensional unstructured Euler solver is parallelized on a Cray Y-MP C90 shared memory computer and on an Intel Touchstone Delta distributed memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between two differing architectures are made.

Soil carbon distribution in Alaska in relation to soil-forming factors

Treesearch

Kristofer D. Johnson; Jennifer Harden; A. David McGuire; Norman B. Bliss; James G. Bockheim; Mark Clark; Teresa Nettleton-Hollingsworth; M. Torre Jorgenson; Evan S. Kane; Michelle Mack; Johathan ODonnell; Chien-Lu Ping; Edward A.G. Schuur; Merritt R. Turetsky; David W. Valentine

2011-01-01

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change remain unclear and depend on the spatial distribution of SOC across landscapes. Uncertainties regarding the fate of SOC are greater in high-latitude systems where data are sparse and the soils are affected by sub-zero temperatures. To address these issues in Alaska, a first-...

Switching behavior of resistive change memory using oxide nanowires

NASA Astrophysics Data System (ADS)

Aono, Takashige; Sugawa, Kosuke; Shimizu, Tomohiro; Shingubara, Shoso; Takase, Kouichi

2018-06-01

Resistive change random access memory (ReRAM), which is expected to be the next-generation nonvolatile memory, often has wide switching voltage distributions due to many kinds of conductive filaments. In this study, we have tried to suppress the distribution through the structural restriction of the filament-forming area using NiO nanowires. The capacitor with Ni metal nanowires whose surface is oxidized showed good switching behaviors with narrow distributions. The knowledge gained from our study will be very helpful in producing practical ReRAM devices.

Metabolic enzymes in glial cells of the honeybee brain and their associations with aging, starvation and food response.

PubMed

Shah, Ashish K; Kreibich, Claus D; Amdam, Gro V; Münch, Daniel

2018-01-01

The honey bee has been extensively studied as a model for neuronal circuit and memory function and more recently has emerged as an unconventional model in biogerontology. Yet, the detailed knowledge of neuronal processing in the honey bee brain contrasts with the very sparse information available on glial cells. In other systems glial cells are involved in nutritional homeostasis, detoxification, and aging. These glial functions have been linked to metabolic enzymes, such as glutamine synthetase and glycogen phosphorylase. As a step in identifying functional roles and potential differences among honey bee glial types, we examined the spatial distribution of these enzymes and asked if enzyme abundance is associated with aging and other processes essential for survival. Using immunohistochemistry and confocal laser microscopy we demonstrate that glutamine synthetase and glycogen phosphorylase are abundant in glia but appear to co-localize with different glial sub-types. The overall spatial distribution of both enzymes was not homogenous and differed markedly between different neuropiles and also within each neuropil. Using semi-quantitative Western blotting we found that rapid aging, typically observed in shortest-lived worker bees (foragers), was associated with declining enzyme levels. Further, we found enzyme abundance changes after severe starvation stress, and that glutamine synthetase is associated with food response. Together, our data indicate that aging and nutritional physiology in bees are linked to glial specific metabolic enzymes. Enzyme specific localization patterns suggest a functional differentiation among identified glial types.

Cultural scripts guide recall of intensely positive life events.

PubMed

Collins, Katherine A; Pillemer, David B; Ivcevic, Zorana; Gooze, Rachel A

2007-06-01

In four studies, we examined the temporal distribution of positive and negative memories of momentous life events. College students and middle-aged adults reported events occurring from the ages of 8 to 18 years in which they had felt especially good or especially bad about themselves. Distributions of positive memories showed a marked peak at ages 17 and 18. In contrast, distributions of negative memories were relatively flat. These patterns were consistent for males and females and for younger and older adults. Content analyses indicated that a substantial proportion of positive memories from late adolescence described culturally prescribed landmark events surrounding the major life transition from high school to college. When the participants were asked for recollections from life periods that lack obvious age-linked milestone events, age distributions of positive and negative memories were similar. The results support and extend Berntsen and Rubin's (2004) conclusion that cultural expectations, or life scripts, organize recall of positive, but not negative, events.

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise. PMID:22276205

Distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye, Altai Mts, Russia (Diplopoda)

PubMed Central

Nefedieva, Julia S.; Nefediev, Pavel S.; Sakhnevich, Miroslava B.; Dyachkov, Yuri V.

2015-01-01

Abstract The distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye, Altai, Russia based on new samples from the Kyga Profile sites, as well as on partly published and freshly revised material (Mikhaljova et al. 2007, 2008, 2014, Nefedieva and Nefediev 2008, Nefediev and Nefedieva 2013, Nefedieva et al. 2014), is established. The millipede diversity is estimated to be at least 15 species and subspecies from 10 genera, 6 families and three orders. The bulk of species diversity is confined both to low- and mid-mountain chern taiga forests and high-mountain shrub tundras, whereas the highest numbers, reaching up to 130 ind./m², is shown in subalpine Pinus sibirica sparse growths. Based on clustering studied localities on species diversity similarity two groups of sites are defined: low-mountain sites and subalpine sparse growths of Pinus sibirica ones. PMID:26257540

Sparse Bayesian Inference and the Temperature Structure of the Solar Corona

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

Warren, Harry P.; Byers, Jeff M.; Crump, Nicholas A.

Measuring the temperature structure of the solar atmosphere is critical to understanding how it is heated to high temperatures. Unfortunately, the temperature of the upper atmosphere cannot be observed directly, but must be inferred from spectrally resolved observations of individual emission lines that span a wide range of temperatures. Such observations are “inverted” to determine the distribution of plasma temperatures along the line of sight. This inversion is ill posed and, in the absence of regularization, tends to produce wildly oscillatory solutions. We introduce the application of sparse Bayesian inference to the problem of inferring the temperature structure of themore » solar corona. Within a Bayesian framework a preference for solutions that utilize a minimum number of basis functions can be encoded into the prior and many ad hoc assumptions can be avoided. We demonstrate the efficacy of the Bayesian approach by considering a test library of 40 assumed temperature distributions.« less

Label-free optical imaging of membrane patches for atomic force microscopy

PubMed Central

Churnside, Allison B.; King, Gavin M.; Perkins, Thomas T.

2010-01-01

In atomic force microscopy (AFM), finding sparsely distributed regions of interest can be difficult and time-consuming. Typically, the tip is scanned until the desired object is located. This process can mechanically or chemically degrade the tip, as well as damage fragile biological samples. Protein assemblies can be detected using the back-scattered light from a focused laser beam. We previously used back-scattered light from a pair of laser foci to stabilize an AFM. In the present work, we integrate these techniques to optically image patches of purple membranes prior to AFM investigation. These rapidly acquired optical images were aligned to the subsequent AFM images to ~40 nm, since the tip position was aligned to the optical axis of the imaging laser. Thus, this label-free imaging efficiently locates sparsely distributed protein assemblies for subsequent AFM study while simultaneously minimizing degradation of the tip and the sample. PMID:21164738

Distributed learning enhances relational memory consolidation.

PubMed

Litman, Leib; Davachi, Lila

2008-09-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of forgetting relative to ML. Furthermore, we demonstrate that this savings in forgetting is specific to relational, but not item, memory. In the context of extant theories and knowledge of memory consolidation, these results suggest that an important mechanism underlying the mnemonic benefit of DL is enhanced memory consolidation. We speculate that synaptic strengthening mechanisms supporting long-term memory consolidation may be differentially mediated by the spacing of memory reactivation. These findings have broad implications for the scientific study of episodic memory consolidation and, more generally, for educational curriculum development and policy.

Using an Android application to assess registration strategies in open hepatic procedures: a planning and simulation tool

NASA Astrophysics Data System (ADS)

Doss, Derek J.; Heiselman, Jon S.; Collins, Jarrod A.; Weis, Jared A.; Clements, Logan W.; Geevarghese, Sunil K.; Miga, Michael I.

2017-03-01

Sparse surface digitization with an optically tracked stylus for use in an organ surface-based image-to-physical registration is an established approach for image-guided open liver surgery procedures. However, variability in sparse data collections during open hepatic procedures can produce disparity in registration alignments. In part, this variability arises from inconsistencies with the patterns and fidelity of collected intraoperative data. The liver lacks distinct landmarks and experiences considerable soft tissue deformation. Furthermore, data coverage of the organ is often incomplete or unevenly distributed. While more robust feature-based registration methodologies have been developed for image-guided liver surgery, it is still unclear how variation in sparse intraoperative data affects registration. In this work, we have developed an application to allow surgeons to study the performance of surface digitization patterns on registration. Given the intrinsic nature of soft-tissue, we incorporate realistic organ deformation when assessing fidelity of a rigid registration methodology. We report the construction of our application and preliminary registration results using four participants. Our preliminary results indicate that registration quality improves as users acquire more experience selecting patterns of sparse intraoperative surface data.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Cloud-In-Cell modeling of shocked particle-laden flows at a ``SPARSE'' cost

NASA Astrophysics Data System (ADS)

Taverniers, Soren; Jacobs, Gustaaf; Sen, Oishik; Udaykumar, H. S.

2017-11-01

A common tool for enabling process-scale simulations of shocked particle-laden flows is Eulerian-Lagrangian Particle-Source-In-Cell (PSIC) modeling where each particle is traced in its Lagrangian frame and treated as a mathematical point. Its dynamics are governed by Stokes drag corrected for high Reynolds and Mach numbers. The computational burden is often reduced further through a ``Cloud-In-Cell'' (CIC) approach which amalgamates groups of physical particles into computational ``macro-particles''. CIC does not account for subgrid particle fluctuations, leading to erroneous predictions of cloud dynamics. A Subgrid Particle-Averaged Reynolds-Stress Equivalent (SPARSE) model is proposed that incorporates subgrid interphase velocity and temperature perturbations. A bivariate Gaussian source distribution, whose covariance captures the cloud's deformation to first order, accounts for the particles' momentum and energy influence on the carrier gas. SPARSE is validated by conducting tests on the interaction of a particle cloud with the accelerated flow behind a shock. The cloud's average dynamics and its deformation over time predicted with SPARSE converge to their counterparts computed with reference PSIC models as the number of Gaussians is increased from 1 to 16. This work was supported by AFOSR Grant No. FA9550-16-1-0008.

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal

NASA Astrophysics Data System (ADS)

Rana, Parvez; Vauhkonen, Jari; Junttila, Virpi; Hou, Zhengyang; Gautam, Basanta; Cawkwell, Fiona; Tokola, Timo

2017-12-01

Large-diameter trees (taking DBH > 30 cm to define large trees) dominate the dynamics, function and structure of a forest ecosystem. The aim here was to employ sparse airborne laser scanning (ALS) data with a mean point density of 0.8 m-2 and the non-parametric k-most similar neighbour (k-MSN) to predict tree diameter at breast height (DBH) distributions in a subtropical forest in southern Nepal. The specific objectives were: (1) to evaluate the accuracy of the large-tree fraction of the diameter distribution; and (2) to assess the effect of the number of training areas (sample size, n) on the accuracy of the predicted tree diameter distribution. Comparison of the predicted distributions with empirical ones indicated that the large tree diameter distribution can be derived in a mixed species forest with a RMSE% of 66% and a bias% of -1.33%. It was also feasible to downsize the sample size without losing the interpretability capacity of the model. For large-diameter trees, even a reduction of half of the training plots (n = 250), giving a marginal increase in the RMSE% (1.12-1.97%) was reported compared with the original training plots (n = 500). To be consistent with these outcomes, the sample areas should capture the entire range of spatial and feature variability in order to reduce the occurrence of error.

Galaxy redshift surveys with sparse sampling

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

Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro

2013-12-01

Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should bemore » chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.« less

The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images.

PubMed

Loxley, P N

2017-10-01

The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.

Shape models of asteroids reconstructed from WISE data and sparse photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Ali-Lagoa, Victor

2017-10-01

By combining sparse-in-time photometry from the Lowell Observatory photometry database with WISE observations, we reconstructed convex shape models for about 700 new asteroids and for other ~850 we derived 'partial' models with unconstrained ecliptic longitude of the spin axis direction. In our approach, the WISE data were treated as reflected light, which enabled us to directly join them with sparse photometry into one dataset that was processed by the lightcurve inversion method. This simplified treatment of thermal infrared data turned out to provide correct results, because in most cases the phase offset between optical and thermal lightcurves was small and the correct sidereal rotation period was determined. The spin and shape parameters derived from only optical data and from a combination of optical and WISE data were very similar. The new models together with those already available in the Database of Asteroid Models from Inversion Techniques (DAMIT) represent a sample of ~1650 asteroids. When including also partial models, the total sample is about 2500 asteroids, which significantly increases the number of models with respect to those that have been available so far. We will show the distribution of spin axes for different size groups and also for several collisional families. These observed distributions in general agree with theoretical expectations proving that smaller asteroids are more affected by YORP/Yarkovsky evolution. In asteroid families, we see a clear bimodal distribution of prograde/retrograde rotation that correlates with the position to the right/left from the center of the family measured by the semimajor axis.

Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield.

PubMed

Rime, Thomas; Hartmann, Martin; Brunner, Ivano; Widmer, Franco; Zeyer, Josef; Frey, Beat

2015-03-01

Spatial patterns of microbial communities have been extensively surveyed in well-developed soils, but few studies investigated the vertical distribution of micro-organisms in newly developed soils after glacier retreat. We used 454-pyrosequencing to assess whether bacterial and fungal community structures differed between stages of soil development (SSD) characterized by an increasing vegetation cover from barren (vegetation cover: 0%/age: 10 years), sparsely vegetated (13%/60 years), transient (60%/80 years) to vegetated (95%/110 years) and depths (surface, 5 and 20 cm) along the Damma glacier forefield (Switzerland). The SSD significantly influenced the bacterial and fungal communities. Based on indicator species analyses, metabolically versatile bacteria (e.g. Geobacter) and psychrophilic yeasts (e.g. Mrakia) characterized the barren soils. Vegetated soils with higher C, N and root biomass consisted of bacteria able to degrade complex organic compounds (e.g. Candidatus Solibacter), lignocellulolytic Ascomycota (e.g. Geoglossum) and ectomycorrhizal Basidiomycota (e.g. Laccaria). Soil depth only influenced bacterial and fungal communities in barren and sparsely vegetated soils. These changes were partly due to more silt and higher soil moisture in the surface. In both soil ages, the surface was characterized by OTUs affiliated to Phormidium and Sphingobacteriales. In lower depths, however, bacterial and fungal communities differed between SSD. Lower depths of sparsely vegetated soils consisted of OTUs affiliated to Acidobacteria and Geoglossum, whereas depths of barren soils were characterized by OTUs related to Gemmatimonadetes. Overall, plant establishment drives the soil microbiota along the successional gradient but does not influence the vertical distribution of microbiota in recently deglaciated soils. © 2014 John Wiley & Sons Ltd.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

PubMed

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-09-07

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

Sparse radar imaging using 2D compressed sensing

NASA Astrophysics Data System (ADS)

Hou, Qingkai; Liu, Yang; Chen, Zengping; Su, Shaoying

2014-10-01

Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition. Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the 2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly. Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our method.

A New Adaptive Framework for Collaborative Filtering Prediction

PubMed Central

Almosallam, Ibrahim A.; Shang, Yi

2010-01-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924

A New Adaptive Framework for Collaborative Filtering Prediction.

PubMed

Almosallam, Ibrahim A; Shang, Yi

2008-06-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.

Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms

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

Williams, Samuel; Oliker, Leonid; Vuduc, Richard

2008-10-16

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific-optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD quad-core, AMD dual-core, and Intel quad-core designs, the heterogeneous STI Cell, as well as one ofmore » the first scientific studies of the highly multithreaded Sun Victoria Falls (a Niagara2 SMP). We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural trade-offs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.« less

Fast solution of elliptic partial differential equations using linear combinations of plane waves.

PubMed

Pérez-Jordá, José M

2016-02-01

Given an arbitrary elliptic partial differential equation (PDE), a procedure for obtaining its solution is proposed based on the method of Ritz: the solution is written as a linear combination of plane waves and the coefficients are obtained by variational minimization. The PDE to be solved is cast as a system of linear equations Ax=b, where the matrix A is not sparse, which prevents the straightforward application of standard iterative methods in order to solve it. This sparseness problem can be circumvented by means of a recursive bisection approach based on the fast Fourier transform, which makes it possible to implement fast versions of some stationary iterative methods (such as Gauss-Seidel) consuming O(NlogN) memory and executing an iteration in O(Nlog(2)N) time, N being the number of plane waves used. In a similar way, fast versions of Krylov subspace methods and multigrid methods can also be implemented. These procedures are tested on Poisson's equation expressed in adaptive coordinates. It is found that the best results are obtained with the GMRES method using a multigrid preconditioner with Gauss-Seidel relaxation steps.

Performance issues for iterative solvers in device simulation

NASA Technical Reports Server (NTRS)

Fan, Qing; Forsyth, P. A.; Mcmacken, J. R. F.; Tang, Wei-Pai

1994-01-01

Due to memory limitations, iterative methods have become the method of choice for large scale semiconductor device simulation. However, it is well known that these methods still suffer from reliability problems. The linear systems which appear in numerical simulation of semiconductor devices are notoriously ill-conditioned. In order to produce robust algorithms for practical problems, careful attention must be given to many implementation issues. This paper concentrates on strategies for developing robust preconditioners. In addition, effective data structures and convergence check issues are also discussed. These algorithms are compared with a standard direct sparse matrix solver on a variety of problems.

Decentralized modal identification using sparse blind source separation

NASA Astrophysics Data System (ADS)

Sadhu, A.; Hazra, B.; Narasimhan, S.; Pandey, M. D.

2011-12-01

Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time-frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure.

Vienna FORTRAN: A FORTRAN language extension for distributed memory multiprocessors

NASA Technical Reports Server (NTRS)

Chapman, Barbara; Mehrotra, Piyush; Zima, Hans

1991-01-01

Exploiting the performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna FORTRAN is a language extension of FORTRAN which provides the user with a wide range of facilities for such mapping of data structures. However, programs in Vienna FORTRAN are written using global data references. Thus, the user has the advantage of a shared memory programming paradigm while explicitly controlling the placement of data. The basic features of Vienna FORTRAN are presented along with a set of examples illustrating the use of these features.

The Effect of the Underlying Distribution in Hurst Exponent Estimation

PubMed Central

Sánchez, Miguel Ángel; Trinidad, Juan E.; García, José; Fernández, Manuel

2015-01-01

In this paper, a heavy-tailed distribution approach is considered in order to explore the behavior of actual financial time series. We show that this kind of distribution allows to properly fit the empirical distribution of the stocks from S&P500 index. In addition to that, we explain in detail why the underlying distribution of the random process under study should be taken into account before using its self-similarity exponent as a reliable tool to state whether that financial series displays long-range dependence or not. Finally, we show that, under this model, no stocks from S&P500 index show persistent memory, whereas some of them do present anti-persistent memory and most of them present no memory at all. PMID:26020942

Discrete Sparse Coding.

PubMed

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

Modeling Distributions of Immediate Memory Effects: No Strategies Needed?

ERIC Educational Resources Information Center

Beaman, C. Philip; Neath, Ian; Surprenant, Aimee M.

2008-01-01

Many models of immediate memory predict the presence or absence of various effects, but none have been tested to see whether they predict an appropriate distribution of effect sizes. The authors show that the feature model (J. S. Nairne, 1990) produces appropriate distributions of effect sizes for both the phonological confusion effect and the…

Explaining lexical-semantic deficits in specific language impairment: the role of phonological similarity, phonological working memory, and lexical competition.

PubMed

Mainela-Arnold, Elina; Evans, Julia L; Coady, Jeffry A

2010-12-01

In this study, the authors investigated potential explanations for sparse lexical-semantic representations in children with specific language impairment (SLI) and typically developing peers. The role of auditory perception, phonological working memory, and lexical competition were investigated. Participants included 32 children (ages 8;5-12;3 [years;months]): Sixteen children with SLI and 16 typically developing age- and nonverbal IQ-matched peers (CA). Children's word definitions were investigated. The words to be defined were manipulated for phonological neighborhood density. Nonword repetition and two lexical competition measures were tested as predictors of word definition abilities. Children with SLI gave word definitions with fewer content details than children in the CA group. Compared with the CA group, the definitions of children in the SLI group were not disproportionately impacted by phonological neighborhood density. Lexical competition was a significant unique predictor of children's word definitions, but nonword repetition was not. Individual differences in richness of lexical semantic representations as well as differences between children with SLI and typically developing peers may-at least, in part-be explained by processes of competition. However, difficulty with auditory perception or phonological working memory does not fully explain difficulties in lexical semantics.

Address tracing for parallel machines

NASA Technical Reports Server (NTRS)

Stunkel, Craig B.; Janssens, Bob; Fuchs, W. Kent

1991-01-01

Recently implemented parallel system address-tracing methods based on several metrics are surveyed. The issues specific to collection of traces for both shared and distributed memory parallel computers are highlighted. Five general categories of address-trace collection methods are examined: hardware-captured, interrupt-based, simulation-based, altered microcode-based, and instrumented program-based traces. The problems unique to shared memory and distributed memory multiprocessors are examined separately.

FloCon 2011 Proceedings

DTIC Science & Technology

2011-01-01

and G. Armitage. Dening and evaluating greynets (sparse darknets ). In LCN󈧉: Proceedings of the IEEE Conference on Local Computer Networks 30th...analysis of distributed darknet trac. In IMC󈧉: Proceedings of the USENIX/ACM Internet Measurement Conference, 2005. Indexing Full Packet Capture Data

Particle Filter Based Tracking in a Detection Sparse Discrete Event Simulation Environment

DTIC Science & Technology

2007-03-01

obtained by disqualifying a large number of particles. 52 (a) (b) ( c ) Figure 31. Particle Disqualification via Sanitization b...1 B. RESEARCH APPROACH..............................................................................5 C . THESIS ORGANIZATION...38 b. Detection Distribution Sampling............................................43 c . Estimated Position Calculation

Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.

PubMed

Ma, Chao; Lam, Fan; Johnson, Curtis L; Liang, Zhi-Pei

2016-02-01

To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain. © 2015 Wiley Periodicals, Inc.

Memory-assisted quantum key distribution resilient against multiple-excitation effects

NASA Astrophysics Data System (ADS)

Lo Piparo, Nicolò; Sinclair, Neil; Razavi, Mohsen

2018-01-01

Memory-assisted measurement-device-independent quantum key distribution (MA-MDI-QKD) has recently been proposed as a technique to improve the rate-versus-distance behavior of QKD systems by using existing, or nearly-achievable, quantum technologies. The promise is that MA-MDI-QKD would require less demanding quantum memories than the ones needed for probabilistic quantum repeaters. Nevertheless, early investigations suggest that, in order to beat the conventional memory-less QKD schemes, the quantum memories used in the MA-MDI-QKD protocols must have high bandwidth-storage products and short interaction times. Among different types of quantum memories, ensemble-based memories offer some of the required specifications, but they typically suffer from multiple excitation effects. To avoid the latter issue, in this paper, we propose two new variants of MA-MDI-QKD both relying on single-photon sources for entangling purposes. One is based on known techniques for entanglement distribution in quantum repeaters. This scheme turns out to offer no advantage even if one uses ideal single-photon sources. By finding the root cause of the problem, we then propose another setup, which can outperform single memory-less setups even if we allow for some imperfections in our single-photon sources. For such a scheme, we compare the key rate for different types of ensemble-based memories and show that certain classes of atomic ensembles can improve the rate-versus-distance behavior.

Execution time supports for adaptive scientific algorithms on distributed memory machines

NASA Technical Reports Server (NTRS)

Berryman, Harry; Saltz, Joel; Scroggs, Jeffrey

1990-01-01

Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated.

Execution time support for scientific programs on distributed memory machines

NASA Technical Reports Server (NTRS)

Berryman, Harry; Saltz, Joel; Scroggs, Jeffrey

1990-01-01

Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated.

Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

PubMed

Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V

2014-11-30

We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression. Copyright © 2014 John Wiley & Sons, Ltd.

N-mixture models for estimating population size from spatially replicated counts

USGS Publications Warehouse

Royle, J. Andrew

2004-01-01

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, i describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and lombard (1985, journal of american statistical association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the caroll and lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

A general parallel sparse-blocked matrix multiply for linear scaling SCF theory

NASA Astrophysics Data System (ADS)

Challacombe, Matt

2000-06-01

A general approach to the parallel sparse-blocked matrix-matrix multiply is developed in the context of linear scaling self-consistent-field (SCF) theory. The data-parallel message passing method uses non-blocking communication to overlap computation and communication. The space filling curve heuristic is used to achieve data locality for sparse matrix elements that decay with “separation”. Load balance is achieved by solving the bin packing problem for blocks with variable size.With this new method as the kernel, parallel performance of the simplified density matrix minimization (SDMM) for solution of the SCF equations is investigated for RHF/6-31G ∗∗ water clusters and RHF/3-21G estane globules. Sustained rates above 5.7 GFLOPS for the SDMM have been achieved for (H 2 O) 200 with 95 Origin 2000 processors. Scalability is found to be limited by load imbalance, which increases with decreasing granularity, due primarily to the inhomogeneous distribution of variable block sizes.

Large Scale Density Estimation of Blue and Fin Whales (LSD)

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Large Scale Density Estimation of Blue and Fin Whales ...sensors, or both. The goal of this research is to develop and implement a new method for estimating blue and fin whale density that is effective over...develop and implement a density estimation methodology for quantifying blue and fin whale abundance from passive acoustic data recorded on sparse

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2013-09-30

underwater acoustic communication technologies for autonomous distributed underwater networks, through innovative signal processing, coding, and navigation...in real enviroments , an offshore testbed has been developed to conduct field experimetns. The testbed consists of four nodes and has been deployed...Leadership by the Connecticut Technology Council. Dr. Zhaohui Wang joined the faculty of the Department of Electrical and Computer Engineering at

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

PubMed Central

Liang, Yujie; Ying, Rendong; Lu, Zhenqi; Liu, Peilin

2014-01-01

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach. PMID:25420150

Testing Mechanisms and Scales of Equilibrium Using Textural and Compositional Analysis of Porphyroblasts in Rocks with Heterogeneous Garnet Distributions

NASA Astrophysics Data System (ADS)

Ruthven, R. C.; Ketcham, R. A.; Kelly, E. D.

2015-12-01

Three-dimensional textural analysis of garnet porphyroblasts and electron microprobe analyses can, in concert, be used to pose novel tests that challenge and ultimately increase our understanding of metamorphic crystallization mechanisms. Statistical analysis of high-resolution X-ray computed tomography (CT) data of garnet porphyroblasts tells us the degree of ordering or randomness of garnets, which can be used to distinguish the rate-limiting factors behind their nucleation and growth. Electron microprobe data for cores, rims, and core-to-rim traverses are used as proxies to ascertain porphyroblast nucleation and growth rates, and the evolution of sample composition during crystallization. MnO concentrations in garnet cores serve as a proxy for the relative timing of nucleation, and rim concentrations test the hypothesis that MnO is in equilibrium sample-wide during the final stages of crystallization, and that concentrations have not been greatly altered by intracrystalline diffusion. Crystal size distributions combined with compositional data can be used to quantify the evolution of nucleation rates and sample composition during crystallization. This study focuses on quartzite schists from the Picuris Mountains with heterogeneous garnet distributions consisting of dense and sparse layers. 3D data shows that the sparse layers have smaller, less euhedral garnets, and petrographic observations show that sparse layers have more quartz and less mica than dense layers. Previous studies on rocks with homogeneously distributed garnet have shown that crystallization rates are diffusion-controlled, meaning that they are limited by diffusion of nutrients to growth and nucleation sites. This research extends this analysis to heterogeneous rocks to determine nucleation and growth rates, and test the assumption of rock-wide equilibrium for some major elements, among a set of compositionally distinct domains evolving in mm- to cm-scale proximity under identical P-T conditions.

Sparse reconstruction localization of multiple acoustic emissions in large diameter pipelines

NASA Astrophysics Data System (ADS)

Dubuc, Brennan; Ebrahimkhanlou, Arvin; Salamone, Salvatore

2017-04-01

A sparse reconstruction localization method is proposed, which is capable of localizing multiple acoustic emission events occurring closely in time. The events may be due to a number of sources, such as the growth of corrosion patches or cracks. Such acoustic emissions may yield localization failure if a triangulation method is used. The proposed method is implemented both theoretically and experimentally on large diameter thin-walled pipes. Experimental examples are presented, which demonstrate the failure of a triangulation method when multiple sources are present in this structure, while highlighting the capabilities of the proposed method. The examples are generated from experimental data of simulated acoustic emission events. The data corresponds to helical guided ultrasonic waves generated in a 3 m long large diameter pipe by pencil lead breaks on its outer surface. Acoustic emission waveforms are recorded by six sparsely distributed low-profile piezoelectric transducers instrumented on the outer surface of the pipe. The same array of transducers is used for both the proposed and the triangulation method. It is demonstrated that the proposed method is able to localize multiple events occurring closely in time. Furthermore, the matching pursuit algorithm and the basis pursuit densoising approach are each evaluated as potential numerical tools in the proposed sparse reconstruction method.

Intercomparison of two BRDF models in the estimation of the directional emissivity in MIR channel from MSG1-SEVIRI data.

PubMed

Jiang, Geng-Ming; Li, Zhao-Liang

2008-11-10

This work intercompared two Bi-directional Reflectance Distribution Function (BRDF) models, the modified Minnaert's model and the RossThick-LiSparse-R model, in the estimation of the directional emissivity in Middle Infra-Red (MIR) channel from the data acquired by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the first Meteosat Second Generation (MSG1). The bi-directional reflectances in SEVIRI channel 4 (3.9 microm) were estimated from the combined MIR and Thermal Infra-Red (TIR) data and then were used to estimate the directional emissivity in this channel with aid of the BRDF models. The results show that: (1) Both models can relatively well describe the non-Lambertian reflective behavior of land surfaces in SEVIRI channel 4; (2) The RossThick-LiSparse-R model is better than the modified Minnaert's model in modeling the bi-directional reflectances, and the directional emissivities modeled by the modified Minnaert's model are always lower than the ones obtained by the RossThick-LiSparse-R model with averaged emissivity differences of approximately 0.01 and approximately 0.04 over the vegetated and bare areas, respectively. The use of the RossThick-LiSparse-R model in the estimation of the directional emissivity in MIR channel is recommended.

Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention.

PubMed

De Dreu, Carsten K W; Nijstad, Bernard A; Baas, Matthijs; Wolsink, Inge; Roskes, Marieke

2012-05-01

Anecdotes from creative eminences suggest that executive control plays an important role in creativity, but scientific evidence is sparse. Invoking the Dual Pathway to Creativity Model, the authors hypothesize that working memory capacity (WMC) relates to creative performance because it enables persistent, focused, and systematic combining of elements and possibilities (persistence). Study 1 indeed showed that under cognitive load, participants performed worse on a creative insight task. Study 2 revealed positive associations between time-on-task and creativity among individuals high but not low in WMC, even after controlling for general intelligence. Study 3 revealed that across trials, semiprofessional cellists performed increasingly more creative improvisations when they had high rather than low WMC. Study 4 showed that WMC predicts original ideation because it allows persistent (rather than flexible) processing. The authors conclude that WMC benefits creativity because it enables the individual to maintain attention focused on the task and prevents undesirable mind wandering.

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

PubMed Central

Aso, Yoshinori; Sitaraman, Divya; Ichinose, Toshiharu; Kaun, Karla R; Vogt, Katrin; Belliart-Guérin, Ghislain; Plaçais, Pierre-Yves; Robie, Alice A; Yamagata, Nobuhiro; Schnaitmann, Christopher; Rowell, William J; Johnston, Rebecca M; Ngo, Teri-T B; Chen, Nan; Korff, Wyatt; Nitabach, Michael N; Heberlein, Ulrike; Preat, Thomas; Branson, Kristin M; Tanimoto, Hiromu; Rubin, Gerald M

2014-01-01

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by ∼2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection. DOI: http://dx.doi.org/10.7554/eLife.04580.001 PMID:25535794

Executive functioning in chronic alcoholism and Korsakoff syndrome.

PubMed

Maharasingam, Malini; Macniven, Jamie A B; Mason, Oliver J

2013-01-01

Korsakoff syndrome (KS) is characterized by dense anterograde and retrograde amnesia. There is often a temporal gradient to the retrograde amnesia, with earlier memories more readily recalled than recent memories. Executive functioning has also been found to be impaired in KS. However, research comparing executive functioning between chronic alcoholics (AL) and patients with KS has been relatively sparse to date. In a group comparison design, executive functioning in 15 KS patients and 16 chronic alcoholic patients was assessed using the Behavioural Assessment of the Dysexecutive Syndrome test (BADS) and other secondary measures. The KS group was found to be significantly more impaired than the AL group on overall performance on the BADS (p < .05). Korsakoff patients are significantly more impaired in executive functioning than non-Korsakoff chronic alcoholics. We thank the participants of the study and also acknowledge the support of the University of Nottingham, particularly Nadina Lincoln, and the North East London NHS Foundation Trust. We are also very grateful to the anonymous reviewers of earlier drafts of this manuscript for their invaluable comments.

A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties.

PubMed

Chartier, Sylvain; Giguère, Gyslain; Langlois, Dominic

2009-01-01

In this paper, we present a new recurrent bidirectional model that encompasses correlational, competitive and topological model properties. The simultaneous use of many classes of network behaviors allows for the unsupervised learning/categorization of perceptual patterns (through input compression) and the concurrent encoding of proximities in a multidimensional space. All of these operations are achieved within a common learning operation, and using a single set of defining properties. It is shown that the model can learn categories by developing prototype representations strictly from exposition to specific exemplars. Moreover, because the model is recurrent, it can reconstruct perfect outputs from incomplete and noisy patterns. Empirical exploration of the model's properties and performance shows that its ability for adequate clustering stems from: (1) properly distributing connection weights, and (2) producing a weight space with a low dispersion level (or higher density). In addition, since the model uses a sparse representation (k-winners), the size of topological neighborhood can be fixed, and no longer requires a decrease through time as was the case with classic self-organizing feature maps. Since the model's learning and transmission parameters are independent from learning trials, the model can develop stable fixed points in a constrained topological architecture, while being flexible enough to learn novel patterns.

Using the Model Coupling Toolkit to couple earth system models

USGS Publications Warehouse

Warner, J.C.; Perlin, N.; Skyllingstad, E.D.

2008-01-01

Continued advances in computational resources are providing the opportunity to operate more sophisticated numerical models. Additionally, there is an increasing demand for multidisciplinary studies that include interactions between different physical processes. Therefore there is a strong desire to develop coupled modeling systems that utilize existing models and allow efficient data exchange and model control. The basic system would entail model "1" running on "M" processors and model "2" running on "N" processors, with efficient exchange of model fields at predetermined synchronization intervals. Here we demonstrate two coupled systems: the coupling of the ocean circulation model Regional Ocean Modeling System (ROMS) to the surface wave model Simulating WAves Nearshore (SWAN), and the coupling of ROMS to the atmospheric model Coupled Ocean Atmosphere Prediction System (COAMPS). Both coupled systems use the Model Coupling Toolkit (MCT) as a mechanism for operation control and inter-model distributed memory transfer of model variables. In this paper we describe requirements and other options for model coupling, explain the MCT library, ROMS, SWAN and COAMPS models, methods for grid decomposition and sparse matrix interpolation, and provide an example from each coupled system. Methods presented in this paper are clearly applicable for coupling of other types of models. ?? 2008 Elsevier Ltd. All rights reserved.

Advanced Soil Moisture Network Technologies; Developments in Collecting in situ Measurements for Remote Sensing Missions

NASA Astrophysics Data System (ADS)

Moghaddam, M.; Silva, A. R. D.; Akbar, R.; Clewley, D.

2015-12-01

The Soil moisture Sensing Controller And oPtimal Estimator (SoilSCAPE) wireless sensor network has been developed to support Calibration and Validation activities (Cal/Val) for large scale soil moisture remote sensing missions (SMAP and AirMOSS). The technology developed here also readily supports small scale hydrological studies by providing sub-kilometer widespread soil moisture observations. An extensive collection of semi-sparse sensor clusters deployed throughout north-central California and southern Arizona provide near real time soil moisture measurements. Such a wireless network architecture, compared to conventional single points measurement profiles, allows for significant and expanded soil moisture sampling. The work presented here aims at discussing and highlighting novel and new technology developments which increase in situ soil moisture measurements' accuracy, reliability, and robustness with reduced data delivery latency. High efficiency and low maintenance custom hardware have been developed and in-field performance has been demonstrated for a period of three years. The SoilSCAPE technology incorporates (a) intelligent sensing to prevent erroneous measurement reporting, (b) on-board short term memory for data redundancy, (c) adaptive scheduling and sampling capabilities to enhance energy efficiency. A rapid streamlined data delivery architecture openly provides distribution of in situ measurements to SMAP and AirMOSS cal/val activities and other interested parties.

Automatic selection of dynamic data partitioning schemes for distributed memory multicomputers

NASA Technical Reports Server (NTRS)

Palermo, Daniel J.; Banerjee, Prithviraj

1995-01-01

For distributed memory multicomputers such as the Intel Paragon, the IBM SP-2, the NCUBE/2, and the Thinking Machines CM-5, the quality of the data partitioning for a given application is crucial to obtaining high performance. This task has traditionally been the user's responsibility, but in recent years much effort has been directed to automating the selection of data partitioning schemes. Several researchers have proposed systems that are able to produce data distributions that remain in effect for the entire execution of an application. For complex programs, however, such static data distributions may be insufficient to obtain acceptable performance. The selection of distributions that dynamically change over the course of a program's execution adds another dimension to the data partitioning problem. In this paper, we present a technique that can be used to automatically determine which partitionings are most beneficial over specific sections of a program while taking into account the added overhead of performing redistribution. This system is being built as part of the PARADIGM (PARAllelizing compiler for DIstributed memory General-purpose Multicomputers) project at the University of Illinois. The complete system will provide a fully automated means to parallelize programs written in a serial programming model obtaining high performance on a wide range of distributed-memory multicomputers.

No estuarine intertidal bathymetry? No worries! Estimating intertidal depth contours from readily available GIS data

EPA Science Inventory

The importance of littoral elevation to the distribution of intertidal species has long been a cornerstone of estuarine ecology and its historical importance to navigation cannot be understated. However, historically, intertidal elevation measurements have been sparse likely due ...

Got Game

ERIC Educational Resources Information Center

Lum, Lydia

2007-01-01

Around the country, disabled sports are often treated like second-class siblings to their able-bodied counterparts, largely because the latter bring in prestigious tournaments and bowl games, lucrative TV contracts and national exposure for top athletes and coaches. Because disabled people are so sparsely distributed in the general population, it…

Are memory traces localized or distributed?

PubMed

Thompson, R F

1991-01-01

Evidence supports the view that "memory traces" are formed in the hippocampus and in the cerebellum in classical conditioning of discrete behavioral responses (e.g. eyeblink conditioning). In the hippocampus, learning results in long-lasting increases in excitability of pyramidal neurons that appear to be localized to these neurons (i.e. changes in membrane properties and receptor function). However, these learning-altered pyramidal neurons are distributed widely throughout CA3 and CA1. Although it plays a key role in certain aspects of classical conditioning, the hippocampus is not necessary for learning and memory of the basic conditioned responses. The cerebellum and its associated brain stem circuitry, on the other hand, does appear to be essential (necessary and sufficient) for learning and memory of the conditioned response. Evidence to date is most consistent with a localized trace in the interpositus nucleus and multiple localized traces in cerebellar cortex, each involving relatively large ensembles of neurons. Perhaps "procedural" memory traces are relatively localized and "declarative" traces more widely distributed.

Distributed Saturation

NASA Technical Reports Server (NTRS)

Chung, Ming-Ying; Ciardo, Gianfranco; Siminiceanu, Radu I.

2007-01-01

The Saturation algorithm for symbolic state-space generation, has been a recent break-through in the exhaustive veri cation of complex systems, in particular globally-asyn- chronous/locally-synchronous systems. The algorithm uses a very compact Multiway Decision Diagram (MDD) encoding for states and the fastest symbolic exploration algo- rithm to date. The distributed version of Saturation uses the overall memory available on a network of workstations (NOW) to efficiently spread the memory load during the highly irregular exploration. A crucial factor in limiting the memory consumption during the symbolic state-space generation is the ability to perform garbage collection to free up the memory occupied by dead nodes. However, garbage collection over a NOW requires a nontrivial communication overhead. In addition, operation cache policies become critical while analyzing large-scale systems using the symbolic approach. In this technical report, we develop a garbage collection scheme and several operation cache policies to help on solving extremely complex systems. Experiments show that our schemes improve the performance of the original distributed implementation, SmArTNow, in terms of time and memory efficiency.

Detection and characterization of nonspecific, sparsely-populated binding modes in the early stages of complexation

PubMed Central

Cardone, A.; Bornstein, A.; Pant, H. C.; Brady, M.; Sriram, R.; Hassan, S. A.

2015-01-01

A method is proposed to study protein-ligand binding in a system governed by specific and non-specific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultra-weak associations lead instead to broader distributions, a manifestation of non-specific, sparsely-populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (pre-relaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated. PMID:25782918

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

PubMed Central

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-01-01

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure. PMID:27618040

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

PubMed

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES

PubMed Central

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

2016-01-01

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986

Bayesian X-ray computed tomography using a three-level hierarchical prior model

NASA Astrophysics Data System (ADS)

Wang, Li; Mohammad-Djafari, Ali; Gac, Nicolas

2017-06-01

In recent decades X-ray Computed Tomography (CT) image reconstruction has been largely developed in both medical and industrial domain. In this paper, we propose using the Bayesian inference approach with a new hierarchical prior model. In the proposed model, a generalised Student-t distribution is used to enforce the Haar transformation of images to be sparse. Comparisons with some state of the art methods are presented. It is shown that by using the proposed model, the sparsity of sparse representation of images is enforced, so that edges of images are preserved. Simulation results are also provided to demonstrate the effectiveness of the new hierarchical model for reconstruction with fewer projections.

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Scattered data interpolation is a problem of interest in numerous areas such as electronic imaging, smooth surface modeling, and computational geometry. Our motivation arises from applications in geology and mining, which often involve large scattered data sets and a demand for high accuracy. The method of choice is ordinary kriging. This is because it is a best unbiased estimator. Unfortunately, this interpolant is computationally very expensive to compute exactly. For n scattered data points, computing the value of a single interpolant involves solving a dense linear system of size roughly n x n. This is infeasible for large n. In practice, kriging is solved approximately by local approaches that are based on considering only a relatively small'number of points that lie close to the query point. There are many problems with this local approach, however. The first is that determining the proper neighborhood size is tricky, and is usually solved by ad hoc methods such as selecting a fixed number of nearest neighbors or all the points lying within a fixed radius. Such fixed neighborhood sizes may not work well for all query points, depending on local density of the point distribution. Local methods also suffer from the problem that the resulting interpolant is not continuous. Meyer showed that while kriging produces smooth continues surfaces, it has zero order continuity along its borders. Thus, at interface boundaries where the neighborhood changes, the interpolant behaves discontinuously. Therefore, it is important to consider and solve the global system for each interpolant. However, solving such large dense systems for each query point is impractical. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. The problems arise from the fact that the covariance functions that are used in kriging have global support. Our implementations combine, utilize, and enhance a number of different approaches that have been introduced in literature for solving large linear systems for interpolation of scattered data points. For very large systems, exact methods such as Gaussian elimination are impractical since they require 0(n(exp 3)) time and 0(n(exp 2)) storage. As Billings et al. suggested, we use an iterative approach. In particular, we use the SYMMLQ method, for solving the large but sparse ordinary kriging systems that result from tapering. The main technical issue that need to be overcome in our algorithmic solution is that the points' covariance matrix for kriging should be symmetric positive definite. The goal of tapering is to obtain a sparse approximate representation of the covariance matrix while maintaining its positive definiteness. Furrer et al. used tapering to obtain a sparse linear system of the form Ax = b, where A is the tapered symmetric positive definite covariance matrix. Thus, Cholesky factorization could be used to solve their linear systems. They implemented an efficient sparse Cholesky decomposition method. They also showed if these tapers are used for a limited class of covariance models, the solution of the system converges to the solution of the original system. Matrix A in the ordinary kriging system, while symmetric, is not positive definite. Thus, their approach is not applicable to the ordinary kriging system. Therefore, we use tapering only to obtain a sparse linear system. Then, we use SYMMLQ to solve the ordinary kriging system. We show that solving large kriging systems becomes practical via tapering and iterative methods, and results in lower estimation errors compared to traditional local approaches, and significant memory savings compared to the original global system. We also developed a more efficient variant of the sparse SYMMLQ method for large ordinary kriging systems. This approach adaptively finds the correct local neighborhood for each query point in the interpolation process.

Thermodynamic Model of Spatial Memory

NASA Astrophysics Data System (ADS)

Kaufman, Miron; Allen, P.

1998-03-01

We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.

Forensic Analysis of Window’s(Registered) Virtual Memory Incorporating the System’s Page-File

DTIC Science & Technology

2008-12-01

Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE December...data in a meaningful way. One reason for this is how memory is managed by the operating system. Data belonging to one process can be distributed...way. One reason for this is how memory is managed by the operating system. Data belonging to one process can be distributed arbitrarily across

Variational 3D-PIV with sparse descriptors

NASA Astrophysics Data System (ADS)

Lasinger, Katrin; Vogel, Christoph; Pock, Thomas; Schindler, Konrad

2018-06-01

3D particle imaging velocimetry (3D-PIV) aims to recover the flow field in a volume of fluid, which has been seeded with tracer particles and observed from multiple camera viewpoints. The first step of 3D-PIV is to reconstruct the 3D locations of the tracer particles from synchronous views of the volume. We propose a new method for iterative particle reconstruction, in which the locations and intensities of all particles are inferred in one joint energy minimization. The energy function is designed to penalize deviations between the reconstructed 3D particles and the image evidence, while at the same time aiming for a sparse set of particles. We find that the new method, without any post-processing, achieves significantly cleaner particle volumes than a conventional, tomographic MART reconstruction, and can handle a wide range of particle densities. The second step of 3D-PIV is to then recover the dense motion field from two consecutive particle reconstructions. We propose a variational model, which makes it possible to directly include physical properties, such as incompressibility and viscosity, in the estimation of the motion field. To further exploit the sparse nature of the input data, we propose a novel, compact descriptor of the local particle layout. Hence, we avoid the memory-intensive storage of high-resolution intensity volumes. Our framework is generic and allows for a variety of different data costs (correlation measures) and regularizers. We quantitatively evaluate it with both the sum of squared differences and the normalized cross-correlation, respectively with both a hard and a soft version of the incompressibility constraint.

pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data.

PubMed

Cao, Huojun; Amendt, Brad A

2016-11-01

Developmental dental anomalies are common forms of congenital defects. The molecular mechanisms of dental anomalies are poorly understood. Systematic approaches such as clustering genes based on similar expression patterns could identify novel genes involved in dental anomalies and provide a framework for understanding molecular regulatory mechanisms of these genes during tooth development (odontogenesis). A python package (pySAPC) of sparse affinity propagation clustering algorithm for large datasets was developed. Whole genome pair-wise similarity was calculated based on expression pattern similarity based on 45 microarrays of several stages during odontogenesis. pySAPC identified 743 gene clusters based on expression pattern similarity during mouse tooth development. Three clusters are significantly enriched for genes associated with dental anomalies (with FDR <0.1). The three clusters of genes have distinct expression patterns during odontogenesis. Clustering genes based on similar expression profiles recovered several known regulatory relationships for genes involved in odontogenesis, as well as many novel genes that may be involved with the same genetic pathways as genes that have already been shown to contribute to dental defects. By using sparse similarity matrix, pySAPC use much less memory and CPU time compared with the original affinity propagation program that uses a full similarity matrix. This python package will be useful for many applications where dataset(s) are too large to use full similarity matrix. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016. Published by Elsevier B.V.

Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

NASA Astrophysics Data System (ADS)

Zhou, Jian; Qi, Jinyi

2011-03-01

Statistically based iterative approaches for image reconstruction have gained much attention in medical imaging. An accurate system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction. However, an accurate system matrix is often associated with high computational cost and huge storage requirement. Here we present a method to address this problem by using sparse matrix factorization and parallel computing on a graphic processing unit (GPU).We factor the accurate system matrix into three sparse matrices: a sinogram blurring matrix, a geometric projection matrix, and an image blurring matrix. The sinogram blurring matrix models the detector response. The geometric projection matrix is based on a simple line integral model. The image blurring matrix is to compensate for the line-of-response (LOR) degradation due to the simplified geometric projection matrix. The geometric projection matrix is precomputed, while the sinogram and image blurring matrices are estimated by minimizing the difference between the factored system matrix and the original system matrix. The resulting factored system matrix has much less number of nonzero elements than the original system matrix and thus substantially reduces the storage and computation cost. The smaller size also allows an efficient implement of the forward and back projectors on GPUs, which have limited amount of memory. Our simulation studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction. The proposed technique is applicable to image reconstruction for different imaging modalities, including x-ray CT, PET, and SPECT.

The application of compressed sensing to long-term acoustic emission-based structural health monitoring

NASA Astrophysics Data System (ADS)

Cattaneo, Alessandro; Park, Gyuhae; Farrar, Charles; Mascareñas, David

2012-04-01

The acoustic emission (AE) phenomena generated by a rapid release in the internal stress of a material represent a promising technique for structural health monitoring (SHM) applications. AE events typically result in a discrete number of short-time, transient signals. The challenge associated with capturing these events using classical techniques is that very high sampling rates must be used over extended periods of time. The result is that a very large amount of data is collected to capture a phenomenon that rarely occurs. Furthermore, the high energy consumption associated with the required high sampling rates makes the implementation of high-endurance, low-power, embedded AE sensor nodes difficult to achieve. The relatively rare occurrence of AE events over long time scales implies that these measurements are inherently sparse in the spike domain. The sparse nature of AE measurements makes them an attractive candidate for the application of compressed sampling techniques. Collecting compressed measurements of sparse AE signals will relax the requirements on the sampling rate and memory demands. The focus of this work is to investigate the suitability of compressed sensing techniques for AE-based SHM. The work explores estimating AE signal statistics in the compressed domain for low-power classification applications. In the event compressed classification finds an event of interest, ι1 norm minimization will be used to reconstruct the measurement for further analysis. The impact of structured noise on compressive measurements is specifically addressed. The suitability of a particular algorithm, called Justice Pursuit, to increase robustness to a small amount of arbitrary measurement corruption is investigated.

Distributed practice can boost evaluative conditioning by increasing memory for the stimulus pairs.

PubMed

Richter, Jasmin; Gast, Anne

2017-09-01

When presenting a neutral stimulus (CS) in close temporal and spatial proximity to a positive or negative stimulus (US) the former is often observed to adopt the valence of the latter, a phenomenon named evaluative conditioning (EC). It is already well established that under most conditions, contingency awareness is important for an EC effect to occur. In addition to that, some findings suggest that awareness of the stimulus pairs is not only relevant during the learning phase, but that it is also relevant whether memory for the pairings is still available during the measurement phase. As previous research has shown that memory is better after temporally distributed than after contiguous (massed) repetitions, it seems plausible that also EC effects are moderated by distributed practice manipulations. This was tested in the current studies. In two experiments with successful distributed practice manipulations on memory, we show that also the magnitude of the EC effect was larger for pairs learned under spaced compared to massed conditions. Both effects, on memory and on EC, are found after a within-participant and after a between-participant manipulation. However, we did not find significant differences in the EC effect for different conditions of spaced practice. These findings are in line with the assumption that EC is based on similar processes as memory for the pairings. Copyright © 2017 Elsevier B.V. All rights reserved.

Time-Frequency Signal Representations Using Interpolations in Joint-Variable Domains

DTIC Science & Technology

2016-06-14

distribution kernels,” IEEE Trans. Signal Process., vol. 42, no. 5, pp. 1156–1165, May 1994. [25] G. S. Cunningham and W. J. Williams , “Kernel...interpolated data. For comparison, we include sparse reconstruction and WVD and Choi– Williams distribution (CWD) [23], which are directly applied to...Prentice-Hall, 1995. [23] H. I. Choi and W. J. Williams , “Improved time-frequency representa- tion of multicomponent signals using exponential kernels

A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems.

PubMed

Liao, Ke; Zhu, Min; Ding, Lei

2013-08-01

The present study investigated the use of transform sparseness of cortical current density on human brain surface to improve electroencephalography/magnetoencephalography (EEG/MEG) inverse solutions. Transform sparseness was assessed by evaluating compressibility of cortical current densities in transform domains. To do that, a structure compression method from computer graphics was first adopted to compress cortical surface structure, either regular or irregular, into hierarchical multi-resolution meshes. Then, a new face-based wavelet method based on generated multi-resolution meshes was proposed to compress current density functions defined on cortical surfaces. Twelve cortical surface models were built by three EEG/MEG softwares and their structural compressibility was evaluated and compared by the proposed method. Monte Carlo simulations were implemented to evaluate the performance of the proposed wavelet method in compressing various cortical current density distributions as compared to other two available vertex-based wavelet methods. The present results indicate that the face-based wavelet method can achieve higher transform sparseness than vertex-based wavelet methods. Furthermore, basis functions from the face-based wavelet method have lower coherence against typical EEG and MEG measurement systems than vertex-based wavelet methods. Both high transform sparseness and low coherent measurements suggest that the proposed face-based wavelet method can improve the performance of L1-norm regularized EEG/MEG inverse solutions, which was further demonstrated in simulations and experimental setups using MEG data. Thus, this new transform on complicated cortical structure is promising to significantly advance EEG/MEG inverse source imaging technologies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Parallel emergence of stable and dynamic memory engrams in the hippocampus.

PubMed

Hainmueller, Thomas; Bartos, Marlene

2018-06-06

During our daily life, we depend on memories of past experiences to plan future behaviour. These memories are represented by the activity of specific neuronal groups or 'engrams' 1,2 . Neuronal engrams are assembled during learning by synaptic modification, and engram reactivation represents the memorized experience 1 . Engrams of conscious memories are initially stored in the hippocampus for several days and then transferred to cortical areas 2 . In the dentate gyrus of the hippocampus, granule cells transform rich inputs from the entorhinal cortex into a sparse output, which is forwarded to the highly interconnected pyramidal cell network in hippocampal area CA3 3 . This process is thought to support pattern separation 4 (but see refs. 5,6 ). CA3 pyramidal neurons project to CA1, the hippocampal output region. Consistent with the idea of transient memory storage in the hippocampus, engrams in CA1 and CA2 do not stabilize over time 7-10 . Nevertheless, reactivation of engrams in the dentate gyrus can induce recall of artificial memories even after weeks 2 . Reconciliation of this apparent paradox will require recordings from dentate gyrus granule cells throughout learning, which has so far not been performed for more than a single day 6,11,12 . Here, we use chronic two-photon calcium imaging in head-fixed mice performing a multiple-day spatial memory task in a virtual environment to record neuronal activity in all major hippocampal subfields. Whereas pyramidal neurons in CA1-CA3 show precise and highly context-specific, but continuously changing, representations of the learned spatial sceneries in our behavioural paradigm, granule cells in the dentate gyrus have a spatial code that is stable over many days, with low place- or context-specificity. Our results suggest that synaptic weights along the hippocampal trisynaptic loop are constantly reassigned to support the formation of dynamic representations in downstream hippocampal areas based on a stable code provided by the dentate gyrus.

Autobiographical Memory in Semantic Dementia: A Longitudinal fMRI Study

ERIC Educational Resources Information Center

Maguire, Eleanor A.; Kumaran, Dharshan; Hassabis, Demis; Kopelman, Michael D.

2010-01-01

Whilst patients with semantic dementia (SD) are known to suffer from semantic memory and language impairments, there is less agreement about whether memory for personal everyday experiences, autobiographical memory, is compromised. In healthy individuals, functional MRI (fMRI) has helped to delineate a consistent and distributed brain network…

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…

Digital Correlation In Laser-Speckle Velocimetry

NASA Technical Reports Server (NTRS)

Gilbert, John A.; Mathys, Donald R.

1992-01-01

Periodic recording helps to eliminate spurious results. Improved digital-correlation process extracts velocity field of two-dimensional flow from laser-speckle images of seed particles distributed sparsely in flow. Method which involves digital correlation of images recorded at unequal intervals, completely automated and has potential to be fastest yet.

Bayesian Semiparametric Structural Equation Models with Latent Variables

ERIC Educational Resources Information Center

Yang, Mingan; Dunson, David B.

2010-01-01

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

NASA Astrophysics Data System (ADS)

Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

2018-01-01

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

An Optimized Multicolor Point-Implicit Solver for Unstructured Grid Applications on Graphics Processing Units

NASA Technical Reports Server (NTRS)

Zubair, Mohammad; Nielsen, Eric; Luitjens, Justin; Hammond, Dana

2016-01-01

In the field of computational fluid dynamics, the Navier-Stokes equations are often solved using an unstructuredgrid approach to accommodate geometric complexity. Implicit solution methodologies for such spatial discretizations generally require frequent solution of large tightly-coupled systems of block-sparse linear equations. The multicolor point-implicit solver used in the current work typically requires a significant fraction of the overall application run time. In this work, an efficient implementation of the solver for graphics processing units is proposed. Several factors present unique challenges to achieving an efficient implementation in this environment. These include the variable amount of parallelism available in different kernel calls, indirect memory access patterns, low arithmetic intensity, and the requirement to support variable block sizes. In this work, the solver is reformulated to use standard sparse and dense Basic Linear Algebra Subprograms (BLAS) functions. However, numerical experiments show that the performance of the BLAS functions available in existing CUDA libraries is suboptimal for matrices representative of those encountered in actual simulations. Instead, optimized versions of these functions are developed. Depending on block size, the new implementations show performance gains of up to 7x over the existing CUDA library functions.

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

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

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Autobiographical memory distributions for negative self-images: memories are organised around negative as well as positive aspects of identity.

PubMed

Rathbone, Clare J; Steel, Craig

2015-01-01

The relationship between developmental experiences, and an individual's emerging beliefs about themselves and the world, is central to many forms of psychotherapy. People suffering from a variety of mental health problems have been shown to use negative memories when defining the self; however, little is known about how these negative memories might be organised and relate to negative self-images. In two online studies with middle-aged (N = 18; study 1) and young (N = 56; study 2) adults, we found that participants' negative self-images (e.g., I am a failure) were associated with sets of autobiographical memories that formed clustered distributions around times of self-formation, in much the same pattern as for positive self-images (e.g., I am talented). This novel result shows that highly organised sets of salient memories may be responsible for perpetuating negative beliefs about the self. Implications for therapy are discussed.

Parallel computing for probabilistic fatigue analysis

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Lua, Yuan J.; Smith, Mark D.

1993-01-01

This paper presents the results of Phase I research to investigate the most effective parallel processing software strategies and hardware configurations for probabilistic structural analysis. We investigate the efficiency of both shared and distributed-memory architectures via a probabilistic fatigue life analysis problem. We also present a parallel programming approach, the virtual shared-memory paradigm, that is applicable across both types of hardware. Using this approach, problems can be solved on a variety of parallel configurations, including networks of single or multiprocessor workstations. We conclude that it is possible to effectively parallelize probabilistic fatigue analysis codes; however, special strategies will be needed to achieve large-scale parallelism to keep large number of processors busy and to treat problems with the large memory requirements encountered in practice. We also conclude that distributed-memory architecture is preferable to shared-memory for achieving large scale parallelism; however, in the future, the currently emerging hybrid-memory architectures will likely be optimal.

Persistently active neurons in human medial frontal and medial temporal lobe support working memory

PubMed Central

Kamiński, J; Sullivan, S; Chung, JM; Ross, IB; Mamelak, AN; Rutishauser, U

2017-01-01

Persistent neural activity is a putative mechanism for the maintenance of working memories. Persistent activity relies on the activity of a distributed network of areas, but the differential contribution of each area remains unclear. We recorded single neurons in the human medial frontal cortex and the medial temporal lobe while subjects held up to three items in memory. We found persistently active neurons in both areas. Persistent activity of hippocampal and amygdala neurons was stimulus-specific, formed stable attractors, and was predictive of memory content. Medial frontal cortex persistent activity, on the other hand, was modulated by memory load and task set but was not stimulus-specific. Trial-by-trial variability in persistent activity in both areas was related to memory strength, because it predicted the speed and accuracy by which stimuli were remembered. This work reveals, in humans, direct evidence for a distributed network of persistently active neurons supporting working memory maintenance. PMID:28218914

Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically Organized.

PubMed

Dotson, Nicholas M; Hoffman, Steven J; Goodell, Baldwin; Gray, Charles M

2018-06-15

Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy. Copyright © 2018 Elsevier Inc. All rights reserved.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

PubMed

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Sequential associative memory with nonuniformity of the layer sizes.

PubMed

Teramae, Jun-Nosuke; Fukai, Tomoki

2007-01-01

Sequence retrieval has a fundamental importance in information processing by the brain, and has extensively been studied in neural network models. Most of the previous sequential associative memory embedded sequences of memory patterns have nearly equal sizes. It was recently shown that local cortical networks display many diverse yet repeatable precise temporal sequences of neuronal activities, termed "neuronal avalanches." Interestingly, these avalanches displayed size and lifetime distributions that obey power laws. Inspired by these experimental findings, here we consider an associative memory model of binary neurons that stores sequences of memory patterns with highly variable sizes. Our analysis includes the case where the statistics of these size variations obey the above-mentioned power laws. We study the retrieval dynamics of such memory systems by analytically deriving the equations that govern the time evolution of macroscopic order parameters. We calculate the critical sequence length beyond which the network cannot retrieve memory sequences correctly. As an application of the analysis, we show how the present variability in sequential memory patterns degrades the power-law lifetime distribution of retrieved neural activities.

Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia

USDA-ARS?s Scientific Manuscript database

For a biological control program against olive fruit fly, Bactrocera oleae Rossi, olives were collected in the Himalayan foothills (China, Nepal, India, and Pakistan) to discover new natural enemies. Wild olives, Olea europaea ssp. cuspidata (Wall ex. G. Don), were sparsely distributed and fly-infes...

Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia

USDA-ARS?s Scientific Manuscript database

For a biological control program against olive fruit fly, Bactrocera oleae Rossi, olives were collected in the Himalayan foothills (China, Nepal, India, and Pakistan) to discover new natural enemies. Wild olives, Olea europaea ssp. cuspidata (Wall ex. G. Don), were sparsely distributed and fly-infe...

Feminism, Neoliberalism, and Social Studies

ERIC Educational Resources Information Center

Schmeichel, Mardi

2011-01-01

The purpose of this article is to analyze the sparse presence of women in social studies education and to consider the possibility of a confluence of feminism and neoliberalism within the most widely distributed National Council for the Social Studies (NCSS) publication, "Social Education." Using poststructural conceptions of discourse, the author…

Sparse Distributed Representation and Hierarchy: Keys to Scalable Machine Intelligence

DTIC Science & Technology

2016-04-01

Lesher, Jasmin Leveille, and Oliver Layton Neurithmic Systems, LLC APRIL 2016 Final Report Approved for public release...61101E 6. AUTHOR(S) Gerard (Rod) Rinkus, Greg Lesher, Jasmin Leveille, and Oliver Layton 5d. PROJECT NUMBER 1000 5e. TASK NUMBER N/A 5f. WORK

Geographic Mobility of Manpower in the USSR.

ERIC Educational Resources Information Center

Kossov, V. V.; Tatevosoc, R. V.

1984-01-01

The Soviet Union is experiencing substantial reduction in the growth of the working-age population, accompanied by a shift in the distribution of population growth. The government is using various means to encourage workers to move to the sparsely populated developing regions and away from the large cities. (SK)

45 CFR 303.20 - Minimum organizational and staffing requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... payments or social services functions under title IV-A or XX of the Act. In the case of a sparsely... social worker. (2) The assistance payments function means activities related to determination of... financial and medical assistance and commodities distribution or food stamps. (3) The social services...

Monitoring NEON terrestrial sites phenology with daily MODIS BRDF/albedo product and landsat data

USDA-ARS?s Scientific Manuscript database

The MODerate resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo products (MCD43) have already been in production for more than a decade. The standard product makes use of a linear “kernel-driven” RossThick-LiSparse Reciprocal (RTLSR) BRDF m...

Patterns of particle distribution in multiparticle systems by random walks with memory enhancement and decay

NASA Astrophysics Data System (ADS)

Tan, Zhi-Jie; Zou, Xian-Wu; Huang, Sheng-You; Zhang, Wei; Jin, Zhun-Zhi

2002-07-01

We investigate the pattern of particle distribution and its evolution with time in multiparticle systems using the model of random walks with memory enhancement and decay. This model describes some biological intelligent walks. With decrease in the memory decay exponent α, the distribution of particles changes from a random dispersive pattern to a locally dense one, and then returns to the random one. Correspondingly, the fractal dimension Df,p characterizing the distribution of particle positions increases from a low value to a maximum and then decreases to the low one again. This is determined by the degree of overlap of regions consisting of sites with remanent information. The second moment of the density ρ(2) was introduced to investigate the inhomogeneity of the particle distribution. The dependence of ρ(2) on α is similar to that of Df,p on α. ρ(2) increases with time as a power law in the process of adjusting the particle distribution, and then ρ(2) tends to a stable equilibrium value.

Distributed state-space generation of discrete-state stochastic models

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco; Gluckman, Joshua; Nicol, David

1995-01-01

High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models of ten requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations. The key technical problem is to find effective methods for on-the-fly partitioning, so that the state space is evenly distributed among processors. In this paper we report on the implementation of a distributed state-space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models, and report on performance observed on a network of workstations, as well as on a distributed memory multi-computer.

A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis

USGS Publications Warehouse

Irvine, Kathryn M.; Thornton, Jamie; Backus, Vickie M.; Hohmann, Matthew G.; Lehnhoff, Erik A.; Maxwell, Bruce D.; Michels, Kurt; Rew, Lisa

2013-01-01

Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species–environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

Vascular system modeling in parallel environment - distributed and shared memory approaches

PubMed Central

Jurczuk, Krzysztof; Kretowski, Marek; Bezy-Wendling, Johanne

2011-01-01

The paper presents two approaches in parallel modeling of vascular system development in internal organs. In the first approach, new parts of tissue are distributed among processors and each processor is responsible for perfusing its assigned parts of tissue to all vascular trees. Communication between processors is accomplished by passing messages and therefore this algorithm is perfectly suited for distributed memory architectures. The second approach is designed for shared memory machines. It parallelizes the perfusion process during which individual processing units perform calculations concerning different vascular trees. The experimental results, performed on a computing cluster and multi-core machines, show that both algorithms provide a significant speedup. PMID:21550891

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations

PubMed Central

Chaspari, Theodora; Tsiartas, Andreas; Tsilifis, Panagiotis; Narayanan, Shrikanth

2016-01-01

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis-Hastings-within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation and other applications. PMID:28649173

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

PubMed

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

PubMed Central

Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models. PMID:27413363

Storage of multiple single-photon pulses emitted from a quantum dot in a solid-state quantum memory.

PubMed

Tang, Jian-Shun; Zhou, Zong-Quan; Wang, Yi-Tao; Li, Yu-Long; Liu, Xiao; Hua, Yi-Lin; Zou, Yang; Wang, Shuang; He, De-Yong; Chen, Geng; Sun, Yong-Nan; Yu, Ying; Li, Mi-Feng; Zha, Guo-Wei; Ni, Hai-Qiao; Niu, Zhi-Chuan; Li, Chuan-Feng; Guo, Guang-Can

2015-10-15

Quantum repeaters are critical components for distributing entanglement over long distances in presence of unavoidable optical losses during transmission. Stimulated by the Duan-Lukin-Cirac-Zoller protocol, many improved quantum repeater protocols based on quantum memories have been proposed, which commonly focus on the entanglement-distribution rate. Among these protocols, the elimination of multiple photons (or multiple photon-pairs) and the use of multimode quantum memory are demonstrated to have the ability to greatly improve the entanglement-distribution rate. Here, we demonstrate the storage of deterministic single photons emitted from a quantum dot in a polarization-maintaining solid-state quantum memory; in addition, multi-temporal-mode memory with 1, 20 and 100 narrow single-photon pulses is also demonstrated. Multi-photons are eliminated, and only one photon at most is contained in each pulse. Moreover, the solid-state properties of both sub-systems make this configuration more stable and easier to be scalable. Our work will be helpful in the construction of efficient quantum repeaters based on all-solid-state devices.

Storage of multiple single-photon pulses emitted from a quantum dot in a solid-state quantum memory

PubMed Central

Tang, Jian-Shun; Zhou, Zong-Quan; Wang, Yi-Tao; Li, Yu-Long; Liu, Xiao; Hua, Yi-Lin; Zou, Yang; Wang, Shuang; He, De-Yong; Chen, Geng; Sun, Yong-Nan; Yu, Ying; Li, Mi-Feng; Zha, Guo-Wei; Ni, Hai-Qiao; Niu, Zhi-Chuan; Li, Chuan-Feng; Guo, Guang-Can

2015-01-01

Quantum repeaters are critical components for distributing entanglement over long distances in presence of unavoidable optical losses during transmission. Stimulated by the Duan–Lukin–Cirac–Zoller protocol, many improved quantum repeater protocols based on quantum memories have been proposed, which commonly focus on the entanglement-distribution rate. Among these protocols, the elimination of multiple photons (or multiple photon-pairs) and the use of multimode quantum memory are demonstrated to have the ability to greatly improve the entanglement-distribution rate. Here, we demonstrate the storage of deterministic single photons emitted from a quantum dot in a polarization-maintaining solid-state quantum memory; in addition, multi-temporal-mode memory with 1, 20 and 100 narrow single-photon pulses is also demonstrated. Multi-photons are eliminated, and only one photon at most is contained in each pulse. Moreover, the solid-state properties of both sub-systems make this configuration more stable and easier to be scalable. Our work will be helpful in the construction of efficient quantum repeaters based on all-solid-state devices. PMID:26468996

The relation of hippocampal subfield volumes to verbal episodic memory measured by the California Verbal Learning Test II in healthy adults.

PubMed

Aslaksen, Per M; Bystad, Martin K; Ørbo, Marte C; Vangberg, Torgil R

2018-06-08

Total hippocampal volume has previously been shown to correlate with performance on tests for verbal episodic memory. However, there are sparse evidence on how hippocampal subfield volumes are related to verbal episodic memory in healthy adults. The present study investigated the association between volumes of separate hippocampal subfields and verbal episodic memory performance in healthy volunteers. Forty-seven participants (31 females) between 20 to 71 years age underwent testing with the California Verbal Learning Test II (CVLT II), and the Wechsler Abbreviated Scale of Intelligence (WASI) to obtain an estimate of cognitive functioning. T1-weighted MR images were obtained after cognitive testing, and volumetric estimates adjusted for age and estimated total intracranial volume were calculated in the FreeSurfer 6.0 software suite for cerebral -and hippocampal structures. The sample performed within the statistical normal range on both CVLT II and WASI. Significant correlations adjusted for multiple testing were found between CVLT II subtests of total learning, free immediate recall and free delayed recall and volumes of the left Cornu Ammonis (CA) 1-4 subfields. There were no significant correlations between right hippocampal subfields and CVLT II performance, and no significant correlation between WASI results and hippocampal subfields. The present results suggest that better verbal episodic memory measured by the CVLT II is associated with relative larger volumes of specific left CA hippocampal subfields in healthy adults. Due to the small sample size and large age-span of the participants, the present findings are preliminary and should be confirmed in larger samples. Copyright © 2018 Elsevier B.V. All rights reserved.

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

2010-01-01

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Distributed Learning Enhances Relational Memory Consolidation

ERIC Educational Resources Information Center

Litman, Leib; Davachi, Lila

2008-01-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of…

Microbial ecology of extreme environments: Antarctic yeasts and growth in substrate-limited habitats

NASA Technical Reports Server (NTRS)

Vishniac, H. S.

1984-01-01

An extreme environment is by definition one with a depauperate biota. While the Ross Desert is by no means homogeneous, the most exposed and arid habitats, soils in the unglaciated high valleys, do indeed contain a very sparse biota of low diversity. So sparse that the natives could easily be outnumbered by airborne exogenous microbes. Native biota must be capable of overwintering as well as growing in the high valley summer. Tourists may undergo a few divisions before contributing their enzymes and, ultimately, elements to the soil - or may die before landing. The simplest way to demonstrate the indigenicity of a particular microbe is therefore to establish unique distribution; occurrence only in the habitat in question precludes foreign origin.

Enhancement of Immune Memory Responses to Respiratory Infection

DTIC Science & Technology

2017-08-01

Unlimited Distribution 13. SUPPLEMENTARY NOTES 14. ABSTRACT Maintenance of long - term immunological memory against pathogens is crucial for the rapid...highly expressed in memory B cells in mice, and Atg7 is required for maintenance of long - term memory B cells needed to protect against influenza...AWARD NUMBER: W81XWH-16-1-0360 TITLE: Enhancement of Immune Memory Responses to Respiratory Infection PRINCIPAL INVESTIGATORs: Dr Min Chen PhD

Autobiographical Memory and Depression in the Later Age: The Bump Is a Turning Point

ERIC Educational Resources Information Center

Gidron, Yori; Alon, Shirly

2007-01-01

This preliminary study integrated previous findings of the distribution of autobiographical memories in the later age according to their age of occurrence, with the overgeneral memory bias predictive of depression. Twenty-five non-demented, Israeli participants between 65-89 years of age provided autobiographical memories to 4 groups of word cues…

Cortex and Memory: Emergence of a New Paradigm

ERIC Educational Resources Information Center

Fuster, Joaquin M.

2009-01-01

Converging evidence from humans and nonhuman primates is obliging us to abandon conventional models in favor of a radically different, distributed-network paradigm of cortical memory. Central to the new paradigm is the concept of memory network or cognit--that is, a memory or an item of knowledge defined by a pattern of connections between neuron…

Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities.

PubMed

Graham, Daniel J; Field, David J

2007-01-01

Paintings are the product of a process that begins with ordinary vision in the natural world and ends with manipulation of pigments on canvas. Because artists must produce images that can be seen by a visual system that is thought to take advantage of statistical regularities in natural scenes, artists are likely to replicate many of these regularities in their painted art. We have tested this notion by computing basic statistical properties and modeled cell response properties for a large set of digitized paintings and natural scenes. We find that both representational and non-representational (abstract) paintings from our sample (124 images) show basic similarities to a sample of natural scenes in terms of their spatial frequency amplitude spectra, but the paintings and natural scenes show significantly different mean amplitude spectrum slopes. We also find that the intensity distributions of paintings show a lower skewness and sparseness than natural scenes. We account for this by considering the range of luminances found in the environment compared to the range available in the medium of paint. A painting's range is limited by the reflective properties of its materials. We argue that artists do not simply scale the intensity range down but use a compressive nonlinearity. In our studies, modeled retinal and cortical filter responses to the images were less sparse for the paintings than for the natural scenes. But when a compressive nonlinearity was applied to the images, both the paintings' sparseness and the modeled responses to the paintings showed the same or greater sparseness compared to the natural scenes. This suggests that artists achieve some degree of nonlinear compression in their paintings. Because paintings have captivated humans for millennia, finding basic statistical regularities in paintings' spatial structure could grant insights into the range of spatial patterns that humans find compelling.

A Comparison between Model Base Hardconstrain, Bandlimited, and Sparse-Spike Seismic Inversion: New Insights for CBM Reservoir Modelling on Muara Enim Formation, South Sumatra

NASA Astrophysics Data System (ADS)

Mohamad Noor, Faris; Adipta, Agra

2018-03-01

Coal Bed Methane (CBM) as a newly developed resource in Indonesia is one of the alternatives to relieve Indonesia’s dependencies on conventional energies. Coal resource of Muara Enim Formation is known as one of the prolific reservoirs in South Sumatra Basin. Seismic inversion and well analysis are done to determine the coal seam characteristics of Muara Enim Formation. This research uses three inversion methods, which are: model base hard- constrain, bandlimited, and sparse-spike inversion. Each type of seismic inversion has its own advantages to display the coal seam and its characteristic. Interpretation result from the analysis data shows that the Muara Enim coal seam has 20 (API) gamma ray value, 1 (gr/cc) – 1.4 (gr/cc) from density log, and low AI cutoff value range between 5000-6400 (m/s)*(g/cc). The distribution of coal seam is laterally thinning northwest to southeast. Coal seam is seen biasedly on model base hard constraint inversion and discontinued on band-limited inversion which isn’t similar to the geological model. The appropriate AI inversion is sparse spike inversion which has 0.884757 value from cross plot inversion as the best correlation value among the chosen inversion methods. Sparse Spike inversion its self-has high amplitude as a proper tool to identify coal seam continuity which commonly appears as a thin layer. Cross-sectional sparse spike inversion shows that there are possible new boreholes in CDP 3662-3722, CDP 3586-3622, and CDP 4004-4148 which is seen in seismic data as a thick coal seam.

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Memories of Physical Education

ERIC Educational Resources Information Center

Sidwell, Amy M.; Walls, Richard T.

2014-01-01

The purpose of this investigation was to explore college students' autobiographical memories of physical education (PE). Questionnaires were distributed to students enrolled in undergraduate Introduction to PE and Introduction to Communications courses. The 261 participants wrote about memories of PE. These students recalled events from Grades…

Immigration, Language Proficiency, and Autobiographical Memories: Lifespan Distribution and Second-Language Access

PubMed Central

Esposito, Alena G.; Baker-Ward, Lynne

2015-01-01

This investigation examined two controversies in the autobiographical literature: how cross-language immigration affects the distribution of autobiographical memories across the lifespan and under what circumstances language-dependent recall is observed. Both Spanish/English bilingual immigrants and English monolingual non-immigrants participated in a cue word study, with the bilingual sample taking part in a within-subject language manipulation. The expected bump in the number of memories from early life was observed for non-immigrants but not immigrants, who reported more memories for events surrounding immigration. Aspects of the methodology addressed possible reasons for past discrepant findings. Language-dependent recall was influenced by second-language proficiency. Results were interpreted as evidence that bilinguals with high second-language proficiency, in contrast to those with lower second-language proficiency, access a single conceptual store through either language. The final multi-level model predicting language-dependent recall, including second-language proficiency, age of immigration, internal language, and cue word language, explained ¾ of the between-person variance and ⅕ of the within-person variance. We arrive at two conclusions. First, major life transitions influence the distribution of memories. Second, concept representation across multiple languages follows a developmental model. In addition, the results underscore the importance of considering language experience in research involving memory reports. PMID:26274061

Immigration, language proficiency, and autobiographical memories: Lifespan distribution and second-language access.

PubMed

Esposito, Alena G; Baker-Ward, Lynne

2016-08-01

This investigation examined two controversies in the autobiographical literature: how cross-language immigration affects the distribution of autobiographical memories across the lifespan and under what circumstances language-dependent recall is observed. Both Spanish/English bilingual immigrants and English monolingual non-immigrants participated in a cue word study, with the bilingual sample taking part in a within-subject language manipulation. The expected bump in the number of memories from early life was observed for non-immigrants but not immigrants, who reported more memories for events surrounding immigration. Aspects of the methodology addressed possible reasons for past discrepant findings. Language-dependent recall was influenced by second-language proficiency. Results were interpreted as evidence that bilinguals with high second-language proficiency, in contrast to those with lower second-language proficiency, access a single conceptual store through either language. The final multi-level model predicting language-dependent recall, including second-language proficiency, age of immigration, internal language, and cue word language, explained ¾ of the between-person variance and (1)/5 of the within-person variance. We arrive at two conclusions. First, major life transitions influence the distribution of memories. Second, concept representation across multiple languages follows a developmental model. In addition, the results underscore the importance of considering language experience in research involving memory reports.

First record of Tettigettalna mariae Quartau & Boulard, 1995 (Insecta: Hemiptera: Cicadoidea) in Spain

PubMed Central

2013-01-01

Abstract Tettigettalna mariae Quartau & Boulard 1995 is recorded for the first time in Spain. Thought to be endemic to Portugal (occurring in the southern province of Algarve), the present paper adds its distribution to southern Spain, being an Iberian endemism. The acoustic signals of the new specimens collected were recorded in different localities of Huelva province, in Andalusia during August 2012. According to their present known distribution, specimens of Tettigettalna mariae tend to be sparsely distributed in small range populations in southern Iberian Peninsula, favouring wooded areas with Pinus pinea. PMID:24723772

Sparse and redundant representations for inverse problems and recognition

NASA Astrophysics Data System (ADS)

Patel, Vishal M.

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.

Random-access scanning microscopy for 3D imaging in awake behaving animals

PubMed Central

Nadella, K. M. Naga Srinivas; Roš, Hana; Baragli, Chiara; Griffiths, Victoria A.; Konstantinou, George; Koimtzis, Theo; Evans, Geoffrey J.; Kirkby, Paul A.; Silver, R. Angus

2018-01-01

Understanding how neural circuits process information requires rapid measurements from identified neurons distributed in 3D space. Here we describe an acousto-optic lens two-photon microscope that performs high-speed focussing and line-scanning within a volume spanning hundreds of micrometres. We demonstrate its random access functionality by selectively imaging cerebellar interneurons sparsely distributed in 3D and by simultaneously recording from the soma, proximal and distal dendrites of neocortical pyramidal cells in behaving mice. PMID:27749836

Time-Frequency Based Instantaneous Frequency Estimation of Sparse Signals from an Incomplete Set of Samples

DTIC Science & Technology

2014-06-17

100 0 2 4 Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function 0 50 100 0 2 4 L- Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function ...bilinear or higher order autocorrelation functions will increase the number of missing samples, the analysis shows that accurate instantaneous...frequency estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with

A ten-year follow-up of a study of memory for the attack of September 11, 2001: Flashbulb memories and memories for flashbulb events.

PubMed

Hirst, William; Phelps, Elizabeth A; Meksin, Robert; Vaidya, Chandan J; Johnson, Marcia K; Mitchell, Karen J; Buckner, Randy L; Budson, Andrew E; Gabrieli, John D E; Lustig, Cindy; Mather, Mara; Ochsner, Kevin N; Schacter, Daniel; Simons, Jon S; Lyle, Keith B; Cuc, Alexandru F; Olsson, Andreas

2015-06-01

Within a week of the attack of September 11, 2001, a consortium of researchers from across the United States distributed a survey asking about the circumstances in which respondents learned of the attack (their flashbulb memories) and the facts about the attack itself (their event memories). Follow-up surveys were distributed 11, 25, and 119 months after the attack. The study, therefore, examines retention of flashbulb memories and event memories at a substantially longer retention interval than any previous study using a test-retest methodology, allowing for the study of such memories over the long term. There was rapid forgetting of both flashbulb and event memories within the first year, but the forgetting curves leveled off after that, not significantly changing even after a 10-year delay. Despite the initial rapid forgetting, confidence remained high throughout the 10-year period. Five putative factors affecting flashbulb memory consistency and event memory accuracy were examined: (a) attention to media, (b) the amount of discussion, (c) residency, (d) personal loss and/or inconvenience, and (e) emotional intensity. After 10 years, none of these factors predicted flashbulb memory consistency; media attention and ensuing conversation predicted event memory accuracy. Inconsistent flashbulb memories were more likely to be repeated rather than corrected over the 10-year period; inaccurate event memories, however, were more likely to be corrected. The findings suggest that even traumatic memories and those implicated in a community's collective identity may be inconsistent over time and these inconsistencies can persist without the corrective force of external influences. (c) 2015 APA, all rights reserved).