Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

NASA Astrophysics Data System (ADS)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Foundations of Sequential Learning

DTIC Science & Technology

2018-02-01

is not sufficient to estimate the payoff. The learned must use a strategy that balances exploration (learning new information by pulling arms that...specifications, or other data does not license the holder or any other person or corporation ; or convey any rights or permission to manufacture, use, or sell any...ABSTRACT This report summarizes the research done under FA8750-16-2-0173. This research advanced understanding of bandit algorithms and exploration in

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

PubMed

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

2018-02-01

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

Risk-aware multi-armed bandit problem with application to portfolio selection

PubMed Central

Huo, Xiaoguang

2017-01-01

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of the financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return. PMID:29291122

Risk-aware multi-armed bandit problem with application to portfolio selection.

PubMed

Huo, Xiaoguang; Fu, Feng

2017-11-01

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of the financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.

Knowledge Acquisition from Structural Descriptions.

ERIC Educational Resources Information Center

Hayes-Roth, Frederick; McDermott, John

The learning machine described in this paper acquires concepts representable as conjunctive forms of the predicate calculus and behaviors representable as productions (antecedent-consequent pairs of such conjunctive forms): these concepts and behavior rules are inferred from sequentially presented pairs of examples by an algorithm that is probably…

Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer

NASA Technical Reports Server (NTRS)

Godoy, William F.; Liu, Xu

2011-01-01

General-purpose computing on graphics processing units (GPGPU) is a recent technique that allows the parallel graphics processing unit (GPU) to accelerate calculations performed sequentially by the central processing unit (CPU). To introduce GPGPU to radiative transfer, the Gauss-Seidel solution of the well-known expressions for 1-D and 3-D homogeneous, isotropic media is selected as a test case. Different algorithms are introduced to balance memory and GPU-CPU communication, critical aspects of GPGPU. Results show that speed-ups of one to two orders of magnitude are obtained when compared to sequential solutions. The underlying value of GPGPU is its potential extension in radiative solvers (e.g., Monte Carlo, discrete ordinates) at a minimal learning curve.

Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach.

PubMed

Nemati, Shamim; Ghassemi, Mohammad M; Clifford, Gari D

2016-08-01

Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.

Scalable Kernel Methods and Algorithms for General Sequence Analysis

ERIC Educational Resources Information Center

Kuksa, Pavel

2011-01-01

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

NASA Astrophysics Data System (ADS)

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

2018-05-01

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods-the particle Metropolis-Hastings algorithm-which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods-including particle Metropolis-Hastings-to a large group of users without requiring them to know all the underlying mathematical details.

Unsupervised learning of natural languages

PubMed Central

Solan, Zach; Horn, David; Ruppin, Eytan; Edelman, Shimon

2005-01-01

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics. PMID:16087885

Unsupervised learning of natural languages.

PubMed

Solan, Zach; Horn, David; Ruppin, Eytan; Edelman, Shimon

2005-08-16

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.

Learning Efficient Sparse and Low Rank Models.

PubMed

Sprechmann, P; Bronstein, A M; Sapiro, G

2015-09-01

Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.

Multi-Level Sequential Pattern Mining Based on Prime Encoding

NASA Astrophysics Data System (ADS)

Lianglei, Sun; Yun, Li; Jiang, Yin

Encoding is not only to express the hierarchical relationship, but also to facilitate the identification of the relationship between different levels, which will directly affect the efficiency of the algorithm in the area of mining the multi-level sequential pattern. In this paper, we prove that one step of division operation can decide the parent-child relationship between different levels by using prime encoding and present PMSM algorithm and CROSS-PMSM algorithm which are based on prime encoding for mining multi-level sequential pattern and cross-level sequential pattern respectively. Experimental results show that the algorithm can effectively extract multi-level and cross-level sequential pattern from the sequence database.

PySeqLab: an open source Python package for sequence labeling and segmentation.

PubMed

Allam, Ahmed; Krauthammer, Michael

2017-11-01

Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data. Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks. It implements CRFs models, that is discriminative models from (i) first-order to higher-order linear-chain CRFs, and from (ii) first-order to higher-order semi-Markov CRFs (semi-CRFs). Moreover, it provides multiple learning algorithms for estimating model parameters such as (i) stochastic gradient descent (SGD) and its multiple variations, (ii) structured perceptron with multiple averaging schemes supporting exact and inexact search using 'violation-fixing' framework, (iii) search-based probabilistic online learning algorithm (SAPO) and (iv) an interface for Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited-memory BFGS algorithms. Viterbi and Viterbi A* are used for inference and decoding of sequences. Using PySeqLab, we built models (classifiers) and evaluated their performance in three different domains: (i) biomedical Natural language processing (NLP), (ii) predictive DNA sequence analysis and (iii) Human activity recognition (HAR). State-of-the-art performance comparable to machine-learning based systems was achieved in the three domains without feature engineering or the use of knowledge sources. PySeqLab is available through https://bitbucket.org/A_2/pyseqlab with tutorials and documentation. ahmed.allam@yale.edu or michael.krauthammer@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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

PubMed

Hu, En-Liang; Kwok, James T

2015-09-01

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

Two generalizations of Kohonen clustering

NASA Technical Reports Server (NTRS)

Bezdek, James C.; Pal, Nikhil R.; Tsao, Eric C. K.

1993-01-01

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Formalizing Neurath's ship: Approximate algorithms for online causal learning.

PubMed

Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A

2017-04-01

Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

NASA Astrophysics Data System (ADS)

Xu, Xiwei; Zhang, Changhai

2013-12-01

Now some sequential patterns mining algorithms generate too many candidate sequences, and increase the processing cost of support counting. Therefore, we present an effective and scalable algorithm called SPMBR (Sequential Patterns Mining based on Bitmap Representation) to solve the problem of mining the sequential patterns for large databases. Our method differs from previous related works of mining sequential patterns. The main difference is that the database of sequential patterns is represented by bitmaps, and a simplified bitmap structure is presented firstly. In this paper, First the algorithm generate candidate sequences by SE(Sequence Extension) and IE(Item Extension), and then obtain all frequent sequences by comparing the original bitmap and the extended item bitmap .This method could simplify the problem of mining the sequential patterns and avoid the high processing cost of support counting. Both theories and experiments indicate that the performance of SPMBR is predominant for large transaction databases, the required memory size for storing temporal data is much less during mining process, and all sequential patterns can be mined with feasibility.

Online Graph Completion: Multivariate Signal Recovery in Computer Vision.

PubMed

Kim, Won Hwa; Jalal, Mona; Hwang, Seongjae; Johnson, Sterling C; Singh, Vikas

2017-07-01

The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.

Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

NASA Astrophysics Data System (ADS)

Roche-Lima, Abiel; Thulasiram, Ruppa K.

2012-02-01

Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

Efficient sequential and parallel algorithms for record linkage.

PubMed

Mamun, Abdullah-Al; Mi, Tian; Aseltine, Robert; Rajasekaran, Sanguthevar

2014-01-01

Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Our sequential and parallel algorithms have been tested on a real dataset of 1,083,878 records and synthetic datasets ranging in size from 50,000 to 9,000,000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

Decoding of finger trajectory from ECoG using deep learning.

PubMed

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

DOE PAGES

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; ...

2017-08-08

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

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

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

NASA Astrophysics Data System (ADS)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Mudigonda, Mayur; Prabhat; Spentzouris, Panagiotis; Spiropoulou, Maria; Tsaris, Aristeidis; Vlimant, Jean-Roch; Zheng, Stephan

2017-08-01

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

An algorithm for propagating the square-root covariance matrix in triangular form

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Choe, C. Y.

1976-01-01

A method for propagating the square root of the state error covariance matrix in lower triangular form is described. The algorithm can be combined with any triangular square-root measurement update algorithm to obtain a triangular square-root sequential estimation algorithm. The triangular square-root algorithm compares favorably with the conventional sequential estimation algorithm with regard to computation time.

Actively learning human gaze shifting paths for semantics-aware photo cropping.

PubMed

Zhang, Luming; Gao, Yue; Ji, Rongrong; Xia, Yingjie; Dai, Qionghai; Li, Xuelong

2014-05-01

Photo cropping is a widely used tool in printing industry, photography, and cinematography. Conventional cropping models suffer from the following three challenges. First, the deemphasized role of semantic contents that are many times more important than low-level features in photo aesthetics. Second, the absence of a sequential ordering in the existing models. In contrast, humans look at semantically important regions sequentially when viewing a photo. Third, the difficulty of leveraging inputs from multiple users. Experience from multiple users is particularly critical in cropping as photo assessment is quite a subjective task. To address these challenges, this paper proposes semantics-aware photo cropping, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo. We first project the local features (graphlets in this paper) onto the semantic space, which is constructed based on the category information of the training photos. An efficient learning algorithm is then derived to sequentially select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path, which simulates humans actively perceiving semantics in a photo. Furthermore, we learn a prior distribution of such active graphlet paths from training photos that are marked as aesthetically pleasing by multiple users. The learned priors enforce the corresponding active graphlet path of a test photo to be maximally similar to those from the training photos. Experimental results show that: 1) the active graphlet path accurately predicts human gaze shifting, and thus is more indicative for photo aesthetics than conventional saliency maps and 2) the cropped photos produced by our approach outperform its competitors in both qualitative and quantitative comparisons.

Efficient sequential and parallel algorithms for record linkage

PubMed Central

Mamun, Abdullah-Al; Mi, Tian; Aseltine, Robert; Rajasekaran, Sanguthevar

2014-01-01

Background and objective Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm. PMID:24154837

A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

NASA Astrophysics Data System (ADS)

Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita

2017-02-01

In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

PubMed

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Online feature selection with streaming features.

PubMed

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

PubMed

Xue, Yun; Liao, Zhengling; Li, Meihang; Luo, Jie; Kuang, Qiuhua; Hu, Xiaohui; Li, Tiechen

2015-01-01

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

Research on parallel algorithm for sequential pattern mining

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Qin, Bai; Wang, Yu; Hao, Zhongxiao

2008-03-01

Sequential pattern mining is the mining of frequent sequences related to time or other orders from the sequence database. Its initial motivation is to discover the laws of customer purchasing in a time section by finding the frequent sequences. In recent years, sequential pattern mining has become an important direction of data mining, and its application field has not been confined to the business database and has extended to new data sources such as Web and advanced science fields such as DNA analysis. The data of sequential pattern mining has characteristics as follows: mass data amount and distributed storage. Most existing sequential pattern mining algorithms haven't considered the above-mentioned characteristics synthetically. According to the traits mentioned above and combining the parallel theory, this paper puts forward a new distributed parallel algorithm SPP(Sequential Pattern Parallel). The algorithm abides by the principal of pattern reduction and utilizes the divide-and-conquer strategy for parallelization. The first parallel task is to construct frequent item sets applying frequent concept and search space partition theory and the second task is to structure frequent sequences using the depth-first search method at each processor. The algorithm only needs to access the database twice and doesn't generate the candidated sequences, which abates the access time and improves the mining efficiency. Based on the random data generation procedure and different information structure designed, this paper simulated the SPP algorithm in a concrete parallel environment and implemented the AprioriAll algorithm. The experiments demonstrate that compared with AprioriAll, the SPP algorithm had excellent speedup factor and efficiency.

Distance majorization and its applications.

PubMed

Chi, Eric C; Zhou, Hua; Lange, Kenneth

2014-08-01

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications.

Perceptually Guided Photo Retargeting.

PubMed

Xia, Yingjie; Zhang, Luming; Hong, Richang; Nie, Liqiang; Yan, Yan; Shao, Ling

2016-04-22

We propose perceptually guided photo retargeting, which shrinks a photo by simulating a human's process of sequentially perceiving visually/semantically important regions in a photo. In particular, we first project the local features (graphlets in this paper) onto a semantic space, wherein visual cues such as global spatial layout and rough geometric context are exploited. Thereafter, a sparsity-constrained learning algorithm is derived to select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path which simulates how a human actively perceives semantics in a photo. Furthermore, we learn the prior distribution of such active graphlet paths (AGPs) from training photos that are marked as esthetically pleasing by multiple users. The learned priors enforce the corresponding AGP of a retargeted photo to be maximally similar to those from the training photos. On top of the retargeting model, we further design an online learning scheme to incrementally update the model with new photos that are esthetically pleasing. The online update module makes the algorithm less dependent on the number and contents of the initial training data. Experimental results show that: 1) the proposed AGP is over 90% consistent with human gaze shifting path, as verified by the eye-tracking data, and 2) the retargeting algorithm outperforms its competitors significantly, as AGP is more indicative of photo esthetics than conventional saliency maps.

A generic motif discovery algorithm for sequential data.

PubMed

Jensen, Kyle L; Styczynski, Mark P; Rigoutsos, Isidore; Stephanopoulos, Gregory N

2006-01-01

Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures. Gemoda is freely available at http://web.mit.edu/bamel/gemoda

Considering User's Access Pattern in Multimedia File Systems

NASA Astrophysics Data System (ADS)

Cho, KyoungWoon; Ryu, YeonSeung; Won, Youjip; Koh, Kern

2002-12-01

Legacy buffer cache management schemes for multimedia server are grounded at the assumption that the application sequentially accesses the multimedia file. However, user access pattern may not be sequential in some circumstances, for example, in distance learning application, where the user may exploit the VCR-like function(rewind and play) of the system and accesses the particular segments of video repeatedly in the middle of sequential playback. Such a looping reference can cause a significant performance degradation of interval-based caching algorithms. And thus an appropriate buffer cache management scheme is required in order to deliver desirable performance even under the workload that exhibits looping reference behavior. We propose Adaptive Buffer cache Management(ABM) scheme which intelligently adapts to the file access characteristics. For each opened file, ABM applies either the LRU replacement or the interval-based caching depending on the Looping Reference Indicator, which indicates that how strong temporally localized access pattern is. According to our experiment, ABM exhibits better buffer cache miss ratio than interval-based caching or LRU, especially when the workload exhibits not only sequential but also looping reference property.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

A Node Linkage Approach for Sequential Pattern Mining

PubMed Central

Navarro, Osvaldo; Cumplido, René; Villaseñor-Pineda, Luis; Feregrino-Uribe, Claudia; Carrasco-Ochoa, Jesús Ariel

2014-01-01

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms. PMID:24933123

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

Serin, E.; Codel, G.; Mabhouti, H.

Purpose: In small field geometries, the electronic equilibrium can be lost, making it challenging for the dose-calculation algorithm to accurately predict the dose, especially in the presence of tissue heterogeneities. In this study, dosimetric accuracy of Monte Carlo (MC) advanced dose calculation and sequential algorithms of Multiplan treatment planning system were investigated for small radiation fields incident on homogeneous and heterogeneous geometries. Methods: Small open fields of fixed cones of Cyberknife M6 unit 100 to 500 mm2 were used for this study. The fields were incident on in house phantom containing lung, air, and bone inhomogeneities and also homogeneous phantom.more » Using the same film batch, the net OD to dose calibration curve was obtained using CK with the 60 mm fixed cone by delivering 0- 800 cGy. Films were scanned 48 hours after irradiation using an Epson 1000XL flatbed scanner. The dosimetric accuracy of MC and sequential algorithms in the presence of the inhomogeneities was compared against EBT3 film dosimetry Results: Open field tests in a homogeneous phantom showed good agreement between two algorithms and film measurement For MC algorithm, the minimum gamma analysis passing rates between measured and calculated dose distributions were 99.7% and 98.3% for homogeneous and inhomogeneous fields in the case of lung and bone respectively. For sequential algorithm, the minimum gamma analysis passing rates were 98.9% and 92.5% for for homogeneous and inhomogeneous fields respectively for used all cone sizes. In the case of the air heterogeneity, the differences were larger for both calculation algorithms. Overall, when compared to measurement, the MC had better agreement than sequential algorithm. Conclusion: The Monte Carlo calculation algorithm in the Multiplan treatment planning system is an improvement over the existing sequential algorithm. Dose discrepancies were observed for in the presence of air inhomogeneities.« less

Distance majorization and its applications

PubMed Central

Chi, Eric C.; Zhou, Hua; Lange, Kenneth

2014-01-01

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton’s method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications. PMID:25392563

Parallelization of sequential Gaussian, indicator and direct simulation algorithms

NASA Astrophysics Data System (ADS)

Nunes, Ruben; Almeida, José A.

2010-08-01

Improving the performance and robustness of algorithms on new high-performance parallel computing architectures is a key issue in efficiently performing 2D and 3D studies with large amount of data. In geostatistics, sequential simulation algorithms are good candidates for parallelization. When compared with other computational applications in geosciences (such as fluid flow simulators), sequential simulation software is not extremely computationally intensive, but parallelization can make it more efficient and creates alternatives for its integration in inverse modelling approaches. This paper describes the implementation and benchmarking of a parallel version of the three classic sequential simulation algorithms: direct sequential simulation (DSS), sequential indicator simulation (SIS) and sequential Gaussian simulation (SGS). For this purpose, the source used was GSLIB, but the entire code was extensively modified to take into account the parallelization approach and was also rewritten in the C programming language. The paper also explains in detail the parallelization strategy and the main modifications. Regarding the integration of secondary information, the DSS algorithm is able to perform simple kriging with local means, kriging with an external drift and collocated cokriging with both local and global correlations. SIS includes a local correction of probabilities. Finally, a brief comparison is presented of simulation results using one, two and four processors. All performance tests were carried out on 2D soil data samples. The source code is completely open source and easy to read. It should be noted that the code is only fully compatible with Microsoft Visual C and should be adapted for other systems/compilers.

Sequential Organization and Room Reverberation for Speech Segregation

DTIC Science & Technology

2012-02-28

we have proposed two algorithms for sequential organization, an unsupervised clustering algorithm applicable to monaural recordings and a binaural ...algorithm that integrates monaural and binaural analyses. In addition, we have conducted speech intelligibility tests that Firmly establish the...comprehensive version is currently under review for journal publication. A binaural approach in room reverberation Most existing approaches to binaural or

Hyperopt: a Python library for model selection and hyperparameter optimization

NASA Astrophysics Data System (ADS)

Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

2015-01-01

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

Involvement of Working Memory in College Students' Sequential Pattern Learning and Performance

ERIC Educational Resources Information Center

Kundey, Shannon M. A.; De Los Reyes, Andres; Rowan, James D.; Lee, Bern; Delise, Justin; Molina, Sabrina; Cogdill, Lindsay

2013-01-01

When learning highly organized sequential patterns of information, humans and nonhuman animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential…

ProperCAD: A portable object-oriented parallel environment for VLSI CAD

NASA Technical Reports Server (NTRS)

Ramkumar, Balkrishna; Banerjee, Prithviraj

1993-01-01

Most parallel algorithms for VLSI CAD proposed to date have one important drawback: they work efficiently only on machines that they were designed for. As a result, algorithms designed to date are dependent on the architecture for which they are developed and do not port easily to other parallel architectures. A new project under way to address this problem is described. A Portable object-oriented parallel environment for CAD algorithms (ProperCAD) is being developed. The objectives of this research are (1) to develop new parallel algorithms that run in a portable object-oriented environment (CAD algorithms using a general purpose platform for portable parallel programming called CARM is being developed and a C++ environment that is truly object-oriented and specialized for CAD applications is also being developed); and (2) to design the parallel algorithms around a good sequential algorithm with a well-defined parallel-sequential interface (permitting the parallel algorithm to benefit from future developments in sequential algorithms). One CAD application that has been implemented as part of the ProperCAD project, flat VLSI circuit extraction, is described. The algorithm, its implementation, and its performance on a range of parallel machines are discussed in detail. It currently runs on an Encore Multimax, a Sequent Symmetry, Intel iPSC/2 and i860 hypercubes, a NCUBE 2 hypercube, and a network of Sun Sparc workstations. Performance data for other applications that were developed are provided: namely test pattern generation for sequential circuits, parallel logic synthesis, and standard cell placement.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

ERIC Educational Resources Information Center

Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L.

2016-01-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.

Building Reliable Metaclassifiers for Text Learning

DTIC Science & Technology

2006-05-01

outputs are often poor [Ben00, DP96] but can be improved [Ben00, ZE01, ZE02]. SVM For linear SVMs, we use the Smox toolkit which is based on Platt’s...and implementations are the same as discussed in Section 6.3. The exception is that for an implementation of linear SVMs, we used the Smox toolkit which...is based on Platt’s Sequential Minimal Optimization algorithm [Pla98]. Since Smox is the best base classifier in the experiments below, it is the

Sequential Analysis of Automatic Target Detection with Classification Algorithms and Optimality of Dynamic Decision Making Under Uncertainty

DTIC Science & Technology

2013-02-01

Pavlovian   drug  cues  to  produce   excessive  “wanting”  to...motivation:  Incentive   salience  boosts  of   drug  or  appetite  states.  Behavioral  Brain  Science  31:440-­‐441...learning   into  motivation.  In  Gutkin,  B.  and  Ahmed,  S.H.  (Eds.)  Computational  Neuroscience  of   Drug

An exact computational method for performance analysis of sequential test algorithms for detecting network intrusions

NASA Astrophysics Data System (ADS)

Chen, Xinjia; Lacy, Fred; Carriere, Patrick

2015-05-01

Sequential test algorithms are playing increasingly important roles for quick detecting network intrusions such as portscanners. In view of the fact that such algorithms are usually analyzed based on intuitive approximation or asymptotic analysis, we develop an exact computational method for the performance analysis of such algorithms. Our method can be used to calculate the probability of false alarm and average detection time up to arbitrarily pre-specified accuracy.

Max-margin weight learning for medical knowledge network.

PubMed

Jiang, Jingchi; Xie, Jing; Zhao, Chao; Su, Jia; Guan, Yi; Yu, Qiubin

2018-03-01

The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The purpose of this study is to investigate a discriminative weight-learning method based on a medical knowledge network (MKN). We propose a training model called the maximum margin medical knowledge network (M 3 KN), which is strictly derived for calculating the weight of medical knowledge. Using the definition of a reasonable margin, the weight learning can be transformed into a margin optimization problem. To solve the optimization problem, we adopt a sequential minimal optimization (SMO) algorithm and the clique property of a Markov network. Ultimately, M 3 KN not only incorporates the inference ability of PGMs but also deals with high-dimensional logic knowledge. The experimental results indicate that M 3 KN obtains a higher F-measure score than the maximum likelihood learning algorithm of MKN for both Chinese Electronic Medical Records (CEMRs) and Blood Examination Records (BERs). Furthermore, the proposed approach is obviously superior to some classical machine learning algorithms for medical diagnosis. To adequately manifest the importance of domain knowledge, we numerically verify that the diagnostic accuracy of M 3 KN is gradually improved as the number of learned CEMRs increase, which contain important medical knowledge. Our experimental results show that the proposed method performs reliably for learning the weights of medical knowledge. M 3 KN outperforms other existing methods by achieving an F-measure of 0.731 for CEMRs and 0.4538 for BERs. This further illustrates that M 3 KN can facilitate the investigations of intelligent healthcare. Copyright © 2018 Elsevier B.V. All rights reserved.

Similar Neural Correlates for Language and Sequential Learning: Evidence from Event-Related Brain Potentials

PubMed Central

Christiansen, Morten H.; Conway, Christopher M.; Onnis, Luca

2011-01-01

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (a) a sequential learning task involving complex structured sequences, and (b) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language, and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally. PMID:23678205

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images.

PubMed

Yao, Libo; Liu, Yong; He, You

2018-06-22

The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.

Algorithms and Application of Sparse Matrix Assembly and Equation Solvers for Aeroacoustics

NASA Technical Reports Server (NTRS)

Watson, W. R.; Nguyen, D. T.; Reddy, C. J.; Vatsa, V. N.; Tang, W. H.

2001-01-01

An algorithm for symmetric sparse equation solutions on an unstructured grid is described. Efficient, sequential sparse algorithms for degree-of-freedom reordering, supernodes, symbolic/numerical factorization, and forward backward solution phases are reviewed. Three sparse algorithms for the generation and assembly of symmetric systems of matrix equations are presented. The accuracy and numerical performance of the sequential version of the sparse algorithms are evaluated over the frequency range of interest in a three-dimensional aeroacoustics application. Results show that the solver solutions are accurate using a discretization of 12 points per wavelength. Results also show that the first assembly algorithm is impractical for high-frequency noise calculations. The second and third assembly algorithms have nearly equal performance at low values of source frequencies, but at higher values of source frequencies the third algorithm saves CPU time and RAM. The CPU time and the RAM required by the second and third assembly algorithms are two orders of magnitude smaller than that required by the sparse equation solver. A sequential version of these sparse algorithms can, therefore, be conveniently incorporated into a substructuring for domain decomposition formulation to achieve parallel computation, where different substructures are handles by different parallel processors.

Sequential Syndrome Decoding of Convolutional Codes

NASA Technical Reports Server (NTRS)

Reed, I. S.; Truong, T. K.

1984-01-01

The algebraic structure of convolutional codes are reviewed and sequential syndrome decoding is applied to those codes. These concepts are then used to realize by example actual sequential decoding, using the stack algorithm. The Fano metric for use in sequential decoding is modified so that it can be utilized to sequentially find the minimum weight error sequence.

Decision Making and Learning while Taking Sequential Risks

ERIC Educational Resources Information Center

Pleskac, Timothy J.

2008-01-01

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies…

Learning Sequential Composition Control.

PubMed

Najafi, Esmaeil; Babuska, Robert; Lopes, Gabriel A D

2016-11-01

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. As these controllers are designed offline, sequential composition cannot address unmodeled situations that might occur during runtime. This paper proposes a learning approach to augment the standard sequential composition framework by using online learning to handle unforeseen situations. New controllers are acquired via learning and added to the existing supervisory control structure. In the proposed setting, learning experiments are restricted to take place within the domain of attraction (DOA) of the existing controllers. This guarantees that the learning process is safe (i.e., the closed loop system is always stable). In addition, the DOA of the new learned controller is approximated after each learning trial. This keeps the learning process short as learning is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two nonlinear systems: 1) a nonlinear mass-damper system and 2) an inverted pendulum. The results show that in both cases a new controller can be rapidly learned and added to the supervisory control structure.

Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2015-01-01

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory. PMID:25910189

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Efficient sequential and parallel algorithms for finding edit distance based motifs.

PubMed

Pal, Soumitra; Xiao, Peng; Rajasekaran, Sanguthevar

2016-08-18

Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in this paper are also applicable to other motif search problems such as Planted Motif Search (PMS) and Simple Motif Search (SMS).

Meta-heuristic algorithm to solve two-sided assembly line balancing problems

NASA Astrophysics Data System (ADS)

Wirawan, A. D.; Maruf, A.

2016-02-01

Two-sided assembly line is a set of sequential workstations where task operations can be performed at two sides of the line. This type of line is commonly used for the assembly of large-sized products: cars, buses, and trucks. This paper propose a Decoding Algorithm with Teaching-Learning Based Optimization (TLBO), a recently developed nature-inspired search method to solve the two-sided assembly line balancing problem (TALBP). The algorithm aims to minimize the number of mated-workstations for the given cycle time without violating the synchronization constraints. The correlation between the input parameters and the emergence point of objective function value is tested using scenarios generated by design of experiments. A two-sided assembly line operated in an Indonesia's multinational manufacturing company is considered as the object of this paper. The result of the proposed algorithm shows reduction of workstations and indicates that there is negative correlation between the emergence point of objective function value and the size of population used.

Self-supervised online metric learning with low rank constraint for scene categorization.

PubMed

Cong, Yang; Liu, Ji; Yuan, Junsong; Luo, Jiebo

2013-08-01

Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.

Gene expression profiling gut microbiota in different races of humans

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong

2016-03-01

The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, such as eating habits, living environment, and antibiotic usage. Thus, various races are characterized by different gut microbiome characteristics. In this present study, we studied the gut microbiomes of three different races, including individuals of Asian, European and American races. The gut microbiome and the expression levels of gut microbiome genes were analyzed in these individuals. Advanced feature selection methods (minimum redundancy maximum relevance and incremental feature selection) and four machine-learning algorithms (random forest, nearest neighbor algorithm, sequential minimal optimization, Dagging) were employed to capture key differentially expressed genes. As a result, sequential minimal optimization was found to yield the best performance using the 454 genes, which could effectively distinguish the gut microbiomes of different races. Our analyses of extracted genes support the widely accepted hypotheses that eating habits, living environments and metabolic levels in different races can influence the characteristics of the gut microbiome.

Gene expression profiling gut microbiota in different races of humans

PubMed Central

Chen, Lei; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong

2016-01-01

The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, such as eating habits, living environment, and antibiotic usage. Thus, various races are characterized by different gut microbiome characteristics. In this present study, we studied the gut microbiomes of three different races, including individuals of Asian, European and American races. The gut microbiome and the expression levels of gut microbiome genes were analyzed in these individuals. Advanced feature selection methods (minimum redundancy maximum relevance and incremental feature selection) and four machine-learning algorithms (random forest, nearest neighbor algorithm, sequential minimal optimization, Dagging) were employed to capture key differentially expressed genes. As a result, sequential minimal optimization was found to yield the best performance using the 454 genes, which could effectively distinguish the gut microbiomes of different races. Our analyses of extracted genes support the widely accepted hypotheses that eating habits, living environments and metabolic levels in different races can influence the characteristics of the gut microbiome. PMID:26975620

A smart-pixel holographic competitive learning network

NASA Astrophysics Data System (ADS)

Slagle, Timothy Michael

Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization-multiplexed PCM readout, and gains of up to 60 were observed when a time-sequential read-out technique was used. Finally, the optical competitive learning laboratory system is described, including some necessary modifications to the previous architectures, and the data acquisition and control system developed for the system. Experimental results showing phase conjugation of the WTA outputs, holographic interconnect storage, associative storage between input images and WTA neuron outputs, and WTA array switching are presented, demonstrating the functions necessary for the operation of the optical learning system.

Rapid code acquisition algorithms employing PN matched filters

NASA Technical Reports Server (NTRS)

Su, Yu T.

1988-01-01

The performance of four algorithms using pseudonoise matched filters (PNMFs), for direct-sequence spread-spectrum systems, is analyzed. They are: parallel search with fix dwell detector (PL-FDD), parallel search with sequential detector (PL-SD), parallel-serial search with fix dwell detector (PS-FDD), and parallel-serial search with sequential detector (PS-SD). The operation characteristic for each detector and the mean acquisition time for each algorithm are derived. All the algorithms are studied in conjunction with the noncoherent integration technique, which enables the system to operate in the presence of data modulation. Several previous proposals using PNMF are seen as special cases of the present algorithms.

Delay test generation for synchronous sequential circuits

NASA Astrophysics Data System (ADS)

Devadas, Srinivas

1989-05-01

We address the problem of generating tests for delay faults in non-scan synchronous sequential circuits. Delay test generation for sequential circuits is a considerably more difficult problem than delay testing of combinational circuits and has received much less attention. In this paper, we present a method for generating test sequences to detect delay faults in sequential circuits using the stuck-at fault sequential test generator STALLION. The method is complete in that it will generate a delay test sequence for a targeted fault given sufficient CPU time, if such a sequence exists. We term faults for which no delay test sequence exists, under out test methodology, sequentially delay redundant. We describe means of eliminating sequential delay redundancies in logic circuits. We present a partial-scan methodology for enhancing the testability of difficult-to-test of untestable sequential circuits, wherein a small number of flip-flops are selected and made controllable/observable. The selection process guarantees the elimination of all sequential delay redundancies. We show that an intimate relationship exists between state assignment and delay testability of a sequential machine. We describe a state assignment algorithm for the synthesis of sequential machines with maximal delay fault testability. Preliminary experimental results using the test generation, partial-scan and synthesis algorithm are presented.

Efficient Controls for Finitely Convergent Sequential Algorithms

PubMed Central

Chen, Wei; Herman, Gabor T.

2010-01-01

Finding a feasible point that satisfies a set of constraints is a common task in scientific computing: examples are the linear feasibility problem and the convex feasibility problem. Finitely convergent sequential algorithms can be used for solving such problems; an example of such an algorithm is ART3, which is defined in such a way that its control is cyclic in the sense that during its execution it repeatedly cycles through the given constraints. Previously we found a variant of ART3 whose control is no longer cyclic, but which is still finitely convergent and in practice it usually converges faster than ART3 does. In this paper we propose a general methodology for automatic transformation of finitely convergent sequential algorithms in such a way that (i) finite convergence is retained and (ii) the speed of convergence is improved. The first of these two properties is proven by mathematical theorems, the second is illustrated by applying the algorithms to a practical problem. PMID:20953327

Comparison between variable and fixed dwell-time PN acquisition algorithms. [for synchronization in pseudonoise spread spectrum systems

NASA Technical Reports Server (NTRS)

Braun, W. R.

1981-01-01

Pseudo noise (PN) spread spectrum systems require a very accurate alignment between the PN code epochs at the transmitter and receiver. This synchronism is typically established through a two-step algorithm, including a coarse synchronization procedure and a fine synchronization procedure. A standard approach for the coarse synchronization is a sequential search over all code phases. The measurement of the power in the filtered signal is used to either accept or reject the code phase under test as the phase of the received PN code. This acquisition strategy, called a single dwell-time system, has been analyzed by Holmes and Chen (1977). A synopsis of the field of sequential analysis as it applies to the PN acquisition problem is provided. From this, the implementation of the variable dwell time algorithm as a sequential probability ratio test is developed. The performance of this algorithm is compared to the optimum detection algorithm and to the fixed dwell-time system.

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.

PubMed

Lee, Junghye; Sun, Jimeng; Wang, Fei; Wang, Shuang; Jun, Chi-Hyuck; Jiang, Xiaoqian

2018-04-13

There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment. The aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patients from one hospital to another. We proposed a federated patient hashing framework and developed a novel algorithm to learn context-specific hash codes to represent patients across institutions. The similarities between patients can be efficiently computed using the resulting hash codes of corresponding patients. To avoid security attack from reverse engineering on the model, we applied homomorphic encryption to patient similarity search in a federated setting. We used sequential medical events extracted from the Multiparameter Intelligent Monitoring in Intensive Care-III database to evaluate the proposed algorithm in predicting the incidence of five diseases independently. Our algorithm achieved averaged area under the curves of 0.9154 and 0.8012 with balanced and imbalanced data, respectively, in κ-nearest neighbor with κ=3. We also confirmed privacy preservation in similarity search by using homomorphic encryption. The proposed algorithm can help search similar patients across institutions effectively to support federated data analysis in a privacy-preserving manner. ©Junghye Lee, Jimeng Sun, Fei Wang, Shuang Wang, Chi-Hyuck Jun, Xiaoqian Jiang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.04.2018.

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

PubMed Central

Sun, Jimeng; Wang, Fei; Wang, Shuang; Jun, Chi-Hyuck; Jiang, Xiaoqian

2018-01-01

Background There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment. Objective The aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patients from one hospital to another. Methods We proposed a federated patient hashing framework and developed a novel algorithm to learn context-specific hash codes to represent patients across institutions. The similarities between patients can be efficiently computed using the resulting hash codes of corresponding patients. To avoid security attack from reverse engineering on the model, we applied homomorphic encryption to patient similarity search in a federated setting. Results We used sequential medical events extracted from the Multiparameter Intelligent Monitoring in Intensive Care-III database to evaluate the proposed algorithm in predicting the incidence of five diseases independently. Our algorithm achieved averaged area under the curves of 0.9154 and 0.8012 with balanced and imbalanced data, respectively, in κ-nearest neighbor with κ=3. We also confirmed privacy preservation in similarity search by using homomorphic encryption. Conclusions The proposed algorithm can help search similar patients across institutions effectively to support federated data analysis in a privacy-preserving manner. PMID:29653917

Sequential visibility-graph motifs

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Lacasa, Lucas

2016-04-01

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

DOE PAGES

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; ...

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

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

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

PubMed

Yamazaki, Keisuke

2012-07-01

Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Parallel algorithm for computation of second-order sequential best rotations

NASA Astrophysics Data System (ADS)

Redif, Soydan; Kasap, Server

2013-12-01

Algorithms for computing an approximate polynomial matrix eigenvalue decomposition of para-Hermitian systems have emerged as a powerful, generic signal processing tool. A technique that has shown much success in this regard is the sequential best rotation (SBR2) algorithm. Proposed is a scheme for parallelising SBR2 with a view to exploiting the modern architectural features and inherent parallelism of field-programmable gate array (FPGA) technology. Experiments show that the proposed scheme can achieve low execution times while requiring minimal FPGA resources.

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

An algorithmic approach to the brain biopsy--part I.

PubMed

Kleinschmidt-DeMasters, B K; Prayson, Richard A

2006-11-01

The formulation of appropriate differential diagnoses for a slide is essential to the practice of surgical pathology but can be particularly challenging for residents and fellows. Algorithmic flow charts can help the less experienced pathologist to systematically consider all possible choices and eliminate incorrect diagnoses. They can assist pathologists-in-training in developing orderly, sequential, and logical thinking skills when confronting difficult cases. To present an algorithmic flow chart as an approach to formulating differential diagnoses for lesions seen in surgical neuropathology. An algorithmic flow chart to be used in teaching residents. Algorithms are not intended to be final diagnostic answers on any given case. Algorithms do not substitute for training received from experienced mentors nor do they substitute for comprehensive reading by trainees of reference textbooks. Algorithmic flow diagrams can, however, direct the viewer to the correct spot in reference texts for further in-depth reading once they hone down their diagnostic choices to a smaller number of entities. The best feature of algorithms is that they remind the user to consider all possibilities on each case, even if they can be quickly eliminated from further consideration. In Part I, we assist the resident in learning how to handle brain biopsies in general and how to distinguish nonneoplastic lesions that mimic tumors from true neoplasms.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

PubMed

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

2014-03-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

Recent advances in lossless coding techniques

NASA Astrophysics Data System (ADS)

Yovanof, Gregory S.

Current lossless techniques are reviewed with reference to both sequential data files and still images. Two major groups of sequential algorithms, dictionary and statistical techniques, are discussed. In particular, attention is given to Lempel-Ziv coding, Huffman coding, and arithmewtic coding. The subject of lossless compression of imagery is briefly discussed. Finally, examples of practical implementations of lossless algorithms and some simulation results are given.

Exploiting Sequential Patterns Found in Users' Solutions and Virtual Tutor Behavior to Improve Assistance in ITS

ERIC Educational Resources Information Center

Fournier-Viger, Philippe; Faghihi, Usef; Nkambou, Roger; Nguifo, Engelbert Mephu

2010-01-01

We propose to mine temporal patterns in Intelligent Tutoring Systems (ITSs) to uncover useful knowledge that can enhance their ability to provide assistance. To discover patterns, we suggest using a custom, sequential pattern-mining algorithm. Two ways of applying the algorithm to enhance an ITS's capabilities are addressed. The first is to…

Lifelong Transfer Learning for Heterogeneous Teams of Agents in Sequential Decision Processes

DTIC Science & Technology

2016-06-01

making (SDM) tasks in dynamic environments with simulated and physical robots . 15. SUBJECT TERMS Sequential decision making, lifelong learning, transfer...sequential decision-making (SDM) tasks in dynamic environments with both simple benchmark tasks and more complex aerial and ground robot tasks. Our work...and ground robots in the presence of disturbances: We applied our methods to the problem of learning controllers for robots with novel disturbances in

Precise algorithm to generate random sequential adsorption of hard polygons at saturation

NASA Astrophysics Data System (ADS)

Zhang, G.

2018-04-01

Random sequential adsorption (RSA) is a time-dependent packing process, in which particles of certain shapes are randomly and sequentially placed into an empty space without overlap. In the infinite-time limit, the density approaches a "saturation" limit. Although this limit has attracted particular research interest, the majority of past studies could only probe this limit by extrapolation. We have previously found an algorithm to reach this limit using finite computational time for spherical particles and could thus determine the saturation density of spheres with high accuracy. In this paper, we generalize this algorithm to generate saturated RSA packings of two-dimensional polygons. We also calculate the saturation density for regular polygons of three to ten sides and obtain results that are consistent with previous, extrapolation-based studies.

Precise algorithm to generate random sequential adsorption of hard polygons at saturation.

PubMed

Zhang, G

2018-04-01

Random sequential adsorption (RSA) is a time-dependent packing process, in which particles of certain shapes are randomly and sequentially placed into an empty space without overlap. In the infinite-time limit, the density approaches a "saturation" limit. Although this limit has attracted particular research interest, the majority of past studies could only probe this limit by extrapolation. We have previously found an algorithm to reach this limit using finite computational time for spherical particles and could thus determine the saturation density of spheres with high accuracy. In this paper, we generalize this algorithm to generate saturated RSA packings of two-dimensional polygons. We also calculate the saturation density for regular polygons of three to ten sides and obtain results that are consistent with previous, extrapolation-based studies.

Multi-Attribute Sequential Search

ERIC Educational Resources Information Center

Bearden, J. Neil; Connolly, Terry

2007-01-01

This article describes empirical and theoretical results from two multi-attribute sequential search tasks. In both tasks, the DM sequentially encounters options described by two attributes and must pay to learn the values of the attributes. In the "continuous" version of the task the DM learns the precise numerical value of an attribute when she…

Risk-sensitive reinforcement learning.

PubMed

Shen, Yun; Tobia, Michael J; Sommer, Tobias; Obermayer, Klaus

2014-07-01

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman & Tversky, 1979 ), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.

Plane-Based Sampling for Ray Casting Algorithm in Sequential Medical Images

PubMed Central

Lin, Lili; Chen, Shengyong; Shao, Yan; Gu, Zichun

2013-01-01

This paper proposes a plane-based sampling method to improve the traditional Ray Casting Algorithm (RCA) for the fast reconstruction of a three-dimensional biomedical model from sequential images. In the novel method, the optical properties of all sampling points depend on the intersection points when a ray travels through an equidistant parallel plan cluster of the volume dataset. The results show that the method improves the rendering speed at over three times compared with the conventional algorithm and the image quality is well guaranteed. PMID:23424608

Dynamic graciloplasty for urinary incontinence: the potential for sequential closed-loop stimulation.

PubMed

Zonnevijlle, Erik D H; Perez-Abadia, Gustavo; Stremel, Richard W; Maldonado, Claudio J; Kon, Moshe; Barker, John H

2003-11-01

Muscle tissue transplantation applied to regain or dynamically assist contractile functions is known as 'dynamic myoplasty'. Success rates of clinical applications are unpredictable, because of lack of endurance, ischemic lesions, abundant scar formation and inadequate performance of tasks due to lack of refined control. Electrical stimulation is used to control dynamic myoplasties and should be improved to reduce some of these drawbacks. Sequential segmental neuromuscular stimulation improves the endurance and closed-loop control offers refinement in rate of contraction of the muscle, while function-controlling stimulator algorithms present the possibility of performing more complex tasks. An acute feasibility study was performed in anaesthetised dogs combining these techniques. Electrically stimulated gracilis-based neo-sphincters were compared to native sphincters with regard to their ability to maintain continence. Measurements were made during fast bladder pressure changes, static high bladder pressure and slow filling of the bladder, mimicking among others posture changes, lifting heavy objects and diuresis. In general, neo-sphincter and native sphincter performance showed no significant difference during these measurements. However, during high bladder pressures reaching 40 cm H(2)O the neo-sphincters maintained positive pressure gradients, whereas most native sphincters relaxed. During slow filling of the bladder the neo-sphincters maintained a controlled positive pressure gradient for a prolonged time without any form of training. Furthermore, the accuracy of these maintained pressure gradients proved to be within the limits set up by the native sphincters. Refinements using more complicated self-learning function-controlling algorithms proved to be effective also and are briefly discussed. In conclusion, a combination of sequential stimulation, closed-loop control and function-controlling algorithms proved feasible in this dynamic graciloplasty-model. Neo-sphincters were created, which would probably provide an acceptable performance, when the stimulation system could be implanted and further tested. Sizing this technique down to implantable proportions seems to be justified and will enable exploration of the possible benefits.

Acquisition of Inductive Biconditional Reasoning Skills: Training of Simultaneous and Sequential Processing.

ERIC Educational Resources Information Center

Lee, Seong-Soo

1982-01-01

Tenth-grade students (n=144) received training on one of three processing methods: coding-mapping (simultaneous), coding only, or decision tree (sequential). The induced simultaneous processing strategy worked optimally under rule learning, while the sequential strategy was difficult to induce and/or not optimal for rule-learning operations.…

Adaptive Sequential Monte Carlo for Multiple Changepoint Analysis

DOE PAGES

Heard, Nicholas A.; Turcotte, Melissa J. M.

2016-05-21

Process monitoring and control requires detection of structural changes in a data stream in real time. This paper introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The method is intuitively simple: new changepoints for the latest window of data are proposed by conditioning only on data observed since the most recent estimated changepoint, as these observations carry most of the information about the current state of the process. The proposed method shows improved performance over the current state of the art. Another advantage of the proposed algorithm is that it can be mademore » adaptive, varying the number of particles according to the apparent local complexity of the target changepoint probability distribution. This saves valuable computing time when changes in the changepoint distribution are negligible, and enables re-balancing of the importance weights of existing particles when a significant change in the target distribution is encountered. The plain and adaptive versions of the method are illustrated using the canonical continuous time changepoint problem of inferring the intensity of an inhomogeneous Poisson process, although the method is generally applicable to any changepoint problem. Performance is demonstrated using both conjugate and non-conjugate Bayesian models for the intensity. Lastly, appendices to the article are available online, illustrating the method on other models and applications.« less

Homeostatic Agent for General Environment

NASA Astrophysics Data System (ADS)

Yoshida, Naoto

2018-03-01

One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby's homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn't be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.

Precise algorithm to generate random sequential adsorption of hard polygons at saturation

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

Zhang, G.

Random sequential adsorption (RSA) is a time-dependent packing process, in which particles of certain shapes are randomly and sequentially placed into an empty space without overlap. In the infinite-time limit, the density approaches a "saturation'' limit. Although this limit has attracted particular research interest, the majority of past studies could only probe this limit by extrapolation. We have previously found an algorithm to reach this limit using finite computational time for spherical particles, and could thus determine the saturation density of spheres with high accuracy. Here in this paper, we generalize this algorithm to generate saturated RSA packings of two-dimensionalmore » polygons. We also calculate the saturation density for regular polygons of three to ten sides, and obtain results that are consistent with previous, extrapolation-based studies.« less

Precise algorithm to generate random sequential adsorption of hard polygons at saturation

DOE PAGES

Zhang, G.

2018-04-30

Random sequential adsorption (RSA) is a time-dependent packing process, in which particles of certain shapes are randomly and sequentially placed into an empty space without overlap. In the infinite-time limit, the density approaches a "saturation'' limit. Although this limit has attracted particular research interest, the majority of past studies could only probe this limit by extrapolation. We have previously found an algorithm to reach this limit using finite computational time for spherical particles, and could thus determine the saturation density of spheres with high accuracy. Here in this paper, we generalize this algorithm to generate saturated RSA packings of two-dimensionalmore » polygons. We also calculate the saturation density for regular polygons of three to ten sides, and obtain results that are consistent with previous, extrapolation-based studies.« less

Random sequential adsorption of cubes

NASA Astrophysics Data System (ADS)

Cieśla, Michał; Kubala, Piotr

2018-01-01

Random packings built of cubes are studied numerically using a random sequential adsorption algorithm. To compare the obtained results with previous reports, three different models of cube orientation sampling were used. Also, three different cube-cube intersection algorithms were tested to find the most efficient one. The study focuses on the mean saturated packing fraction as well as kinetics of packing growth. Microstructural properties of packings were analyzed using density autocorrelation function.

Least-squares sequential parameter and state estimation for large space structures

NASA Technical Reports Server (NTRS)

Thau, F. E.; Eliazov, T.; Montgomery, R. C.

1982-01-01

This paper presents the formulation of simultaneous state and parameter estimation problems for flexible structures in terms of least-squares minimization problems. The approach combines an on-line order determination algorithm, with least-squares algorithms for finding estimates of modal approximation functions, modal amplitudes, and modal parameters. The approach combines previous results on separable nonlinear least squares estimation with a regression analysis formulation of the state estimation problem. The technique makes use of sequential Householder transformations. This allows for sequential accumulation of matrices required during the identification process. The technique is used to identify the modal prameters of a flexible beam.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Habituation based synaptic plasticity and organismic learning in a quantum perovskite.

PubMed

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; Li, Jiarui; Kang, Mingu; Mazzoli, Claudio; Zhou, Hua; Barbour, Andi; Wilkins, Stuart; Narayanan, Badri; Cherukara, Mathew; Zhang, Zhen; Sankaranarayanan, Subramanian K R S; Comin, Riccardo; Rabe, Karin M; Roy, Kaushik; Ramanathan, Shriram

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO 3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen-induced electron localization.

Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

PubMed

Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

2016-09-15

Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.

How Cognitive Styles Affect the Learning Behaviors of Online Problem-Solving Based Discussion Activity: A Lag Sequential Analysis

ERIC Educational Resources Information Center

Wu, Sheng-Yi; Hou, Huei-Tse

2015-01-01

Cognitive styles play an important role in influencing the learning process, but to date no relevant study has been conducted using lag sequential analysis to assess knowledge construction learning patterns based on different cognitive styles in computer-supported collaborative learning activities in online collaborative discussions. This study…

Systolic array processing of the sequential decoding algorithm

NASA Technical Reports Server (NTRS)

Chang, C. Y.; Yao, K.

1989-01-01

A systolic array processing technique is applied to implementing the stack algorithm form of the sequential decoding algorithm. It is shown that sorting, a key function in the stack algorithm, can be efficiently realized by a special type of systolic arrays known as systolic priority queues. Compared to the stack-bucket algorithm, this approach is shown to have the advantages that the decoding always moves along the optimal path, that it has a fast and constant decoding speed and that its simple and regular hardware architecture is suitable for VLSI implementation. Three types of systolic priority queues are discussed: random access scheme, shift register scheme and ripple register scheme. The property of the entries stored in the systolic priority queue is also investigated. The results are applicable to many other basic sorting type problems.

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

Sequential and parallel image restoration: neural network implementations.

PubMed

Figueiredo, M T; Leitao, J N

1994-01-01

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

Multiple Ordinal Regression by Maximizing the Sum of Margins

PubMed Central

Hamsici, Onur C.; Martinez, Aleix M.

2016-01-01

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery

PubMed Central

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S.; Pusey, Marc L.; Aygün, Ramazan S.

2015-01-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset. PMID:25914518

First year medical students' learning style preferences and their correlation with performance in different subjects within the medical course.

PubMed

Hernández-Torrano, Daniel; Ali, Syed; Chan, Chee-Kai

2017-08-08

Students commencing their medical training arrive with different educational backgrounds and a diverse range of learning experiences. Consequently, students would have developed preferred approaches to acquiring and processing information or learning style preferences. Understanding first-year students' learning style preferences is important to success in learning. However, little is understood about how learning styles impact learning and performance across different subjects within the medical curriculum. Greater understanding of the relationship between students' learning style preferences and academic performance in specific medical subjects would be valuable. This cross-sectional study examined the learning style preferences of first-year medical students and how they differ across gender. This research also analyzed the effect of learning styles on academic performance across different subjects within a medical education program in a Central Asian university. A total of 52 students (57.7% females) from two batches of first-year medical school completed the Index of Learning Styles Questionnaire, which measures four dimensions of learning styles: sensing-intuitive; visual-verbal; active-reflective; sequential-global. First-year medical students reported preferences for visual (80.8%) and sequential (60.5%) learning styles, suggesting that these students preferred to learn through demonstrations and diagrams and in a linear and sequential way. Our results indicate that male medical students have higher preference for visual learning style over verbal, while females seemed to have a higher preference for sequential learning style over global. Significant associations were found between sensing-intuitive learning styles and performance in Genetics [β = -0.46, B = -0.44, p < 0.01] and Anatomy [β = -0.41, B = -0.61, p < 0.05] and between sequential-global styles and performance in Genetics [β = 0.36, B = 0.43, p < 0.05]. More specifically, sensing learners were more likely to perform better than intuitive learners in the two subjects and global learners were more likely to perform better than sequential learners in Genetics. This knowledge will be helpful to individual students to improve their performance in these subjects by adopting new sensing learning techniques. Instructors can also benefit by modifying and adapting more appropriate teaching approaches in these subjects. Future studies to validate this observation will be valuable.

A weight modification sequential method for VSC-MTDC power system state estimation

NASA Astrophysics Data System (ADS)

Yang, Xiaonan; Zhang, Hao; Li, Qiang; Guo, Ziming; Zhao, Kun; Li, Xinpeng; Han, Feng

2017-06-01

This paper presents an effective sequential approach based on weight modification for VSC-MTDC power system state estimation, called weight modification sequential method. The proposed approach simplifies the AC/DC system state estimation algorithm through modifying the weight of state quantity to keep the matrix dimension constant. The weight modification sequential method can also make the VSC-MTDC system state estimation calculation results more ccurate and increase the speed of calculation. The effectiveness of the proposed weight modification sequential method is demonstrated and validated in modified IEEE 14 bus system.

Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

PubMed

Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

2015-11-01

Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

Research on aviation unsafe incidents classification with improved TF-IDF algorithm

NASA Astrophysics Data System (ADS)

Wang, Yanhua; Zhang, Zhiyuan; Huo, Weigang

2016-05-01

The text content of Aviation Safety Confidential Reports contains a large number of valuable information. Term frequency-inverse document frequency algorithm is commonly used in text analysis, but it does not take into account the sequential relationship of the words in the text and its role in semantic expression. According to the seven category labels of civil aviation unsafe incidents, aiming at solving the problems of TF-IDF algorithm, this paper improved TF-IDF algorithm based on co-occurrence network; established feature words extraction and words sequential relations for classified incidents. Aviation domain lexicon was used to improve the accuracy rate of classification. Feature words network model was designed for multi-documents unsafe incidents classification, and it was used in the experiment. Finally, the classification accuracy of improved algorithm was verified by the experiments.

Concurrent Learning of Control in Multi agent Sequential Decision Tasks

DTIC Science & Technology

2018-04-17

Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement...learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable...shall be subject to any oenalty for failing to comply with a collection of information if it does not display a currently valid OMB control number

Mining of high utility-probability sequential patterns from uncertain databases

PubMed Central

Zhang, Binbin; Fournier-Viger, Philippe; Li, Ting

2017-01-01

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds. PMID:28742847

Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification.

PubMed

Mirza, Bilal; Lin, Zhiping

2016-08-01

In this paper, a meta-cognitive online sequential extreme learning machine (MOS-ELM) is proposed for class imbalance and concept drift learning. In MOS-ELM, meta-cognition is used to self-regulate the learning by selecting suitable learning strategies for class imbalance and concept drift problems. MOS-ELM is the first sequential learning method to alleviate the imbalance problem for both binary class and multi-class data streams with concept drift. In MOS-ELM, a new adaptive window approach is proposed for concept drift learning. A single output update equation is also proposed which unifies various application specific OS-ELM methods. The performance of MOS-ELM is evaluated under different conditions and compared with methods each specific to some of the conditions. On most of the datasets in comparison, MOS-ELM outperforms the competing methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

2012-04-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Predictive Movements and Human Reinforcement Learning of Sequential Action

ERIC Educational Resources Information Center

de Kleijn, Roy; Kachergis, George; Hommel, Bernhard

2018-01-01

Sequential action makes up the bulk of human daily activity, and yet much remains unknown about how people learn such actions. In one motor learning paradigm, the serial reaction time (SRT) task, people are taught a consistent sequence of button presses by cueing them with the next target response. However, the SRT task only records keypress…

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

NASA Astrophysics Data System (ADS)

Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli

2018-04-01

Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.

Rational approximations to rational models: alternative algorithms for category learning.

PubMed

Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J

2010-10-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

Parallelization and implementation of approximate root isolation for nonlinear system by Monte Carlo

NASA Astrophysics Data System (ADS)

Khosravi, Ebrahim

1998-12-01

This dissertation solves a fundamental problem of isolating the real roots of nonlinear systems of equations by Monte-Carlo that were published by Bush Jones. This algorithm requires only function values and can be applied readily to complicated systems of transcendental functions. The implementation of this sequential algorithm provides scientists with the means to utilize function analysis in mathematics or other fields of science. The algorithm, however, is so computationally intensive that the system is limited to a very small set of variables, and this will make it unfeasible for large systems of equations. Also a computational technique was needed for investigating a metrology of preventing the algorithm structure from converging to the same root along different paths of computation. The research provides techniques for improving the efficiency and correctness of the algorithm. The sequential algorithm for this technique was corrected and a parallel algorithm is presented. This parallel method has been formally analyzed and is compared with other known methods of root isolation. The effectiveness, efficiency, enhanced overall performance of the parallel processing of the program in comparison to sequential processing is discussed. The message passing model was used for this parallel processing, and it is presented and implemented on Intel/860 MIMD architecture. The parallel processing proposed in this research has been implemented in an ongoing high energy physics experiment: this algorithm has been used to track neutrinoes in a super K detector. This experiment is located in Japan, and data can be processed on-line or off-line locally or remotely.

Dissociating hippocampal and striatal contributions to sequential prediction learning

PubMed Central

Bornstein, Aaron M.; Daw, Nathaniel D.

2011-01-01

Behavior may be generated on the basis of many different kinds of learned contingencies. For instance, responses could be guided by the direct association between a stimulus and response, or by sequential stimulus-stimulus relationships (as in model-based reinforcement learning or goal-directed actions). However, the neural architecture underlying sequential predictive learning is not well-understood, in part because it is difficult to isolate its effect on choice behavior. To track such learning more directly, we examined reaction times (RTs) in a probabilistic sequential picture identification task. We used computational learning models to isolate trial-by-trial effects of two distinct learning processes in behavior, and used these as signatures to analyze the separate neural substrates of each process. RTs were best explained via the combination of two delta rule learning processes with different learning rates. To examine neural manifestations of these learning processes, we used functional magnetic resonance imaging to seek correlates of timeseries related to expectancy or surprise. We observed such correlates in two regions, hippocampus and striatum. By estimating the learning rates best explaining each signal, we verified that they were uniquely associated with one of the two distinct processes identified behaviorally. These differential correlates suggest that complementary anticipatory functions drive each region's effect on behavior. Our results provide novel insights as to the quantitative computational distinctions between medial temporal and basal ganglia learning networks and enable experiments that exploit trial-by-trial measurement of the unique contributions of both hippocampus and striatum to response behavior. PMID:22487032

Optimal Sequential Rules for Computer-Based Instruction.

ERIC Educational Resources Information Center

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

Three-dimensional mapping of equiprobable hydrostratigraphic units at the Frenchman Flat Corrective Action Unit, Nevada Test Site

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

Shirley, C.; Pohlmann, K.; Andricevic, R.

1996-09-01

Geological and geophysical data are used with the sequential indicator simulation algorithm of Gomez-Hernandez and Srivastava to produce multiple, equiprobable, three-dimensional maps of informal hydrostratigraphic units at the Frenchman Flat Corrective Action Unit, Nevada Test Site. The upper 50 percent of the Tertiary volcanic lithostratigraphic column comprises the study volume. Semivariograms are modeled from indicator-transformed geophysical tool signals. Each equiprobable study volume is subdivided into discrete classes using the ISIM3D implementation of the sequential indicator simulation algorithm. Hydraulic conductivity is assigned within each class using the sequential Gaussian simulation method of Deutsch and Journel. The resulting maps show the contiguitymore » of high and low hydraulic conductivity regions.« less

Multiuser signal detection using sequential decoding

NASA Astrophysics Data System (ADS)

Xie, Zhenhua; Rushforth, Craig K.; Short, Robert T.

1990-05-01

The application of sequential decoding to the detection of data transmitted over the additive white Gaussian noise channel by K asynchronous transmitters using direct-sequence spread-spectrum multiple access is considered. A modification of Fano's (1963) sequential-decoding metric, allowing the messages from a given user to be safely decoded if its Eb/N0 exceeds -1.6 dB, is presented. Computer simulation is used to evaluate the performance of a sequential decoder that uses this metric in conjunction with the stack algorithm. In many circumstances, the sequential decoder achieves results comparable to those obtained using the much more complicated optimal receiver.

Distributed Immune Systems for Wireless Network Information Assurance

DTIC Science & Technology

2010-04-26

ratio test (SPRT), where the goal is to optimize a hypothesis testing problem given a trade-off between the probability of errors and the...using cumulative sum (CUSUM) and Girshik-Rubin-Shiryaev (GRSh) statistics. In sequential versions of the problem the sequential probability ratio ...the more complicated problems, in particular those where no clear mean can be established. We developed algorithms based on the sequential probability

Online Cross-Validation-Based Ensemble Learning

PubMed Central

Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark

2017-01-01

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. PMID:28474419

Information Retrieval: A Sequential Learning Process.

ERIC Educational Resources Information Center

Bookstein, Abraham

1983-01-01

Presents decision-theoretic models which intrinsically include retrieval of multiple documents whereby system responds to request by presenting documents to patron in sequence, gathering feedback, and using information to modify future retrievals. Document independence model, set retrieval model, sequential retrieval model, learning model,…

Scalable Parallel Density-based Clustering and Applications

NASA Astrophysics Data System (ADS)

Patwary, Mostofa Ali

2014-04-01

Recently, density-based clustering algorithms (DBSCAN and OPTICS) have gotten significant attention of the scientific community due to their unique capability of discovering arbitrary shaped clusters and eliminating noise data. These algorithms have several applications, which require high performance computing, including finding halos and subhalos (clusters) from massive cosmology data in astrophysics, analyzing satellite images, X-ray crystallography, and anomaly detection. However, parallelization of these algorithms are extremely challenging as they exhibit inherent sequential data access order, unbalanced workload resulting in low parallel efficiency. To break the data access sequentiality and to achieve high parallelism, we develop new parallel algorithms, both for DBSCAN and OPTICS, designed using graph algorithmic techniques. For example, our parallel DBSCAN algorithm exploits the similarities between DBSCAN and computing connected components. Using datasets containing up to a billion floating point numbers, we show that our parallel density-based clustering algorithms significantly outperform the existing algorithms, achieving speedups up to 27.5 on 40 cores on shared memory architecture and speedups up to 5,765 using 8,192 cores on distributed memory architecture. In our experiments, we found that while achieving the scalability, our algorithms produce clustering results with comparable quality to the classical algorithms.

Should Bilingual Children Learn Reading in Two Languages at the Same Time or in Sequence?

ERIC Educational Resources Information Center

Berens, Melody S.; Kovelman, Ioulia; Petitto, Laura-Ann

2013-01-01

Is it best to learn reading in two languages simultaneously or sequentially? We observed second- and third-grade children in two-way "dual-language learning contexts": (a) 50:50 or Simultaneous dual-language (two languages within same developmental period) and (b) 90:10 or Sequential dual-language (one language, followed gradually by the other).…

Application of the sequential quadratic programming algorithm for reconstructing the distribution of optical parameters based on the time-domain radiative transfer equation.

PubMed

Qi, Hong; Qiao, Yao-Bin; Ren, Ya-Tao; Shi, Jing-Wen; Zhang, Ze-Yu; Ruan, Li-Ming

2016-10-17

Sequential quadratic programming (SQP) is used as an optimization algorithm to reconstruct the optical parameters based on the time-domain radiative transfer equation (TD-RTE). Numerous time-resolved measurement signals are obtained using the TD-RTE as forward model. For a high computational efficiency, the gradient of objective function is calculated using an adjoint equation technique. SQP algorithm is employed to solve the inverse problem and the regularization term based on the generalized Gaussian Markov random field (GGMRF) model is used to overcome the ill-posed problem. Simulated results show that the proposed reconstruction scheme performs efficiently and accurately.

A sequential quadratic programming algorithm using an incomplete solution of the subproblem

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

Murray, W.; Prieto, F.J.

1993-05-01

We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed; in particular, it is notmore » assumed that the iterates lie on a compact set.« less

Understanding Human Motion Skill with Peak Timing Synergy

NASA Astrophysics Data System (ADS)

Ueno, Ken; Furukawa, Koichi

The careful observation of motion phenomena is important in understanding the skillful human motion. However, this is a difficult task due to the complexities in timing when dealing with the skilful control of anatomical structures. To investigate the dexterity of human motion, we decided to concentrate on timing with respect to motion, and we have proposed a method to extract the peak timing synergy from multivariate motion data. The peak timing synergy is defined as a frequent ordered graph with time stamps, which has nodes consisting of turning points in motion waveforms. A proposed algorithm, PRESTO automatically extracts the peak timing synergy. PRESTO comprises the following 3 processes: (1) detecting peak sequences with polygonal approximation; (2) generating peak-event sequences; and (3) finding frequent peak-event sequences using a sequential pattern mining method, generalized sequential patterns (GSP). Here, we measured right arm motion during the task of cello bowing and prepared a data set of the right shoulder and arm motion. We successfully extracted the peak timing synergy on cello bowing data set using the PRESTO algorithm, which consisted of common skills among cellists and personal skill differences. To evaluate the sequential pattern mining algorithm GSP in PRESTO, we compared the peak timing synergy by using GSP algorithm and the one by using filtering by reciprocal voting (FRV) algorithm as a non time-series method. We found that the support is 95 - 100% in GSP, while 83 - 96% in FRV and that the results by GSP are better than the one by FRV in the reproducibility of human motion. Therefore we show that sequential pattern mining approach is more effective to extract the peak timing synergy than non-time series analysis approach.

Stochastic Control of Multi-Scale Networks: Modeling, Analysis and Algorithms

DTIC Science & Technology

2014-10-20

Theory, (02 2012): 0. doi: B. T. Swapna, Atilla Eryilmaz, Ness B. Shroff. Throughput-Delay Analysis of Random Linear Network Coding for Wireless ... Wireless Sensor Networks and Effects of Long-Range Dependent Data, Sequential Analysis , (10 2012): 0. doi: 10.1080/07474946.2012.719435 Stefano...Sequential Analysis , (10 2012): 0. doi: John S. Baras, Shanshan Zheng. Sequential Anomaly Detection in Wireless Sensor Networks andEffects of Long

Structural drift: the population dynamics of sequential learning.

PubMed

Crutchfield, James P; Whalen, Sean

2012-01-01

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream "teacher" and then pass samples from the model to their downstream "student". It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

Linear Algebra and Sequential Importance Sampling for Network Reliability

DTIC Science & Technology

2011-12-01

first test case is an Erdős- Renyi graph with 100 vertices and 150 edges. Figure 1 depicts the relative variance of the three Algorithms: Algorithm TOP...e va ria nc e Figure 1: Relative variance of various algorithms on Erdős Renyi graph, 100 vertices 250 edges. Key: Solid = TOP-DOWN algorithm

On the Use of Adaptive Instructional Images Based on the Sequential-Global Dimension of the Felder-Silverman Learning Style Theory

ERIC Educational Resources Information Center

Filippidis, Stavros K.; Tsoukalas, Ioannis A.

2009-01-01

An adaptive educational system that uses adaptive presentation is presented. In this system fragments of different images present the same content and the system can choose the one most relevant to the user based on the sequential-global dimension of Felder-Silverman's learning style theory. In order to retrieve the learning style of each student…

RACE/A: an architectural account of the interactions between learning, task control, and retrieval dynamics.

PubMed

van Maanen, Leendert; van Rijn, Hedderik; Taatgen, Niels

2012-01-01

This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture-word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term declarative learning, and between sequential sampling and task control. In a traditional sequential sampling model, the onset of the process within the task is unclear, as is the number of sampling processes. RACE/A provides a theoretical basis for estimating the onset of sequential sampling processes during task execution and allows for easy modeling of multiple sequential sampling processes within a task. Copyright © 2011 Cognitive Science Society, Inc.

Error Control Coding Techniques for Space and Satellite Communications

NASA Technical Reports Server (NTRS)

Costello, Daniel J., Jr.; Cabral, Hermano A.; He, Jiali

1997-01-01

Bootstrap Hybrid Decoding (BHD) (Jelinek and Cocke, 1971) is a coding/decoding scheme that adds extra redundancy to a set of convolutionally encoded codewords and uses this redundancy to provide reliability information to a sequential decoder. Theoretical results indicate that bit error probability performance (BER) of BHD is close to that of Turbo-codes, without some of their drawbacks. In this report we study the use of the Multiple Stack Algorithm (MSA) (Chevillat and Costello, Jr., 1977) as the underlying sequential decoding algorithm in BHD, which makes possible an iterative version of BHD.

Sequential structural damage diagnosis algorithm using a change point detection method

NASA Astrophysics Data System (ADS)

Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

2013-11-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Parallel Algorithm Solves Coupled Differential Equations

NASA Technical Reports Server (NTRS)

Hayashi, A.

1987-01-01

Numerical methods adapted to concurrent processing. Algorithm solves set of coupled partial differential equations by numerical integration. Adapted to run on hypercube computer, algorithm separates problem into smaller problems solved concurrently. Increase in computing speed with concurrent processing over that achievable with conventional sequential processing appreciable, especially for large problems.

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

DOE PAGES

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles; ...

2018-05-05

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

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

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

Learning multiple variable-speed sequences in striatum via cortical tutoring.

PubMed

Murray, James M; Escola, G Sean

2017-05-08

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

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

Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems

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

Scherrer, Chad; Halappanavar, Mahantesh; Tewari, Ambuj

2012-07-03

We present a generic framework for parallel coordinate descent (CD) algorithms that has as special cases the original sequential algorithms of Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm of Bradley et al. We introduce two novel parallel algorithms that are also special cases---Thread-Greedy CD and Coloring-Based CD---and give performance measurements for an OpenMP implementation of these.

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

Computerized Classification Testing with the Rasch Model

ERIC Educational Resources Information Center

Eggen, Theo J. H. M.

2011-01-01

If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

J-adaptive estimation with estimated noise statistics

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Hipkins, C.

1973-01-01

The J-adaptive sequential estimator is extended to include simultaneous estimation of the noise statistics in a model for system dynamics. This extension completely automates the estimator, eliminating the requirement of an analyst in the loop. Simulations in satellite orbit determination demonstrate the efficacy of the sequential estimation algorithm.

Concurrent computation of attribute filters on shared memory parallel machines.

PubMed

Wilkinson, Michael H F; Gao, Hui; Hesselink, Wim H; Jonker, Jan-Eppo; Meijster, Arnold

2008-10-01

Morphological attribute filters have not previously been parallelized, mainly because they are both global and non-separable. We propose a parallel algorithm that achieves efficient parallelism for a large class of attribute filters, including attribute openings, closings, thinnings and thickenings, based on Salembier's Max-Trees and Min-trees. The image or volume is first partitioned in multiple slices. We then compute the Max-trees of each slice using any sequential Max-Tree algorithm. Subsequently, the Max-trees of the slices can be merged to obtain the Max-tree of the image. A C-implementation yielded good speed-ups on both a 16-processor MIPS 14000 parallel machine, and a dual-core Opteron-based machine. It is shown that the speed-up of the parallel algorithm is a direct measure of the gain with respect to the sequential algorithm used. Furthermore, the concurrent algorithm shows a speed gain of up to 72 percent on a single-core processor, due to reduced cache thrashing.

A Bayesian approach to tracking patients having changing pharmacokinetic parameters

NASA Technical Reports Server (NTRS)

Bayard, David S.; Jelliffe, Roger W.

2004-01-01

This paper considers the updating of Bayesian posterior densities for pharmacokinetic models associated with patients having changing parameter values. For estimation purposes it is proposed to use the Interacting Multiple Model (IMM) estimation algorithm, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and Maximum A-Posteriori (MAP) Bayesian estimation methods, which are presently used for posterior updating when pharmacokinetic parameters do not change. Both the MM and MAP Bayesian estimation methods are used in their sequential forms, to facilitate tracking of changing parameters. Results indicate that the IMM algorithm is well suited for tracking time-varying pharmacokinetic parameters in acutely ill and unstable patients, incurring only about half of the integrated error compared to the sequential MM and MAP methods on the same example.

Two Tales of Time: Uncovering the Significance of Sequential Patterns among Contribution Types in Knowledge-Building Discourse

ERIC Educational Resources Information Center

Chen, Bodong; Resendes, Monica; Chai, Ching Sing; Hong, Huang-Yao

2017-01-01

As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish "productive" threads of knowledge-building discourse. A database of Grade 1-6 knowledge-building discourse was first coded for…

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

Sequential and Simultaneous Processing in Children with Learning Disabilities: An Attempted Replication.

ERIC Educational Resources Information Center

Bain, Sherry K.

1993-01-01

Analysis of Kaufman Assessment Battery for Children (K-ABC) Sequential and Simultaneous Processing scores of 94 children (ages 6-12) with learning disabilities produced factor patterns generally supportive of the traditional K-ABC Mental Processing structure with the exception of Spatial Memory. The sample exhibited relative processing strengths…

Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone

NASA Technical Reports Server (NTRS)

Daida, Jason; Samadani, Ramin; Vesecky, John F.

1990-01-01

An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.

SLAM algorithm applied to robotics assistance for navigation in unknown environments.

PubMed

Cheein, Fernando A Auat; Lopez, Natalia; Soria, Carlos M; di Sciascio, Fernando A; Pereira, Fernando Lobo; Carelli, Ricardo

2010-02-17

The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI). In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents. The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface. The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.

Automated ILA design for synchronous sequential circuits

NASA Technical Reports Server (NTRS)

Liu, M. N.; Liu, K. Z.; Maki, G. K.; Whitaker, S. R.

1991-01-01

An iterative logic array (ILA) architecture for synchronous sequential circuits is presented. This technique utilizes linear algebra to produce the design equations. The ILA realization of synchronous sequential logic can be fully automated with a computer program. A programmable design procedure is proposed to fullfill the design task and layout generation. A software algorithm in the C language has been developed and tested to generate 1 micron CMOS layouts using the Hewlett-Packard FUNGEN module generator shell.

Poster error probability in the Mu-11 Sequential Ranging System

NASA Technical Reports Server (NTRS)

Coyle, C. W.

1981-01-01

An expression is derived for the posterior error probability in the Mu-2 Sequential Ranging System. An algorithm is developed which closely bounds the exact answer and can be implemented in the machine software. A computer simulation is provided to illustrate the improved level of confidence in a ranging acquisition using this figure of merit as compared to that using only the prior probabilities. In a simulation of 20,000 acquisitions with an experimentally determined threshold setting, the algorithm detected 90% of the actual errors and made false indication of errors on 0.2% of the acquisitions.

Conception of discrete systems decomposition algorithm using p-invariants and hypergraphs

NASA Astrophysics Data System (ADS)

Stefanowicz, Ł.

2016-09-01

In the article author presents an idea of decomposition algorithm of discrete systems described by Petri Nets using pinvariants. Decomposition process is significant from the point of view of discrete systems design, because it allows separation of the smaller sequential parts. Proposed algorithm uses modified Martinez-Silva method as well as author's selection algorithm. The developed method is a good complement of classical decomposition algorithms using graphs and hypergraphs.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

PubMed

Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

2014-01-01

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

PubMed Central

Monner, Derek; Reggia, James A.

2011-01-01

The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities.

PubMed

Remillard, Gilbert

2011-07-01

There is evidence that sequence learning in the traditional serial reaction time task (SRTT), where target location is the response dimension, and sequence learning in the perceptual SRTT, where target location is not the response dimension, are handled by different mechanisms. The ability of the latter mechanism to learn sequential contingencies that can be learned by the former mechanism was examined. Prior research has established that people can learn second-, third-, and fourth-order probabilities in the traditional SRTT. The present study reveals that people can learn such probabilities in the perceptual SRTT. This suggests that the two mechanisms may have similar architectures. A possible neural basis of the two mechanisms is discussed.

Progressive data transmission for anatomical landmark detection in a cloud.

PubMed

Sofka, M; Ralovich, K; Zhang, J; Zhou, S K; Comaniciu, D

2012-01-01

In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

A Fast parallel tridiagonal algorithm for a class of CFD applications

NASA Technical Reports Server (NTRS)

Moitra, Stuti; Sun, Xian-He

1996-01-01

The parallel diagonal dominant (PDD) algorithm is an efficient tridiagonal solver. This paper presents for study a variation of the PDD algorithm, the reduced PDD algorithm. The new algorithm maintains the minimum communication provided by the PDD algorithm, but has a reduced operation count. The PDD algorithm also has a smaller operation count than the conventional sequential algorithm for many applications. Accuracy analysis is provided for the reduced PDD algorithm for symmetric Toeplitz tridiagonal (STT) systems. Implementation results on Langley's Intel Paragon and IBM SP2 show that both the PDD and reduced PDD algorithms are efficient and scalable.

The Effects of Partial Reinforcement in the Acquisition and Extinction of Recurrent Serial Patterns.

ERIC Educational Resources Information Center

Dockstader, Steven L.

The purpose of these 2 experiments was to determine whether sequential response pattern behavior is affected by partial reinforcement in the same way as other behavior systems. The first experiment investigated the partial reinforcement extinction effects (PREE) in a sequential concept learning task where subjects were required to learn a…

Contribution of Implicit Sequence Learning to Spoken Language Processing: Some Preliminary Findings with Hearing Adults

ERIC Educational Resources Information Center

Conway, Christopher M.; Karpicke, Jennifer; Pisoni, David B.

2007-01-01

Spoken language consists of a complex, sequentially arrayed signal that contains patterns that can be described in terms of statistical relations among language units. Previous research has suggested that a domain-general ability to learn structured sequential patterns may underlie language acquisition. To test this prediction, we examined the…

Addressing Challenges in Web Accessibility for the Blind and Visually Impaired

ERIC Educational Resources Information Center

Guercio, Angela; Stirbens, Kathleen A.; Williams, Joseph; Haiber, Charles

2011-01-01

Searching for relevant information on the web is an important aspect of distance learning. This activity is a challenge for visually impaired distance learners. While sighted people have the ability to filter information in a fast and non sequential way, blind persons rely on tools that process the information in a sequential way. Learning is…

Extinction and Renewal of Pavlovian Modulation in Human Sequential Feature Positive Discrimination Learning

ERIC Educational Resources Information Center

Baeyens, Frank; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Kerkhof, Ineke; De Ceulaer, Annick

2005-01-01

Using a conditioned suppression task, we investigated extinction and renewal of Pavlovian modulation in human sequential Feature Positive (FP) discrimination learning. In Experiment 1, in context a participants were first trained on two FP discriminations, X[right arrow]A+/A- and Y[right arrow]B+/B-. Extinction treatment was administered in the…

Similar Neural Correlates for Language and Sequential Learning: Evidence from Event-Related Brain Potentials

ERIC Educational Resources Information Center

Christiansen, Morten H.; Conway, Christopher M.; Onnis, Luca

2012-01-01

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (1) a sequential learning task involving complex structured sequences and (2) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic…

Simultaneous and Sequential Feature Negative Discriminations: Elemental Learning and Occasion Setting in Human Pavlovian Conditioning

ERIC Educational Resources Information Center

Baeyens, Frank; Vervliet, Bram; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Eelen, Paul

2004-01-01

Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discrimination learning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

Sequential Prediction of Literacy Achievement for Specific Learning Disabilities Contrasting in Impaired Levels of Language in Grades 4 to 9

ERIC Educational Resources Information Center

Sanders, Elizabeth A.; Berninger, Virginia W.; Abbott, Robert D.

2018-01-01

Sequential regression was used to evaluate whether language-related working memory components uniquely predict reading and writing achievement beyond cognitive-linguistic translation for students in Grades 4 through 9 (N = 103) with specific learning disabilities (SLDs) in subword handwriting (dysgraphia, n = 25), word reading and spelling…

Online cross-validation-based ensemble learning.

PubMed

Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark

2018-01-30

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Dizocilpine (MK-801) impairs learning in the active place avoidance task but has no effect on the performance during task/context alternation.

PubMed

Vojtechova, Iveta; Petrasek, Tomas; Hatalova, Hana; Pistikova, Adela; Vales, Karel; Stuchlik, Ales

2016-05-15

The prevention of engram interference, pattern separation, flexibility, cognitive coordination and spatial navigation are usually studied separately at the behavioral level. Impairment in executive functions is often observed in patients suffering from schizophrenia. We have designed a protocol for assessing these functions all together as behavioral separation. This protocol is based on alternated or sequential training in two tasks testing different hippocampal functions (the Morris water maze and active place avoidance), and alternated or sequential training in two similar environments of the active place avoidance task. In Experiment 1, we tested, in adult rats, whether the performance in two different spatial tasks was affected by their order in sequential learning, or by their day-to-day alternation. In Experiment 2, rats learned to solve the active place avoidance task in two environments either alternately or sequentially. We found that rats are able to acquire both tasks and to discriminate both similar contexts without obvious problems regardless of the order or the alternation. We used two groups of rats, controls and a rat model of psychosis induced by a subchronic intraperitoneal application of 0.08mg/kg of dizocilpine (MK-801), a non-competitive antagonist of NMDA receptors. Dizocilpine had no selective effect on parallel/sequential learning of tasks/contexts. However, it caused hyperlocomotion and a significant deficit in learning in the active place avoidance task regardless of the task alternation. Cognitive coordination tested by this task is probably more sensitive to dizocilpine than spatial orientation because no hyperactivity or learning impairment was observed in the Morris water maze. Copyright © 2016 Elsevier B.V. All rights reserved.

High data rate coding for the space station telemetry links.

NASA Technical Reports Server (NTRS)

Lumb, D. R.; Viterbi, A. J.

1971-01-01

Coding systems for high data rates were examined from the standpoint of potential application in space-station telemetry links. Approaches considered included convolutional codes with sequential, Viterbi, and cascaded-Viterbi decoding. It was concluded that a high-speed (40 Mbps) sequential decoding system best satisfies the requirements for the assumed growth potential and specified constraints. Trade-off studies leading to this conclusion are viewed, and some sequential (Fano) algorithm improvements are discussed, together with real-time simulation results.

The [Gamma] Algorithm and Some Applications

ERIC Educational Resources Information Center

Castillo, Enrique; Jubete, Francisco

2004-01-01

In this paper the power of the [gamma] algorithm for obtaining the dual of a given cone and some of its multiple applications is discussed. The meaning of each sequential tableau appearing during the process is interpreted. It is shown that each tableau contains the generators of the dual cone of a given cone and that the algorithm updates the…

Boundary and object detection in real world images. [by means of algorithms

NASA Technical Reports Server (NTRS)

Yakimovsky, Y.

1974-01-01

A solution to the problem of automatic location of objects in digital pictures by computer is presented. A self-scaling local edge detector which can be applied in parallel on a picture is described. Clustering algorithms and boundary following algorithms which are sequential in nature process the edge data to locate images of objects.

Index of learning styles in a u.s. School of pharmacy.

PubMed

Teevan, Colleen J; Li, Michael; Schlesselman, Lauren S

2011-04-01

The goal of this study was to assess for a predominance of learning styles among pharmacy students at an accredited U.S. school of pharmacy. Following approval by the Institutional Review Board, the Index of Learning Styles© was administered to 210 pharmacy students. The survey provides results within 4 domains: perception, input, processing, and understanding. Analyses were conducted to determine trends in student learning styles. Within the four domains, 84% of students showed a preference toward sensory perception, 66% toward visual input, and 74% toward sequential understanding. Students showed no significant preference for active or reflective processing. Preferences were of moderate strength for the sensing, visual, and sequential learning styles. Students showed preferences for sensing, visual, and sequential learning styles with gender playing a role in learning style preferences. Faculty should be aware, despite some preferences, a mix of learning styles exists. To focus on the preferences found, instructors should focus teaching in a logical progression while adding visual aids. To account for other types of learning styles found, the instructors can offer other approaches and provide supplemental activities for those who would benefit from them. Further research is necessary to compare these learning styles to the teaching styles of pharmacy preceptors and faculty at schools of pharmacy.

The PlusCal Algorithm Language

NASA Astrophysics Data System (ADS)

Lamport, Leslie

Algorithms are different from programs and should not be described with programming languages. The only simple alternative to programming languages has been pseudo-code. PlusCal is an algorithm language that can be used right now to replace pseudo-code, for both sequential and concurrent algorithms. It is based on the TLA + specification language, and a PlusCal algorithm is automatically translated to a TLA + specification that can be checked with the TLC model checker and reasoned about formally.

The timing of language learning shapes brain structure associated with articulation.

PubMed

Berken, Jonathan A; Gracco, Vincent L; Chen, Jen-Kai; Klein, Denise

2016-09-01

We compared the brain structure of highly proficient simultaneous (two languages from birth) and sequential (second language after age 5) bilinguals, who differed only in their degree of native-like accent, to determine how the brain develops when a skill is acquired from birth versus later in life. For the simultaneous bilinguals, gray matter density was increased in the left putamen, as well as in the left posterior insula, right dorsolateral prefrontal cortex, and left and right occipital cortex. For the sequential bilinguals, gray matter density was increased in the bilateral premotor cortex. Sequential bilinguals with better accents also showed greater gray matter density in the left putamen, and in several additional brain regions important for sensorimotor integration and speech-motor control. Our findings suggest that second language learning results in enhanced brain structure of specific brain areas, which depends on whether two languages are learned simultaneously or sequentially, and on the extent to which native-like proficiency is acquired.

Self-regulated learning of important information under sequential and simultaneous encoding conditions.

PubMed

Middlebrooks, Catherine D; Castel, Alan D

2018-05-01

Learners make a number of decisions when attempting to study efficiently: they must choose which information to study, for how long to study it, and whether to restudy it later. The current experiments examine whether documented impairments to self-regulated learning when studying information sequentially, as opposed to simultaneously, extend to the learning of and memory for valuable information. In Experiment 1, participants studied lists of words ranging in value from 1-10 points sequentially or simultaneously at a preset presentation rate; in Experiment 2, study was self-paced and participants could choose to restudy. Although participants prioritized high-value over low-value information, irrespective of presentation, those who studied the items simultaneously demonstrated superior value-based prioritization with respect to recall, study selections, and self-pacing. The results of the present experiments support the theory that devising, maintaining, and executing efficient study agendas is inherently different under sequential formatting than simultaneous. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

PubMed Central

Jawarneh, Sana; Abdullah, Salwani

2015-01-01

This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

ARCH: Adaptive recurrent-convolutional hybrid networks for long-term action recognition

PubMed Central

Xin, Miao; Zhang, Hong; Wang, Helong; Sun, Mingui; Yuan, Ding

2017-01-01

Recognition of human actions from digital video is a challenging task due to complex interfering factors in uncontrolled realistic environments. In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities. Utilizing a cognitive-based data reduction method and a hybrid “network upon networks” architecture, we extract human action representations which are robust against spatial and temporal interferences and adaptive to variations in both action speed and duration. We evaluated our method on the UCF101 and other three challenging datasets. Our results demonstrated a superior performance of the proposed algorithm in human action recognition. PMID:29290647

Decision theory, reinforcement learning, and the brain.

PubMed

Dayan, Peter; Daw, Nathaniel D

2008-12-01

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

Solving the infeasible trust-region problem using approximations.

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

Renaud, John E.; Perez, Victor M.; Eldred, Michael Scott

2004-07-01

The use of optimization in engineering design has fueled the development of algorithms for specific engineering needs. When the simulations are expensive to evaluate or the outputs present some noise, the direct use of nonlinear optimizers is not advisable, since the optimization process will be expensive and may result in premature convergence. The use of approximations for both cases is an alternative investigated by many researchers including the authors. When approximations are present, a model management is required for proper convergence of the algorithm. In nonlinear programming, the use of trust-regions for globalization of a local algorithm has been provenmore » effective. The same approach has been used to manage the local move limits in sequential approximate optimization frameworks as in Alexandrov et al., Giunta and Eldred, Perez et al. , Rodriguez et al., etc. The experience in the mathematical community has shown that more effective algorithms can be obtained by the specific inclusion of the constraints (SQP type of algorithms) rather than by using a penalty function as in the augmented Lagrangian formulation. The presence of explicit constraints in the local problem bounded by the trust region, however, may have no feasible solution. In order to remedy this problem the mathematical community has developed different versions of a composite steps approach. This approach consists of a normal step to reduce the amount of constraint violation and a tangential step to minimize the objective function maintaining the level of constraint violation attained at the normal step. Two of the authors have developed a different approach for a sequential approximate optimization framework using homotopy ideas to relax the constraints. This algorithm called interior-point trust-region sequential approximate optimization (IPTRSAO) presents some similarities to the two normal-tangential steps algorithms. In this paper, a description of the similarities is presented and an expansion of the two steps algorithm is presented for the case of approximations.« less

The Integration of Study and Work-Integrated Learning Experience through the Sequential, Embedded Completion of Tertiary Qualifications

ERIC Educational Resources Information Center

Whannell, Patricia; Humphries, Judy; Whannell, Robert; Usher, Kim

2015-01-01

A number of different models have been developed to integrate both Vocational Education and Training (VET) and university study with a view to producing work-ready graduates. This paper describes one joint initiative which allows students to integrate their theoretical study and work-integrated learning (WIL) experience by supporting a sequential,…

Exploring the Factors That Affect the Intention to Use Collaborative Technologies: The Differing Perspectives of Sequential/Global Learners

ERIC Educational Resources Information Center

Huang, Yong-Ming

2015-01-01

The use of collaborative technologies in learning has received considerable attention in recent years, but few studies to date have examined the factors that affect sequential and global learners' intention to use such technologies. Previous studies have shown that the learners of different learning styles have different needs for educational…

Optimal trajectories of aircraft and spacecraft

NASA Technical Reports Server (NTRS)

Miele, A.

1990-01-01

Work done on algorithms for the numerical solutions of optimal control problems and their application to the computation of optimal flight trajectories of aircraft and spacecraft is summarized. General considerations on calculus of variations, optimal control, numerical algorithms, and applications of these algorithms to real-world problems are presented. The sequential gradient-restoration algorithm (SGRA) is examined for the numerical solution of optimal control problems of the Bolza type. Both the primal formulation and the dual formulation are discussed. Aircraft trajectories, in particular, the application of the dual sequential gradient-restoration algorithm (DSGRA) to the determination of optimal flight trajectories in the presence of windshear are described. Both take-off trajectories and abort landing trajectories are discussed. Take-off trajectories are optimized by minimizing the peak deviation of the absolute path inclination from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. The survival capability of an aircraft in a severe windshear is discussed, and the optimal trajectories are found to be superior to both constant pitch trajectories and maximum angle of attack trajectories. Spacecraft trajectories, in particular, the application of the primal sequential gradient-restoration algorithm (PSGRA) to the determination of optimal flight trajectories for aeroassisted orbital transfer are examined. Both the coplanar case and the noncoplanar case are discussed within the frame of three problems: minimization of the total characteristic velocity; minimization of the time integral of the square of the path inclination; and minimization of the peak heating rate. The solution of the second problem is called nearly-grazing solution, and its merits are pointed out as a useful engineering compromise between energy requirements and aerodynamics heating requirements.

Optimal integer resolution for attitude determination using global positioning system signals

NASA Technical Reports Server (NTRS)

Crassidis, John L.; Markley, F. Landis; Lightsey, E. Glenn

1998-01-01

In this paper, a new motion-based algorithm for GPS integer ambiguity resolution is derived. The first step of this algorithm converts the reference sightline vectors into body frame vectors. This is accomplished by an optimal vectorized transformation of the phase difference measurements. The result of this transformation leads to the conversion of the integer ambiguities to vectorized biases. This essentially converts the problem to the familiar magnetometer-bias determination problem, for which an optimal and efficient solution exists. Also, the formulation in this paper is re-derived to provide a sequential estimate, so that a suitable stopping condition can be found during the vehicle motion. The advantages of the new algorithm include: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can sequentially estimate the ambiguities during the vehicle motion. The only disadvantage of the new algorithm is that it requires at least three non-coplanar baselines. The performance of the new algorithm is tested on a dynamic hardware simulator.

A Hybrid Shared-Memory Parallel Max-Tree Algorithm for Extreme Dynamic-Range Images.

PubMed

Moschini, Ugo; Meijster, Arnold; Wilkinson, Michael H F

2018-03-01

Max-trees, or component trees, are graph structures that represent the connected components of an image in a hierarchical way. Nowadays, many application fields rely on images with high-dynamic range or floating point values. Efficient sequential algorithms exist to build trees and compute attributes for images of any bit depth. However, we show that the current parallel algorithms perform poorly already with integers at bit depths higher than 16 bits per pixel. We propose a parallel method combining the two worlds of flooding and merging max-tree algorithms. First, a pilot max-tree of a quantized version of the image is built in parallel using a flooding method. Later, this structure is used in a parallel leaf-to-root approach to compute efficiently the final max-tree and to drive the merging of the sub-trees computed by the threads. We present an analysis of the performance both on simulated and actual 2D images and 3D volumes. Execution times are about better than the fastest sequential algorithm and speed-up goes up to on 64 threads.

Solving constrained minimum-time robot problems using the sequential gradient restoration algorithm

NASA Technical Reports Server (NTRS)

Lee, Allan Y.

1991-01-01

Three constrained minimum-time control problems of a two-link manipulator are solved using the Sequential Gradient and Restoration Algorithm (SGRA). The inequality constraints considered are reduced via Valentine-type transformations to nondifferential path equality constraints. The SGRA is then used to solve these transformed problems with equality constraints. The results obtained indicate that at least one of the two controls is at its limits at any instant in time. The remaining control then adjusts itself so that none of the system constraints is violated. Hence, the minimum-time control is either a pure bang-bang control or a combined bang-bang/singular control.

Security Criteria for Distributed Systems: Functional Requirements.

DTIC Science & Technology

1995-09-01

Open Company Limited. Ziv , J. and A. Lempel . 1977. A Universal Algorithm for Sequential Data Compression . IEEE Transactions on Information Theory Vol...3, SCF-5 DCF-7. Configurable Cryptographic Algorithms (a) It shall be possible to configure the system such that the data confidentiality functions...use different cryptographic algorithms for different protocols (e.g., mail or interprocess communication data ). (b) The modes of encryption

Decision Aids for Naval Air ASW

DTIC Science & Technology

1980-03-15

Algorithm for Zone Optimization Investigation) NADC Developing Sonobuoy Pattern for Air ASW Search DAISY (Decision Aiding Information System) Wharton...sion making behavior. 0 Artificial intelligence sequential pattern recognition algorithm for reconstructing the decision maker’s utility functions. 0...display presenting the uncertainty area of the target. 3.1.5 Algorithm for Zone Optimization Investigation (AZOI) -- Naval Air Development Center 0 A

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

PubMed

Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

The nondeterministic divide

NASA Technical Reports Server (NTRS)

Charlesworth, Arthur

1990-01-01

The nondeterministic divide partitions a vector into two non-empty slices by allowing the point of division to be chosen nondeterministically. Support for high-level divide-and-conquer programming provided by the nondeterministic divide is investigated. A diva algorithm is a recursive divide-and-conquer sequential algorithm on one or more vectors of the same range, whose division point for a new pair of recursive calls is chosen nondeterministically before any computation is performed and whose recursive calls are made immediately after the choice of division point; also, access to vector components is only permitted during activations in which the vector parameters have unit length. The notion of diva algorithm is formulated precisely as a diva call, a restricted call on a sequential procedure. Diva calls are proven to be intimately related to associativity. Numerous applications of diva calls are given and strategies are described for translating a diva call into code for a variety of parallel computers. Thus diva algorithms separate logical correctness concerns from implementation concerns.

Separation of left and right lungs using 3D information of sequential CT images and a guided dynamic programming algorithm

PubMed Central

Park, Sang Cheol; Leader, Joseph Ken; Tan, Jun; Lee, Guee Sang; Kim, Soo Hyung; Na, In Seop; Zheng, Bin

2011-01-01

Objective this article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on CT examinations. Methods we developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. Results the scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing dataset of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. Conclusions The proposed method is able to robustly and accurately disconnect all connections between left and right lungs and the guided dynamic programming algorithm is able to remove redundant processing. PMID:21412104

Separation of left and right lungs using 3-dimensional information of sequential computed tomography images and a guided dynamic programming algorithm.

PubMed

Park, Sang Cheol; Leader, Joseph Ken; Tan, Jun; Lee, Guee Sang; Kim, Soo Hyung; Na, In Seop; Zheng, Bin

2011-01-01

This article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on computed tomography (CT) examinations. We developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. The scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing data set of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. The proposed method is able to robustly and accurately disconnect all connections between left and right lungs, and the guided dynamic programming algorithm is able to remove redundant processing.

Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.

PubMed

Douglas, P K; Harris, Sam; Yuille, Alan; Cohen, Mark S

2011-05-15

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief. Copyright © 2010 Elsevier Inc. All rights reserved.

Fast and Accurate Support Vector Machines on Large Scale Systems

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

Vishnu, Abhinav; Narasimhan, Jayenthi; Holder, Larry

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminatemore » the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.« less

SymDex: increasing the efficiency of chemical fingerprint similarity searches for comparing large chemical libraries by using query set indexing.

PubMed

Tai, David; Fang, Jianwen

2012-08-27

The large sizes of today's chemical databases require efficient algorithms to perform similarity searches. It can be very time consuming to compare two large chemical databases. This paper seeks to build upon existing research efforts by describing a novel strategy for accelerating existing search algorithms for comparing large chemical collections. The quest for efficiency has focused on developing better indexing algorithms by creating heuristics for searching individual chemical against a chemical library by detecting and eliminating needless similarity calculations. For comparing two chemical collections, these algorithms simply execute searches for each chemical in the query set sequentially. The strategy presented in this paper achieves a speedup upon these algorithms by indexing the set of all query chemicals so redundant calculations that arise in the case of sequential searches are eliminated. We implement this novel algorithm by developing a similarity search program called Symmetric inDexing or SymDex. SymDex shows over a 232% maximum speedup compared to the state-of-the-art single query search algorithm over real data for various fingerprint lengths. Considerable speedup is even seen for batch searches where query set sizes are relatively small compared to typical database sizes. To the best of our knowledge, SymDex is the first search algorithm designed specifically for comparing chemical libraries. It can be adapted to most, if not all, existing indexing algorithms and shows potential for accelerating future similarity search algorithms for comparing chemical databases.

The composite sequential clustering technique for analysis of multispectral scanner data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

Observation Uncertainty in Gaussian Sensor Networks

DTIC Science & Technology

2006-01-23

Ziv , J., and Lempel , A. A universal algorithm for sequential data compression . IEEE Transactions on Information Theory 23, 3 (1977), 337–343. 73 ...using the Lempel - Ziv algorithm [42], context-tree weighting [41], or the Burrows-Wheeler Trans- form [4], [15], for example. These source codes will...and Computation (Monticello, IL, September 2004). [4] Burrows, M., and Wheeler, D. A block sorting lossless data compression algorithm . Tech.

A parallel algorithm for the eigenvalues and eigenvectors for a general complex matrix

NASA Technical Reports Server (NTRS)

Shroff, Gautam

1989-01-01

A new parallel Jacobi-like algorithm is developed for computing the eigenvalues of a general complex matrix. Most parallel methods for this parallel typically display only linear convergence. Sequential norm-reducing algorithms also exit and they display quadratic convergence in most cases. The new algorithm is a parallel form of the norm-reducing algorithm due to Eberlein. It is proven that the asymptotic convergence rate of this algorithm is quadratic. Numerical experiments are presented which demonstrate the quadratic convergence of the algorithm and certain situations where the convergence is slow are also identified. The algorithm promises to be very competitive on a variety of parallel architectures.

A Comparison of Two Instructional Methods for Teaching Logo to Learning Disabled and Nonlearning Disabled Children.

ERIC Educational Resources Information Center

Mathinos, Debra A.; Leonard, Ann Scheier

The study examines the use of LOGO, a computer language, with 19 learning disabled (LD) and 19 non-LD students in grades 4-6. Ss were randomly assigned to one of two instructional groups: sequential or whole-task, each with 10 LD and 10 non-LD students. The sequential method features a carefully ordered plan for teaching LOGO commands; the…

Edge-following algorithm for tracking geological features

NASA Technical Reports Server (NTRS)

Tietz, J. C.

1977-01-01

Sequential edge-tracking algorithm employs circular scanning to point permit effective real-time tracking of coastlines and rivers from earth resources satellites. Technique eliminates expensive high-resolution cameras. System might also be adaptable for application in monitoring automated assembly lines, inspecting conveyor belts, or analyzing thermographs, or x ray images.

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

Chrisochoides, N.; Sukup, F.

In this paper we present a parallel implementation of the Bowyer-Watson (BW) algorithm using the task-parallel programming model. The BW algorithm constitutes an ideal mesh refinement strategy for implementing a large class of unstructured mesh generation techniques on both sequential and parallel computers, by preventing the need for global mesh refinement. Its implementation on distributed memory multicomputes using the traditional data-parallel model has been proven very inefficient due to excessive synchronization needed among processors. In this paper we demonstrate that with the task-parallel model we can tolerate synchronization costs inherent to data-parallel methods by exploring concurrency in the processor level.more » Our preliminary performance data indicate that the task- parallel approach: (i) is almost four times faster than the existing data-parallel methods, (ii) scales linearly, and (iii) introduces minimum overheads compared to the {open_quotes}best{close_quotes} sequential implementation of the BW algorithm.« less

A sequential coalescent algorithm for chromosomal inversions

PubMed Central

Peischl, S; Koch, E; Guerrero, R F; Kirkpatrick, M

2013-01-01

Chromosomal inversions are common in natural populations and are believed to be involved in many important evolutionary phenomena, including speciation, the evolution of sex chromosomes and local adaptation. While recent advances in sequencing and genotyping methods are leading to rapidly increasing amounts of genome-wide sequence data that reveal interesting patterns of genetic variation within inverted regions, efficient simulation methods to study these patterns are largely missing. In this work, we extend the sequential Markovian coalescent, an approximation to the coalescent with recombination, to include the effects of polymorphic inversions on patterns of recombination. Results show that our algorithm is fast, memory-efficient and accurate, making it feasible to simulate large inversions in large populations for the first time. The SMC algorithm enables studies of patterns of genetic variation (for example, linkage disequilibria) and tests of hypotheses (using simulation-based approaches) that were previously intractable. PMID:23632894

Optimal trajectories for aeroassisted orbital transfer

NASA Technical Reports Server (NTRS)

Miele, A.; Venkataraman, P.

1983-01-01

Consideration is given to classical and minimax problems involved in aeroassisted transfer from high earth orbit (HEO) to low earth orbit (LEO). The transfer is restricted to coplanar operation, with trajectory control effected by means of lift modulation. The performance of the maneuver is indexed to the energy expenditure or, alternatively, the time integral of the heating rate. Firist-order optimality conditions are defined for the classical approach, as are a sequential gradient-restoration algorithm and a combined gradient-restoration algorithm. Minimization techniques are presented for the aeroassisted transfer energy consumption and time-delay integral of the heating rate, as well as minimization of the pressure. It is shown that the eigenvalues of the Jacobian matrix of the differential system is both stiff and unstable, implying that the sequential gradient restoration algorithm in its present version is unsuitable. A new method, involving a multipoint approach to the two-poing boundary value problem, is recommended.

SLAM algorithm applied to robotics assistance for navigation in unknown environments

PubMed Central

2010-01-01

Background The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI). Methods In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents. Results The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface. Conclusions The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation. PMID:20163735

Disentangling beat perception from sequential learning and examining the influence of attention and musical abilities on ERP responses to rhythm.

PubMed

Bouwer, Fleur L; Werner, Carola M; Knetemann, Myrthe; Honing, Henkjan

2016-05-01

Beat perception is the ability to perceive temporal regularity in musical rhythm. When a beat is perceived, predictions about upcoming events can be generated. These predictions can influence processing of subsequent rhythmic events. However, statistical learning of the order of sounds in a sequence can also affect processing of rhythmic events and must be differentiated from beat perception. In the current study, using EEG, we examined the effects of attention and musical abilities on beat perception. To ensure we measured beat perception and not absolute perception of temporal intervals, we used alternating loud and soft tones to create a rhythm with two hierarchical metrical levels. To control for sequential learning of the order of the different sounds, we used temporally regular (isochronous) and jittered rhythmic sequences. The order of sounds was identical in both conditions, but only the regular condition allowed for the perception of a beat. Unexpected intensity decrements were introduced on the beat and offbeat. In the regular condition, both beat perception and sequential learning were expected to enhance detection of these deviants on the beat. In the jittered condition, only sequential learning was expected to affect processing of the deviants. ERP responses to deviants were larger on the beat than offbeat in both conditions. Importantly, this difference was larger in the regular condition than in the jittered condition, suggesting that beat perception influenced responses to rhythmic events in addition to sequential learning. The influence of beat perception was present both with and without attention directed at the rhythm. Moreover, beat perception as measured with ERPs correlated with musical abilities, but only when attention was directed at the stimuli. Our study shows that beat perception is possible when attention is not directed at a rhythm. In addition, our results suggest that attention may mediate the influence of musical abilities on beat perception. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

On the specificity of sequential congruency effects in implicit learning of motor and perceptual sequences.

PubMed

D'Angelo, Maria C; Jiménez, Luis; Milliken, Bruce; Lupiáñez, Juan

2013-01-01

Individuals experience less interference from conflicting information following events that contain conflicting information. Recently, Jiménez, Lupiáñez, and Vaquero (2009) demonstrated that such adaptations to conflict occur even when the source of conflict arises from implicit knowledge of sequences. There is accumulating evidence that momentary changes in adaptations made in response to conflicting information are conflict-type specific (e.g., Funes, Lupiáñez, & Humphreys, 2010a), suggesting that there are multiple modes of control. The current study examined whether conflict-specific sequential congruency effects occur when the 2 sources of conflict are implicitly learned. Participants implicitly learned a motor sequence while simultaneously learning a perceptual sequence. In a first experiment, after learning the 2 orthogonal sequences, participants expressed knowledge of the 2 sequences independently of each other in a transfer phase. In Experiments 2 and 3, within each sequence, the presence of a single control trial disrupted the expression of this specific type of learning on the following trial. There was no evidence of cross-conflict modulations in the expression of sequence learning. The results suggest that the mechanisms involved in transient shifts in conflict-specific control, as reflected in sequential congruency effects, are also engaged when the source of conflict is implicit. (c) 2013 APA, all rights reserved.

Neuromusculoskeletal model self-calibration for on-line sequential bayesian moment estimation

NASA Astrophysics Data System (ADS)

Bueno, Diana R.; Montano, L.

2017-04-01

Objective. Neuromusculoskeletal models involve many subject-specific physiological parameters that need to be adjusted to adequately represent muscle properties. Traditionally, neuromusculoskeletal models have been calibrated with a forward-inverse dynamic optimization which is time-consuming and unfeasible for rehabilitation therapy. Non self-calibration algorithms have been applied to these models. To the best of our knowledge, the algorithm proposed in this work is the first on-line calibration algorithm for muscle models that allows a generic model to be adjusted to different subjects in a few steps. Approach. In this paper we propose a reformulation of the traditional muscle models that is able to sequentially estimate the kinetics (net joint moments), and also its full self-calibration (subject-specific internal parameters of the muscle from a set of arbitrary uncalibrated data), based on the unscented Kalman filter. The nonlinearity of the model as well as its calibration problem have obliged us to adopt the sum of Gaussians filter suitable for nonlinear systems. Main results. This sequential Bayesian self-calibration algorithm achieves a complete muscle model calibration using as input only a dataset of uncalibrated sEMG and kinematics data. The approach is validated experimentally using data from the upper limbs of 21 subjects. Significance. The results show the feasibility of neuromusculoskeletal model self-calibration. This study will contribute to a better understanding of the generalization of muscle models for subject-specific rehabilitation therapies. Moreover, this work is very promising for rehabilitation devices such as electromyography-driven exoskeletons or prostheses.

Superpixel-based graph cuts for accurate stereo matching

NASA Astrophysics Data System (ADS)

Feng, Liting; Qin, Kaihuai

2017-06-01

Estimating the surface normal vector and disparity of a pixel simultaneously, also known as three-dimensional label method, has been widely used in recent continuous stereo matching problem to achieve sub-pixel accuracy. However, due to the infinite label space, it’s extremely hard to assign each pixel an appropriate label. In this paper, we present an accurate and efficient algorithm, integrating patchmatch with graph cuts, to approach this critical computational problem. Besides, to get robust and precise matching cost, we use a convolutional neural network to learn a similarity measure on small image patches. Compared with other MRF related methods, our method has several advantages: its sub-modular property ensures a sub-problem optimality which is easy to perform in parallel; graph cuts can simultaneously update multiple pixels, avoiding local minima caused by sequential optimizers like belief propagation; it uses segmentation results for better local expansion move; local propagation and randomization can easily generate the initial solution without using external methods. Middlebury experiments show that our method can get higher accuracy than other MRF-based algorithms.

Implementation of combined SVM-algorithm and computer-aided perception feedback for pulmonary nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Rannou, Didier; Brennan, Patrick C.

2012-02-01

This pilot study examines the effect of a novel decision support system in medical image interpretation. This system is based on combining image spatial frequency properties and eye-tracking data in order to recognize over and under calling errors. Thus, before it can be implemented as a detection aided schema, training is required during which SVMbased algorithm learns to recognize FP from all reported outcomes, and, FN from all unreported prolonged dwelled regions. Eight radiologists inspected 50 PA chest radiographs with the specific task of identifying lung nodules. Twentyfive cases contained CT proven subtle malignant lesions (5-20mm), but prevalence was not known by the subjects, who took part in two sequential reading sessions, the second, without and with support system feedback. MCMR ROC DBM and JAFROC analyses were conducted and demonstrated significantly higher scores following feedback with p values of 0.04, and 0.03 respectively, highlighting significant improvements in radiology performance once feedback was used. This positive effect on radiologists' performance might have important implications for future CAD-system development.

Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

PubMed

Ye, Jiaxing; Kobayashi, Takumi; Iwata, Masaya; Tsuda, Hiroshi; Murakawa, Masahiro

2018-03-09

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals

PubMed Central

Fourment, Mathieu; Claywell, Brian C; Dinh, Vu; McCoy, Connor; Matsen IV, Frederick A; Darling, Aaron E

2018-01-01

Abstract Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop “guided” proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy. PMID:29186587

Multigrid methods in structural mechanics

NASA Technical Reports Server (NTRS)

Raju, I. S.; Bigelow, C. A.; Taasan, S.; Hussaini, M. Y.

1986-01-01

Although the application of multigrid methods to the equations of elasticity has been suggested, few such applications have been reported in the literature. In the present work, multigrid techniques are applied to the finite element analysis of a simply supported Bernoulli-Euler beam, and various aspects of the multigrid algorithm are studied and explained in detail. In this study, six grid levels were used to model half the beam. With linear prolongation and sequential ordering, the multigrid algorithm yielded results which were of machine accuracy with work equivalent to 200 standard Gauss-Seidel iterations on the fine grid. Also with linear prolongation and sequential ordering, the V(1,n) cycle with n greater than 2 yielded better convergence rates than the V(n,1) cycle. The restriction and prolongation operators were derived based on energy principles. Conserving energy during the inter-grid transfers required that the prolongation operator be the transpose of the restriction operator, and led to improved convergence rates. With energy-conserving prolongation and sequential ordering, the multigrid algorithm yielded results of machine accuracy with a work equivalent to 45 Gauss-Seidel iterations on the fine grid. The red-black ordering of relaxations yielded solutions of machine accuracy in a single V(1,1) cycle, which required work equivalent to about 4 iterations on the finest grid level.

The cost-effectiveness of 10 antenatal syphilis screening and treatment approaches in Peru, Tanzania, and Zambia.

PubMed

Terris-Prestholt, Fern; Vickerman, Peter; Torres-Rueda, Sergio; Santesso, Nancy; Sweeney, Sedona; Mallma, Patricia; Shelley, Katharine D; Garcia, Patricia J; Bronzan, Rachel; Gill, Michelle M; Broutet, Nathalie; Wi, Teodora; Watts, Charlotte; Mabey, David; Peeling, Rosanna W; Newman, Lori

2015-06-01

Rapid plasma reagin (RPR) is frequently used to test women for maternal syphilis. Rapid syphilis immunochromatographic strip tests detecting only Treponema pallidum antibodies (single RSTs) or both treponemal and non-treponemal antibodies (dual RSTs) are now available. This study assessed the cost-effectiveness of algorithms using these tests to screen pregnant women. Observed costs of maternal syphilis screening and treatment using clinic-based RPR and single RSTs in 20 clinics across Peru, Tanzania, and Zambia were used to model the cost-effectiveness of algorithms using combinations of RPR, single, and dual RSTs, and no and mass treatment. Sensitivity analyses determined drivers of key results. Although this analysis found screening using RPR to be relatively cheap, most (>70%) true cases went untreated. Algorithms using single RSTs were the most cost-effective in all observed settings, followed by dual RSTs, which became the most cost-effective if dual RST costs were halved. Single test algorithms dominated most sequential testing algorithms, although sequential algorithms reduced overtreatment. Mass treatment was relatively cheap and effective in the absence of screening supplies, though treated many uninfected women. This analysis highlights the advantages of introducing RSTs in three diverse settings. The results should be applicable to other similar settings. Copyright © 2015 International Federation of Gynecology and Obstetrics. All rights reserved.

The cost-effectiveness of 10 antenatal syphilis screening and treatment approaches in Peru, Tanzania, and Zambia

PubMed Central

Terris-Prestholt, Fern; Vickerman, Peter; Torres-Rueda, Sergio; Santesso, Nancy; Sweeney, Sedona; Mallma, Patricia; Shelley, Katharine D.; Garcia, Patricia J.; Bronzan, Rachel; Gill, Michelle M.; Broutet, Nathalie; Wi, Teodora; Watts, Charlotte; Mabey, David; Peeling, Rosanna W.; Newman, Lori

2015-01-01

Objective Rapid plasma reagin (RPR) is frequently used to test women for maternal syphilis. Rapid syphilis immunochromatographic strip tests detecting only Treponema pallidum antibodies (single RSTs) or both treponemal and non-treponemal antibodies (dual RSTs) are now available. This study assessed the cost-effectiveness of algorithms using these tests to screen pregnant women. Methods Observed costs of maternal syphilis screening and treatment using clinic-based RPR and single RSTs in 20 clinics across Peru, Tanzania, and Zambia were used to model the cost-effectiveness of algorithms using combinations of RPR, single, and dual RSTs, and no and mass treatment. Sensitivity analyses determined drivers of key results. Results Although this analysis found screening using RPR to be relatively cheap, most (> 70%) true cases went untreated. Algorithms using single RSTs were the most cost-effective in all observed settings, followed by dual RSTs, which became the most cost-effective if dual RST costs were halved. Single test algorithms dominated most sequential testing algorithms, although sequential algorithms reduced overtreatment. Mass treatment was relatively cheap and effective in the absence of screening supplies, though treated many uninfected women. Conclusion This analysis highlights the advantages of introducing RSTs in three diverse settings. The results should be applicable to other similar settings. PMID:25963907

Three-step sequential positioning algorithm during sonographic evaluation for appendicitis increases appendiceal visualization rate and reduces CT use.

PubMed

Chang, Stephanie T; Jeffrey, R Brooke; Olcott, Eric W

2014-11-01

The purpose of this article is to examine the rates of appendiceal visualization by sonography, imaging-based diagnoses of appendicitis, and CT use after appendiceal sonography, before and after the introduction of a sonographic algorithm involving sequential changes in patient positioning. We used a search engine to retrospectively identify patients who underwent graded-compression sonography for suspected appendicitis during 6-month periods before (period 1; 419 patients) and after (period 2; 486 patients) implementation of a new three-step positional sonographic algorithm. The new algorithm included initial conventional supine scanning and, as long as the appendix remained nonvisualized, left posterior oblique scanning and then "second-look" supine scanning. Abdominal CT within 7 days after sonography was recorded. Between periods 1 and 2, appendiceal visualization on sonography increased from 31.0% to 52.5% (p < 0.001), postsonography CT use decreased from 31.3% to 17.7% (p < 0.001), and the proportion of imaging-based diagnoses of appendicitis made by sonography increased from 63.8% to 85.7% (p = 0.002). The incidence of appendicitis diagnosed by imaging (either sonography or CT) remained similar at 16.5% and 17.3%, respectively (p = 0.790). Sensitivity and overall accuracy were 57.8% (95% CI, 44.8-70.1%) and 93.0% (95% CI, 90.1-95.3%), respectively, in period 1 and 76.5% (95% CI, 65.8-85.2%) and 95.4% (95% CI, 93.1-97.1%), respectively, in period 2. Similar findings were observed for adults and children. Implementation of an ultrasound algorithm with sequential positioning significantly improved the appendiceal visualization rate and the proportion of imaging-based diagnoses of appendicitis made by ultrasound, enabling a concomitant decrease in abdominal CT use in both children and adults.

Computational Cognitive Neuroscience Modeling of Sequential Skill Learning

DTIC Science & Technology

2016-09-21

101 EAST 27TH STREET STE 4308 AUSTIN , TX 78712 09/21/2016 Final Report DISTRIBUTION A: Distribution approved for public release. Air Force Research ...5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) The University of Texas at Austin 108 E Dean Keeton Stop A8000 Austin , TX ...AFRL-AFOSR-VA-TR-2016-0320 Computational Cognitive Neuroscience Modeling of Sequential Skill Learning David Schnyer UNIVERSITY OF TEXAS AT AUSTIN

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

PubMed

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study.

PubMed

Lyles, Courtney Rees; Godbehere, Andrew; Le, Gem; El Ghaoui, Laurent; Sarkar, Urmimala

2016-06-10

It is difficult to synthesize the vast amount of textual data available from social media websites. Capturing real-world discussions via social media could provide insights into individuals' opinions and the decision-making process. We conducted a sequential mixed methods study to determine the utility of sparse machine learning techniques in summarizing Twitter dialogues. We chose a narrowly defined topic for this approach: cervical cancer discussions over a 6-month time period surrounding a change in Pap smear screening guidelines. We applied statistical methodologies, known as sparse machine learning algorithms, to summarize Twitter messages about cervical cancer before and after the 2012 change in Pap smear screening guidelines by the US Preventive Services Task Force (USPSTF). All messages containing the search terms "cervical cancer," "Pap smear," and "Pap test" were analyzed during: (1) January 1-March 13, 2012, and (2) March 14-June 30, 2012. Topic modeling was used to discern the most common topics from each time period, and determine the singular value criterion for each topic. The results were then qualitatively coded from top 10 relevant topics to determine the efficiency of clustering method in grouping distinct ideas, and how the discussion differed before vs. after the change in guidelines . This machine learning method was effective in grouping the relevant discussion topics about cervical cancer during the respective time periods (~20% overall irrelevant content in both time periods). Qualitative analysis determined that a significant portion of the top discussion topics in the second time period directly reflected the USPSTF guideline change (eg, "New Screening Guidelines for Cervical Cancer"), and many topics in both time periods were addressing basic screening promotion and education (eg, "It is Cervical Cancer Awareness Month! Click the link to see where you can receive a free or low cost Pap test.") It was demonstrated that machine learning tools can be useful in cervical cancer prevention and screening discussions on Twitter. This method allowed us to prove that there is publicly available significant information about cervical cancer screening on social media sites. Moreover, we observed a direct impact of the guideline change within the Twitter messages.

Application of machine learning methods for traffic signs recognition

NASA Astrophysics Data System (ADS)

Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

2018-02-01

This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

Protein classification using sequential pattern mining.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2006-01-01

Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.

Development of high-accuracy convection schemes for sequential solvers

NASA Technical Reports Server (NTRS)

Thakur, Siddharth; Shyy, Wei

1993-01-01

An exploration is conducted of the applicability of such high resolution schemes as TVD to the resolving of sharp flow gradients using a sequential solution approach borrowed from pressure-based algorithms. It is shown that by extending these high-resolution shock-capturing schemes to a sequential solver that treats the equations as a collection of scalar conservation equations, the speed of signal propagation in the solution has to be coordinated by assigning the local convection speed as the characteristic speed for the entire system. A higher amount of dissipation is therefore needed to eliminate oscillations near discontinuities.

Unsteady, one-dimensional gas dynamics computations using a TVD type sequential solver

NASA Technical Reports Server (NTRS)

Thakur, Siddharth; Shyy, Wei

1992-01-01

The efficacy of high resolution convection schemes to resolve sharp gradient in unsteady, 1D flows is examined using the TVD concept based on a sequential solution algorithm. Two unsteady flow problems are considered which include the problem involving the interaction of the various waves in a shock tube with closed reflecting ends and the problem involving the unsteady gas dynamics in a tube with closed ends subject to an initial pressure perturbation. It is concluded that high accuracy convection schemes in a sequential solution framework are capable of resolving discontinuities in unsteady flows involving complex gas dynamics. However, a sufficient amount of dissipation is required to suppress oscillations near discontinuities in the sequential approach, which leads to smearing of the solution profiles.

New Techniques in Numerical Analysis and Their Application to Aerospace Systems.

DTIC Science & Technology

1979-01-01

employment of the sequential gradient-restoration algorithm and the modified quasilineari- zation algorithm in some problems of structural analysis (Refs. 6...and a state inequa - lity constraint. The state inequality constraint is of a special type, namely, it is linear in some or all of the com- ponents of

A study of active learning methods for named entity recognition in clinical text.

PubMed

Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

2015-12-01

Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random sampling, the best uncertainty based method saved 42% annotations in words. But the best diversity based method reduced only 7% annotation effort. In the simulated setting, AL methods, particularly uncertainty-sampling based approaches, seemed to significantly save annotation cost for the clinical NER task. The actual benefit of active learning in clinical NER should be further evaluated in a real-time setting. Copyright © 2015 Elsevier Inc. All rights reserved.

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Exact parallel algorithms for some members of the traveling salesman problem family

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

Pekny, J.F.

1989-01-01

The traveling salesman problem and its many generalizations comprise one of the best known combinatorial optimization problem families. Most members of the family are NP-complete problems so that exact algorithms require an unpredictable and sometimes large computational effort. Parallel computers offer hope for providing the power required to meet these demands. A major barrier to applying parallel computers is the lack of parallel algorithms. The contributions presented in this thesis center around new exact parallel algorithms for the asymmetric traveling salesman problem (ATSP), prize collecting traveling salesman problem (PCTSP), and resource constrained traveling salesman problem (RCTSP). The RCTSP is amore » particularly difficult member of the family since finding a feasible solution is an NP-complete problem. An exact sequential algorithm is also presented for the directed hamiltonian cycle problem (DHCP). The DHCP algorithm is superior to current heuristic approaches and represents the first exact method applicable to large graphs. Computational results presented for each of the algorithms demonstrates the effectiveness of combining efficient algorithms with parallel computing methods. Performance statistics are reported for randomly generated ATSPs with 7,500 cities, PCTSPs with 200 cities, RCTSPs with 200 cities, DHCPs with 3,500 vertices, and assignment problems of size 10,000. Sequential results were collected on a Sun 4/260 engineering workstation, while parallel results were collected using a 14 and 100 processor BBN Butterfly Plus computer. The computational results represent the largest instances ever solved to optimality on any type of computer.« less

Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.

PubMed

Zhou, Zhiguo; Folkert, Michael; Cannon, Nathan; Iyengar, Puneeth; Westover, Kenneth; Zhang, Yuanyuan; Choy, Hak; Timmerman, Robert; Yan, Jingsheng; Xie, Xian-J; Jiang, Steve; Wang, Jing

2016-06-01

The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms. The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Furthermore, to select an optimal clinical parameter set for the model construction, three strategies were adopted: (1) clonal selection algorithm (CSA) based selection strategy; (2) sequential forward selection (SFS) method; and (3) statistical analysis (SA) based strategy. 5-cross-validation is used to validate the performance of each predictive model. The accuracy was assessed by area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the system was also evaluated. The AUCs for ANN, LR and SVM were 0.75, 0.73, and 0.80, respectively. The sensitivity values for ANN, LR and SVM were 71.2%, 72.9% and 83.1%, while the specificity values for ANN, LR and SVM were 59.1%, 63.6% and 63.6%, respectively. Meanwhile, the CSA based strategy outperformed SFS and SA in terms of AUC, sensitivity and specificity. Based on clinical parameters, the SVM with the CSA optimal parameter set selection strategy achieves better performance than other strategies for predicting distant failure in lung SBRT patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

NASA Astrophysics Data System (ADS)

Shi, Yi Fang; Park, Seung Hyo; Song, Taek Lyul

2017-12-01

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality. In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via "supertargets" using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is performed to verify the superiority of the proposed SP-JIPDA algorithm over the MJIPDA in this multistatic passive radar system.

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Algorithms for the Construction of Parallel Tests by Zero-One Programming. Project Psychometric Aspects of Item Banking No. 7. Research Report 86-7.

ERIC Educational Resources Information Center

Boekkooi-Timminga, Ellen

Nine methods for automated test construction are described. All are based on the concepts of information from item response theory. Two general kinds of methods for the construction of parallel tests are presented: (1) sequential test design; and (2) simultaneous test design. Sequential design implies that the tests are constructed one after the…

Algorithms for Large-Scale Astronomical Problems

DTIC Science & Technology

2013-08-01

implemented as a succession of Hadoop MapReduce jobs and sequential programs written in Java . The sampling and splitting stages are implemented as...one MapReduce job, the partitioning and clustering phases make up another job. The merging stage is implemented as a stand-alone Java program. The...Merging. The merging stage is implemented as a sequential Java program that reads the files with the shell information, which were generated by

A Sequential Linear Quadratic Approach for Constrained Nonlinear Optimal Control with Adaptive Time Discretization and Application to Higher Elevation Mars Landing Problem

NASA Astrophysics Data System (ADS)

Sandhu, Amit

A sequential quadratic programming method is proposed for solving nonlinear optimal control problems subject to general path constraints including mixed state-control and state only constraints. The proposed algorithm further develops on the approach proposed in [1] with objective to eliminate the use of a high number of time intervals for arriving at an optimal solution. This is done by introducing an adaptive time discretization to allow formation of a desirable control profile without utilizing a lot of intervals. The use of fewer time intervals reduces the computation time considerably. This algorithm is further used in this thesis to solve a trajectory planning problem for higher elevation Mars landing.

Online sequential Monte Carlo smoother for partially observed diffusion processes

NASA Astrophysics Data System (ADS)

Gloaguen, Pierre; Étienne, Marie-Pierre; Le Corff, Sylvain

2018-12-01

This paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the observations are received, and with a computational complexity growing linearly with the number of Monte Carlo samples. The original algorithm cannot be used in the case of partially observed stochastic differential equations since the transition density of the latent data is usually unknown. We prove that it may be extended to partially observed continuous processes by replacing this unknown quantity by an unbiased estimator obtained for instance using general Poisson estimators. This estimator is proved to be consistent and its performance are illustrated using data from two models.

Sleep and memory consolidation: motor performance and proactive interference effects in sequence learning.

PubMed

Borragán, Guillermo; Urbain, Charline; Schmitz, Rémy; Mary, Alison; Peigneux, Philippe

2015-04-01

That post-training sleep supports the consolidation of sequential motor skills remains debated. Performance improvement and sensitivity to proactive interference are both putative measures of long-term memory consolidation. We tested sleep-dependent memory consolidation for visuo-motor sequence learning using a proactive interference paradigm. Thirty-three young adults were trained on sequence A on Day 1, then had Regular Sleep (RS) or were Sleep Deprived (SD) on the night after learning. After two recovery nights, they were tested on the same sequence A, then had to learn a novel, potentially competing sequence B. We hypothesized that proactive interference effects on sequence B due to the prior learning of sequence A would be higher in the RS condition, considering that proactive interference is an indirect marker of the robustness of sequence A, which should be better consolidated over post-training sleep. Results highlighted sleep-dependent improvement for sequence A, with faster RTs overnight for RS participants only. Moreover, the beneficial impact of sleep was specific to the consolidation of motor but not sequential skills. Proactive interference effects on learning a new material at Day 4 were similar between RS and SD participants. These results suggest that post-training sleep contributes to optimizing motor but not sequential components of performance in visuo-motor sequence learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Efficient Grammar Induction Algorithm with Parse Forests from Real Corpora

NASA Astrophysics Data System (ADS)

Kurihara, Kenichi; Kameya, Yoshitaka; Sato, Taisuke

The task of inducing grammar structures has received a great deal of attention. The reasons why researchers have studied are different; to use grammar induction as the first stage in building large treebanks or to make up better language models. However, grammar induction has inherent computational complexity. To overcome it, some grammar induction algorithms add new production rules incrementally. They refine the grammar while keeping their computational complexity low. In this paper, we propose a new efficient grammar induction algorithm. Although our algorithm is similar to algorithms which learn a grammar incrementally, our algorithm uses the graphical EM algorithm instead of the Inside-Outside algorithm. We report results of learning experiments in terms of learning speeds. The results show that our algorithm learns a grammar in constant time regardless of the size of the grammar. Since our algorithm decreases syntactic ambiguities in each step, our algorithm reduces required time for learning. This constant-time learning considerably affects learning time for larger grammars. We also reports results of evaluation of criteria to choose nonterminals. Our algorithm refines a grammar based on a nonterminal in each step. Since there can be several criteria to decide which nonterminal is the best, we evaluate them by learning experiments.

Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Performance of the architect EBV antibody panel for determination of Epstein-Barr virus infection stage in immunocompetent adolescents and young adults with clinical suspicion of infectious mononucleosis.

PubMed

Guerrero-Ramos, Alvaro; Patel, Mauli; Kadakia, Kinjal; Haque, Tanzina

2014-06-01

The Architect EBV antibody panel is a new chemiluminescence immunoassay system used to determine the stage of Epstein-Barr virus (EBV) infection based on the detection of IgM and IgG antibodies to viral capsid antigen (VCA) and IgG antibodies against Epstein-Barr nuclear antigen 1 (EBNA-1). We evaluated its diagnostic accuracy in immunocompetent adolescents and young adults with clinical suspicion of infectious mononucleosis (IM) using the RecomLine EBV IgM and IgG immunoblots as the reference standard. In addition, the use of the antibody panel in a sequential testing algorithm based on initial EBNA-1 IgG analysis was assessed for cost-effectiveness. Finally, we investigated the degree of cross-reactivity of the VCA IgM marker during other primary viral infections that may present with an EBV IM-like picture. High sensitivity (98.3% [95% confidence interval {CI}, 90.7 to 99.7%]) and specificity (94.2% [95% CI, 87.9 to 97.8%]) were found after testing 162 precharacterized archived serum samples. There was perfect agreement between the use of the antibody panel in sequential and parallel testing algorithms, but substantial cost savings (23%) were obtained with the sequential strategy. A high rate of reactive VCA IgM results was found in primary cytomegalovirus (CMV) infections (60.7%). In summary, the Architect EBV antibody panel performs satisfactorily in the investigation of EBV IM in immunocompetent adolescents and young adults, and the application of an EBNA-1 IgG-based sequential testing algorithm is cost-effective in this diagnostic setting. Concomitant testing for CMV is strongly recommended to aid in the interpretation of EBV serological patterns. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Performance of the Architect EBV Antibody Panel for Determination of Epstein-Barr Virus Infection Stage in Immunocompetent Adolescents and Young Adults with Clinical Suspicion of Infectious Mononucleosis

PubMed Central

Patel, Mauli; Kadakia, Kinjal; Haque, Tanzina

2014-01-01

The Architect EBV antibody panel is a new chemiluminescence immunoassay system used to determine the stage of Epstein-Barr virus (EBV) infection based on the detection of IgM and IgG antibodies to viral capsid antigen (VCA) and IgG antibodies against Epstein-Barr nuclear antigen 1 (EBNA-1). We evaluated its diagnostic accuracy in immunocompetent adolescents and young adults with clinical suspicion of infectious mononucleosis (IM) using the RecomLine EBV IgM and IgG immunoblots as the reference standard. In addition, the use of the antibody panel in a sequential testing algorithm based on initial EBNA-1 IgG analysis was assessed for cost-effectiveness. Finally, we investigated the degree of cross-reactivity of the VCA IgM marker during other primary viral infections that may present with an EBV IM-like picture. High sensitivity (98.3% [95% confidence interval {CI}, 90.7 to 99.7%]) and specificity (94.2% [95% CI, 87.9 to 97.8%]) were found after testing 162 precharacterized archived serum samples. There was perfect agreement between the use of the antibody panel in sequential and parallel testing algorithms, but substantial cost savings (23%) were obtained with the sequential strategy. A high rate of reactive VCA IgM results was found in primary cytomegalovirus (CMV) infections (60.7%). In summary, the Architect EBV antibody panel performs satisfactorily in the investigation of EBV IM in immunocompetent adolescents and young adults, and the application of an EBNA-1 IgG-based sequential testing algorithm is cost-effective in this diagnostic setting. Concomitant testing for CMV is strongly recommended to aid in the interpretation of EBV serological patterns. PMID:24695777

Reed-Solomon decoder

NASA Technical Reports Server (NTRS)

Lahmeyer, Charles R. (Inventor)

1987-01-01

A Reed-Solomon decoder with dedicated hardware for five sequential algorithms was designed with overall pipelining by memory swapping between input, processing and output memories, and internal pipelining through the five algorithms. The code definition used in decoding is specified by a keyword received with each block of data so that a number of different code formats may be decoded by the same hardware.

Sleep to the beat: A nap favours consolidation of timing.

PubMed

Verweij, Ilse M; Onuki, Yoshiyuki; Van Someren, Eus J W; Van der Werf, Ysbrand D

2016-06-01

Growing evidence suggests that sleep is important for procedural learning, but few studies have investigated the effect of sleep on the temporal aspects of motor skill learning. We assessed the effect of a 90-min day-time nap on learning a motor timing task, using 2 adaptations of a serial interception sequence learning (SISL) task. Forty-two right-handed participants performed the task before and after a 90-min period of sleep or wake. Electroencephalography (EEG) was recorded throughout. The motor task consisted of a sequential spatial pattern and was performed according to 2 different timing conditions, that is, either following a sequential or a random temporal pattern. The increase in accuracy was compared between groups using a mixed linear regression model. Within the sleep group, performance improvement was modeled based on sleep characteristics, including spindle- and slow-wave density. The sleep group, but not the wake group, showed improvement in the random temporal, but especially and significantly more strongly in the sequential temporal condition. None of the sleep characteristics predicted improvement on either general of the timing conditions. In conclusion, a daytime nap improves performance on a timing task. We show that performance on the task with a sequential timing sequence benefits more from sleep than motor timing. More important, the temporal sequence did not benefit initial learning, because differences arose only after an offline period and specifically when this period contained sleep. Sleep appears to aid in the extraction of regularities for optimal subsequent performance. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Learning Styles: Fashion Fad or Lever for Change? The Application of Learning Style Theory to Inclusive Curriculum Delivery.

ERIC Educational Resources Information Center

Smith, Jan

2002-01-01

Examines how four classifications of learning styles--field dependence/independence, holistic/sequential, styles linked to the experiential learning cycle, and "deep" and "surface" learning--relate to curriculum values. Draws on research at Sheffield Hallam University into the learning styles of General National Vocational…

Examining Informal Learning in Commercial Airline Pilots' Communities of Practice

ERIC Educational Resources Information Center

Corns, Kevin M.

2014-01-01

A pragmatic sequential mixed methods research methodology was used to examine commercial airline pilots' (N =156) types and frequencies of informal learning activities, perceptions of workplace informal learning, and opinions on how organizations should support workplace informal learning outside of the formal learning environment. This study…

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models.

PubMed

Ferrucci, Filomena; Salza, Pasquale; Sarro, Federica

2017-06-29

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store operations. For this reason, the island model is more suitable for PGAs than the global and grid model, also in terms of costs when executed on a commercial cloud provider.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

Binary tree eigen solver in finite element analysis

NASA Technical Reports Server (NTRS)

Akl, F. A.; Janetzke, D. C.; Kiraly, L. J.

1993-01-01

This paper presents a transputer-based binary tree eigensolver for the solution of the generalized eigenproblem in linear elastic finite element analysis. The algorithm is based on the method of recursive doubling, which parallel implementation of a number of associative operations on an arbitrary set having N elements is of the order of o(log2N), compared to (N-1) steps if implemented sequentially. The hardware used in the implementation of the binary tree consists of 32 transputers. The algorithm is written in OCCAM which is a high-level language developed with the transputers to address parallel programming constructs and to provide the communications between processors. The algorithm can be replicated to match the size of the binary tree transputer network. Parallel and sequential finite element analysis programs have been developed to solve for the set of the least-order eigenpairs using the modified subspace method. The speed-up obtained for a typical analysis problem indicates close agreement with the theoretical prediction given by the method of recursive doubling.

Comparison of Learning Styles of Pharmacy Students and Faculty Members

PubMed Central

Crawford, Stephanie Y.; Alhreish, Suhail K.

2012-01-01

Objectives. To compare dominant learning styles of pharmacy students and faculty members and between faculty members in different tracks. Methods. Gregorc Style Delineator (GSD) and Zubin’s Pharmacists’ Inventory of Learning Styles (PILS) were administered to students and faculty members at an urban, Midwestern college of pharmacy. Results. Based on responses from 299 students (classes of 2008, 2009, and 2010) and 59 faculty members, GSD styles were concrete sequential (48%), abstract sequential (18%), abstract random (13%), concrete random (13%), and multimodal (8%). With PILS, dominant styles were assimilator (47%) and converger (30%). There were no significant differences between faculty members and student learning styles nor across pharmacy student class years (p>0.05). Learning styles differed between men and women across both instruments (p<0.01), and between faculty members in tenure and clinical tracks for the GSD styles (p=0.01). Conclusion. Learning styles differed among respondents based on gender and faculty track. PMID:23275657

SemiBoost: boosting for semi-supervised learning.

PubMed

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

Enhanced transformation of incidentally learned knowledge into explicit memory by dopaminergic modulation.

PubMed

Clos, Mareike; Sommer, Tobias; Schneider, Signe L; Rose, Michael

2018-01-01

During incidental learning statistical regularities are extracted from the environment without the intention to learn. Acquired implicit memory of these regularities can affect behavior in the absence of awareness. However, conscious insight in the underlying regularities can also develop during learning. Such emergence of explicit memory is an important learning mechanism that is assumed to involve prediction errors in the striatum and to be dopamine-dependent. Here we directly tested this hypothesis by manipulating dopamine levels during incidental learning in a modified serial reaction time task (SRTT) featuring a hidden regular sequence of motor responses in a placebo-controlled between-group study. Awareness for the sequential regularity was subsequently assessed using cued generation and additionally verified using free recall. The results demonstrated that dopaminergic modulation nearly doubled the amount of explicit sequence knowledge emerged during learning in comparison to the placebo group. This strong effect clearly argues for a causal role of dopamine-dependent processing for the development of awareness for sequential regularities during learning.

Physics Learning Styles in Higher Education

NASA Astrophysics Data System (ADS)

Loos, Rebecca; Ward, James

2012-03-01

Students in Physics learn in a variety ways depending on backgrounds and interests. This study proposes to evaluate how students in Physics learn using Howard Gardner's Theory of Multiple Intelligences. Physics utilizes numbers, conceptualization of models, observations and visualization skills, and the ability to understand and reflect on specific information. The main objective is to evaluate how Physics students learn specifically using spatial, visual and sequential approaches. This will be assessed by conducting a learning style survey provided by North Carolina State University (NCSU). The survey is completed online by the student after which the results are sent to NCSU. Students will print out the completed survey analysis for further evaluation. The NCSU results categorize students within five of ten learning styles. After the evaluation of Howard Gardner's Theory of Multiple Intelligences and the NCSU definitions of the ten learning styles, the NCSU sensing and visual learning styles will be defined as the Gardener's spatial, visual learning styles. NCSU's sequential learning style will be looked at separately. With the survey results, it can be determined if Physics students fall within the hypothesized learning styles.

Focusing light through random photonic layers by four-element division algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zhang, Xicheng; Zuo, Haoyi; Pang, Lin

2018-02-01

The propagation of waves in turbid media is a fundamental problem of optics with vast applications. Optical phase optimization approaches for focusing light through turbid media using phase control algorithm have been widely studied in recent years due to the rapid development of spatial light modulator. The existing approaches include element-based algorithms - stepwise sequential algorithm, continuous sequential algorithm and whole element optimization approaches - partitioning algorithm, transmission matrix approach and genetic algorithm. The advantage of element-based approaches is that the phase contribution of each element is very clear; however, because the intensity contribution of each element to the focal point is small especially for the case of large number of elements, the determination of the optimal phase for a single element would be difficult. In other words, the signal to noise ratio of the measurement is weak, leading to possibly local maximal during the optimization. As for whole element optimization approaches, all elements are employed for the optimization. Of course, signal to noise ratio during the optimization is improved. However, because more random processings are introduced into the processing, optimizations take more time to converge than the single element based approaches. Based on the advantages of both single element based approaches and whole element optimization approaches, we propose FEDA approach. Comparisons with the existing approaches show that FEDA only takes one third of measurement time to reach the optimization, which means that FEDA is promising in practical application such as for deep tissue imaging.

Noise-enhanced clustering and competitive learning algorithms.

PubMed

Osoba, Osonde; Kosko, Bart

2013-01-01

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation

NASA Astrophysics Data System (ADS)

Tata Hardinata, Jaya; Zarlis, Muhammad; Budhiarti Nababan, Erna; Hartama, Dedy; Sembiring, Rahmat W.

2017-12-01

One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

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

El Naqa, I; Ten, R

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with amore » month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the quantum approach averaged a more optimistic 0.57±0.4, but with high phase fluctuations. Conclusion: Our results demonstrate that quantum machine learning approaches provide a feasible and promising framework for real-time and sequential clinical decision-making in adaptive radiotherapy.« less

Theorising Teaching and Learning: Pre-Service Teachers' Theoretical Awareness of Learning

ERIC Educational Resources Information Center

Brante, Göran; Holmqvist Olander, Mona; Holmquist, Per-Ola; Palla, Marta

2015-01-01

We examine pre-service teachers' theoretical learning during one five-week training module, and their educators' learning about better lecture design to foster student learning. The study is iterative: interventions (one per group) were implemented sequentially in student groups A-C, the results of the previous intervention serving as the baseline…

Multi-Target Tracking via Mixed Integer Optimization

DTIC Science & Technology

2016-05-13

solving these two problems separately, however few algorithms attempt to solve these simultaneously and even fewer utilize optimization. In this paper we...introduce a new mixed integer optimization (MIO) model which solves the data association and trajectory estimation problems simultaneously by minimizing...Kalman filter [5], which updates the trajectory estimates before the algorithm progresses forward to the next scan. This process repeats sequentially

Predicting the survival of diabetes using neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Data mining techniques at the present time are used in predicting diseases of health care industries. Neural Network is one among the prevailing method in data mining techniques of an intelligent field for predicting diseases in health care industries. This paper presents a study on the prediction of the survival of diabetes diseases using different learning algorithms from the supervised learning algorithms of neural network. Three learning algorithms are considered in this study: (i) The levenberg-marquardt learning algorithm (ii) The Bayesian regulation learning algorithm and (iii) The scaled conjugate gradient learning algorithm. The network is trained using the Pima Indian Diabetes Dataset with the help of MATLAB R2014(a) software. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is further computed to validate the accurate prediction

Efficient algorithm for locating and sizing series compensation devices in large power transmission grids: II. Solutions and applications

DOE PAGES

Frolov, Vladimir; Backhaus, Scott; Chertkov, Misha

2014-10-01

In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~ 2700 nodes and ~ 3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polishmore » grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements.« less

Efficient Algorithm for Locating and Sizing Series Compensation Devices in Large Transmission Grids: Solutions and Applications (PART II)

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

Frolov, Vladimir; Backhaus, Scott N.; Chertkov, Michael

2014-01-14

In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~2700 nodes and ~3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid ismore » used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements« less

Deferred discrimination algorithm (nibbling) for target filter management

NASA Astrophysics Data System (ADS)

Caulfield, H. John; Johnson, John L.

1999-07-01

A new method of classifying objects is presented. Rather than trying to form the classifier in one step or in one training algorithm, it is done in a series of small steps, or nibbles. This leads to an efficient and versatile system that is trained in series with single one-shot examples but applied in parallel, is implemented with single layer perceptrons, yet maintains its fully sequential hierarchical structure. Based on the nibbling algorithm, a basic new method of target reference filter management is described.

Learning style and teaching method preferences of Saudi students of physical therapy

PubMed Central

Al Maghraby, Mohamed A.; Alshami, Ali M.

2013-01-01

Context: To the researchers’ knowledge, there are no published studies that have investigated the learning styles and preferred teaching methods of physical therapy students in Saudi Arabia. Aim: The study was conducted to determine the learning styles and preferred teaching methods of Saudi physical therapy students. Settings and Design: A cross-sectional study design. Materials and Methods: Fifty-three Saudis studying physical therapy (21 males and 32 females) participated in the study. The principal researcher gave an introductory lecture to explain the different learning styles and common teaching methods. Upon completion of the lecture, questionnaires were distributed, and were collected on completion. Statistical Analysis Used: Percentages were calculated for the learning styles and teaching methods. Pearson’s correlations were performed to investigate the relationship between them. Results: More than 45 (85%) of the students rated hands-on training as the most preferred teaching method. Approximately 30 (57%) students rated the following teaching methods as the most preferred methods: “Advanced organizers,” “demonstrations,” and “multimedia activities.” Although 31 (59%) students rated the concrete-sequential learning style the most preferred, these students demonstrated mixed styles on the other style dimensions: Abstract-sequential, abstract-random, and concrete-random. Conclusions: The predominant concrete-sequential learning style is consistent with the most preferred teaching method (hands-on training). The high percentage of physical therapy students whose responses were indicative of mixed learning styles suggests that they can accommodate multiple teaching methods. It is recommended that educators consider the diverse learning styles of the students and utilize a variety of teaching methods in order to promote an optimal learning environment for the students. PMID:24672278

Two-IMU FDI performance of the sequential probability ratio test during shuttle entry

NASA Technical Reports Server (NTRS)

Rich, T. M.

1976-01-01

Performance data for the sequential probability ratio test (SPRT) during shuttle entry are presented. Current modeling constants and failure thresholds are included for the full mission 3B from entry through landing trajectory. Minimum 100 percent detection/isolation failure levels and a discussion of the effects of failure direction are presented. Finally, a limited comparison of failures introduced at trajectory initiation shows that the SPRT algorithm performs slightly worse than the data tracking test.

Puzzles in modern biology. V. Why are genomes overwired?

PubMed

Frank, Steven A

2017-01-01

Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks.

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.

PubMed

Chen, Peng; Li, Jinyan; Wong, Limsoon; Kuwahara, Hiroyuki; Huang, Jianhua Z; Gao, Xin

2013-08-01

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Copyright © 2013 Wiley Periodicals, Inc.

Using Mobile Devices to Enhance the Interactive Learning for Spatial Geometry

ERIC Educational Resources Information Center

Chang, Kuo-En; Wu, Lin-Jung; Lai, Shing-Chuang; Sung, Yao-Ting

2016-01-01

The purpose of this research is to develop a hands-on spatial geometry learning system to facilitate the learning of geometry. The development of this system was based on Duval's four critical elements of geometric learning: perceptual apprehension, sequential apprehension, operative apprehension, and discursive apprehension. The system offers…

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme.

PubMed

Tian, Xiumei; Zeng, Dong; Zhang, Shanli; Huang, Jing; Zhang, Hua; He, Ji; Lu, Lijun; Xi, Weiwen; Ma, Jianhua; Bian, Zhaoying

2016-11-22

Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance

ERIC Educational Resources Information Center

Grivokostopoulou, Foteini; Perikos, Isidoros; Hatzilygeroudis, Ioannis

2017-01-01

In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active…

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

PubMed

Ergen, Tolga; Kozat, Suleyman Serdar

2017-09-13

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

Episodic Contributions to Sequential Control: Learning from a Typist's Touch

ERIC Educational Resources Information Center

Crump, Matthew J. C.; Logan, Gordon D.

2010-01-01

Sequential control over routine action is widely assumed to be controlled by stable, highly practiced representations. Our findings demonstrate that the processes controlling routine actions in the domain of skilled typing can be flexibly manipulated by memory processes coding recent experience with typing particular words and letters. In two…

Safeguarding a Lunar Rover with Wald's Sequential Probability Ratio Test

NASA Technical Reports Server (NTRS)

Furlong, Michael; Dille, Michael; Wong, Uland; Nefian, Ara

2016-01-01

The virtual bumper is a safeguarding mechanism for autonomous and remotely operated robots. In this paper we take a new approach to the virtual bumper system by using an old statistical test. By using a modified version of Wald's sequential probability ratio test we demonstrate that we can reduce the number of false positive reported by the virtual bumper, thereby saving valuable mission time. We use the concept of sequential probability ratio to control vehicle speed in the presence of possible obstacles in order to increase certainty about whether or not obstacles are present. Our new algorithm reduces the chances of collision by approximately 98 relative to traditional virtual bumper safeguarding without speed control.

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Mobile Guide System Using Problem-Solving Strategy for Museum Learning: A Sequential Learning Behavioural Pattern Analysis

ERIC Educational Resources Information Center

Sung, Y.-T.; Hou, H.-T.; Liu, C.-K.; Chang, K.-E.

2010-01-01

Mobile devices have been increasingly utilized in informal learning because of their high degree of portability; mobile guide systems (or electronic guidebooks) have also been adopted in museum learning, including those that combine learning strategies and the general audio-visual guide systems. To gain a deeper understanding of the features and…

Parallel consistent labeling algorithms

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

Samal, A.; Henderson, T.

Mackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms. Mohr and Henderson have given new algorithms, AC-4 and PC-3, for arc and path consistency, respectively, and have shown that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. In this paper, they give parallel algorithms for solving node and arc consistency. They show that any parallel algorithm for enforcing arc consistency in the worst case must have O(na) sequential steps, where n is number of nodes, and a is the number of labels per node.more » They give several parallel algorithms to do arc consistency. It is also shown that they all have optimal time complexity. The results of running the parallel algorithms on a BBN Butterfly multiprocessor are also presented.« less

Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

NASA Astrophysics Data System (ADS)

Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

1995-02-01

We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

Learning control system design based on 2-D theory - An application to parallel link manipulator

NASA Technical Reports Server (NTRS)

Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

1990-01-01

An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

Mache: No-Loss Trace Compaction

DTIC Science & Technology

1988-09-15

Data Compression . IEEE Computer 176 (June 1984), 8-19. 10. ZIV , J. AND LEMPEL , A. A Universal Algorithm for Sequential Data Com- pression. IEEE... compression scheme which takes ad- vantage of repeating patterns in the sequence of bytes. I have used the Lempel - Ziv compression algorithm [9,10,11...Transactions on Information Theory 23 (1976), 75-81. 11. ZIV , J. AND LEMPEL , A. Compression of Individual Sequences via Variable-

The Learning Preferences among Nursing Students in the King Saud University in Saudi Arabia: A Cross-Sectional Survey

PubMed Central

Almutairi, Adel F.; Alhelih, Eyad M.; Alshehry, Abdualrahman S.

2017-01-01

Objective The present study aimed to identify the most common learning preferences among the nursing students in Saudi Arabia and to investigate the associations of certain demographic variables with the learning preferences. Methods All the undergraduate nursing students in the nursing college were requested to participate in this descriptive cross-sectional study. An Arabic version of the Felder-Silverman learning style model (FSLSM) questionnaire was used to examine the learning preferences among undergraduate nursing students. Results A total of 56 (43%) completed questionnaires were included in the final analysis. Results of the present study indicate that the most common learning preferences among the nursing students were visual (67.9%), followed by active (50%) and sequential (37.5%) learning preferences. The verbal style was the least common learning preference (3.6%) among the nursing students. There was no association between gender and learning preferences (p > .05). Conclusion The present study concluded that the visual, active, and sequential styles are the commonest learning preferences among the nursing students. The nursing educators should emphasize the use of this information in their teaching methods to improve learning skills among the nursing students. PMID:28630767

A high level language for a high performance computer

NASA Technical Reports Server (NTRS)

Perrott, R. H.

1978-01-01

The proposed computational aerodynamic facility will join the ranks of the supercomputers due to its architecture and increased execution speed. At present, the languages used to program these supercomputers have been modifications of programming languages which were designed many years ago for sequential machines. A new programming language should be developed based on the techniques which have proved valuable for sequential programming languages and incorporating the algorithmic techniques required for these supercomputers. The design objectives for such a language are outlined.

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation

NASA Astrophysics Data System (ADS)

Gorpas, D.; Yova, D.

2009-07-01

One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest.

Developing and Implementing a Framework of Participatory Simulation for Mobile Learning Using Scaffolding

ERIC Educational Resources Information Center

Yin, Chengjiu; Song, Yanjie; Tabata, Yoshiyuki; Ogata, Hiroaki; Hwang, Gwo-Jen

2013-01-01

This paper proposes a conceptual framework, scaffolding participatory simulation for mobile learning (SPSML), used on mobile devices for helping students learn conceptual knowledge in the classroom. As the pedagogical design, the framework adopts an experiential learning model, which consists of five sequential but cyclic steps: the initial stage,…

Psychological Principles in Materials Selection.

ERIC Educational Resources Information Center

Colvin, Cynthia M.

Those psychological principles which might aid the teacher in the selection of instructional materials are examined. Since learning is a process which builds sequentially on past learning, beginning reading materials should include words that have personal relevance for the individual child. Meaningful material is learned more quickly than…

Sequential drug treatment algorithm for agitation and aggression in Alzheimer's and mixed dementia.

PubMed

Davies, Simon Jc; Burhan, Amer M; Kim, Donna; Gerretsen, Philip; Graff-Guerrero, Ariel; Woo, Vincent L; Kumar, Sanjeev; Colman, Sarah; Pollock, Bruce G; Mulsant, Benoit H; Rajji, Tarek K

2018-05-01

Behavioural and psychological symptoms of dementia (BPSD) include agitation and aggression in people with dementia. BPSD is common on inpatient psychogeriatric units and may prevent individuals from living at home or in residential/nursing home settings. Several drugs and non-pharmacological treatments have been shown to be effective in reducing behavioural and psychological symptoms of dementia. Algorithmic treatment may address the challenge of synthesizing this evidence-based knowledge. A multidisciplinary team created evidence-based algorithms for the treatment of behavioural and psychological symptoms of dementia. We present drug treatment algorithms for agitation and aggression associated with Alzheimer's and mixed Alzheimer's/vascular dementia. Drugs were appraised by psychiatrists based on strength of evidence of efficacy, time to onset of clinical effect, tolerability, ease of use, and efficacy for indications other than behavioural and psychological symptoms of dementia. After baseline assessment and discontinuation of potentially exacerbating medications, sequential trials are recommended with risperidone, aripiprazole or quetiapine, carbamazepine, citalopram, gabapentin, and prazosin. Titration schedules are proposed, with adjustments for frailty. Additional guidance is given on use of electroconvulsive therapy, optimization of existing cholinesterase inhibitors/memantine, and use of pro re nata medications. This algorithm-based approach for drug treatment of agitation/aggression in Alzheimer's/mixed dementia has been implemented in several Canadian Hospital Inpatient Units. Impact should be assessed in future research.

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

NASA Astrophysics Data System (ADS)

Maes, Pieter-Jan; Amelynck, Denis; Leman, Marc

2012-12-01

In this article, a computational platform is presented, entitled "Dance-the-Music", that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers' models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method-can determine the quality of a student's performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures.

A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

NASA Astrophysics Data System (ADS)

Obulesu, O.; Rama Mohan Reddy, A., Dr; Mahendra, M.

2017-08-01

Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Active Learning Using Hint Information.

PubMed

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study

PubMed Central

Godbehere, Andrew; Le, Gem; El Ghaoui, Laurent; Sarkar, Urmimala

2016-01-01

Background It is difficult to synthesize the vast amount of textual data available from social media websites. Capturing real-world discussions via social media could provide insights into individuals’ opinions and the decision-making process. Objective We conducted a sequential mixed methods study to determine the utility of sparse machine learning techniques in summarizing Twitter dialogues. We chose a narrowly defined topic for this approach: cervical cancer discussions over a 6-month time period surrounding a change in Pap smear screening guidelines. Methods We applied statistical methodologies, known as sparse machine learning algorithms, to summarize Twitter messages about cervical cancer before and after the 2012 change in Pap smear screening guidelines by the US Preventive Services Task Force (USPSTF). All messages containing the search terms “cervical cancer,” “Pap smear,” and “Pap test” were analyzed during: (1) January 1–March 13, 2012, and (2) March 14–June 30, 2012. Topic modeling was used to discern the most common topics from each time period, and determine the singular value criterion for each topic. The results were then qualitatively coded from top 10 relevant topics to determine the efficiency of clustering method in grouping distinct ideas, and how the discussion differed before vs. after the change in guidelines . Results This machine learning method was effective in grouping the relevant discussion topics about cervical cancer during the respective time periods (~20% overall irrelevant content in both time periods). Qualitative analysis determined that a significant portion of the top discussion topics in the second time period directly reflected the USPSTF guideline change (eg, “New Screening Guidelines for Cervical Cancer”), and many topics in both time periods were addressing basic screening promotion and education (eg, “It is Cervical Cancer Awareness Month! Click the link to see where you can receive a free or low cost Pap test.”) Conclusions It was demonstrated that machine learning tools can be useful in cervical cancer prevention and screening discussions on Twitter. This method allowed us to prove that there is publicly available significant information about cervical cancer screening on social media sites. Moreover, we observed a direct impact of the guideline change within the Twitter messages. PMID:27288093

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Iterative non-sequential protein structural alignment.

PubMed

Salem, Saeed; Zaki, Mohammed J; Bystroff, Christopher

2009-06-01

Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence. We propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of SNAP alignments were assessed by comparing against the manually curated reference alignments in the challenging SISY and RIPC datasets. Moreover, when applied to a dataset of 4410 protein pairs selected from the CATH database, SNAP produced longer alignments with lower rmsd than several state-of-the-art alignment methods. Classification of folds using SNAP alignments was both highly sensitive and highly selective. The SNAP software along with the datasets are available online at http://www.cs.rpi.edu/~zaki/software/SNAP.

Sequentially reweighted TV minimization for CT metal artifact reduction.

PubMed

Zhang, Xiaomeng; Xing, Lei

2013-07-01

Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions. A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach. Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties. The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

Teaching Improvisation through Melody and Blues-Based Harmony: A Comprehensive and Sequential Approach

ERIC Educational Resources Information Center

Heil, Leila

2017-01-01

This article describes a sequential approach to improvisation teaching that can be used with students at various age and ability levels by any educator, regardless of improvisation experience. The 2014 National Core Music Standards include improvisation as a central component in musical learning and promote instructional approaches that are…

A developmental approach to learning causal models for cyber security

NASA Astrophysics Data System (ADS)

Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence Analysis.

PubMed

Wei, Qinglai; Lewis, Frank L; Sun, Qiuye; Yan, Pengfei; Song, Ruizhuo

2017-05-01

In this paper, a novel discrete-time deterministic Q -learning algorithm is developed. In each iteration of the developed Q -learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q -learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function converges to the optimum, where the convergence criterion of the learning rates for traditional Q -learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative Q function are analyzed to obtain the convergence criterion, instead of analyzing the iterative Q function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic Q -learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic Q -learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

The science of computing - Parallel computation

NASA Technical Reports Server (NTRS)

Denning, P. J.

1985-01-01

Although parallel computation architectures have been known for computers since the 1920s, it was only in the 1970s that microelectronic components technologies advanced to the point where it became feasible to incorporate multiple processors in one machine. Concommitantly, the development of algorithms for parallel processing also lagged due to hardware limitations. The speed of computing with solid-state chips is limited by gate switching delays. The physical limit implies that a 1 Gflop operational speed is the maximum for sequential processors. A computer recently introduced features a 'hypercube' architecture with 128 processors connected in networks at 5, 6 or 7 points per grid, depending on the design choice. Its computing speed rivals that of supercomputers, but at a fraction of the cost. The added speed with less hardware is due to parallel processing, which utilizes algorithms representing different parts of an equation that can be broken into simpler statements and processed simultaneously. Present, highly developed computer languages like FORTRAN, PASCAL, COBOL, etc., rely on sequential instructions. Thus, increased emphasis will now be directed at parallel processing algorithms to exploit the new architectures.

Enhancing battery efficiency for pervasive health-monitoring systems based on electronic textiles.

PubMed

Zheng, Nenggan; Wu, Zhaohui; Lin, Man; Yang, Laurence Tianruo

2010-03-01

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.

Performance Trend of Different Algorithms for Structural Design Optimization

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG

PubMed Central

Lee, Kwang Jin; Lee, Boreom

2016-01-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR. PMID:27376296

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG.

PubMed

Lee, Kwang Jin; Lee, Boreom

2016-07-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR.

Effects of neostriatal 6-OHDA lesion on performance in a rat sequential reaction time task.

PubMed

Domenger, D; Schwarting, R K W

2008-10-31

Work in humans and monkeys has provided evidence that the basal ganglia, and the neurotransmitter dopamine therein, play an important role for sequential learning and performance. Compared to primates, experimental work in rodents is rather sparse, largely due to the fact that tasks comparable to the human ones, especially serial reaction time tasks (SRTT), had been lacking until recently. We have developed a rat model of the SRTT, which allows to study neural correlates of sequential performance and motor sequence execution. Here, we report the effects of dopaminergic neostriatal lesions, performed using bilateral 6-hydroxydopamine injections, on performance of well-trained rats tested in our SRTT. Sequential behavior was measured in two ways: for one, the effects of small violations of otherwise well trained sequences were examined as a measure of attention and automation. Secondly, sequential versus random performance was compared as a measure of sequential learning. Neurochemically, the lesions led to sub-total dopamine depletions in the neostriatum, which ranged around 60% in the lateral, and around 40% in the medial neostriatum. These lesions led to a general instrumental impairment in terms of reduced speed (response latencies) and response rate, and these deficits were correlated with the degree of striatal dopamine loss. Furthermore, the violation test indicated that the lesion group conducted less automated responses. The comparison of random versus sequential responding showed that the lesion group did not retain its superior sequential performance in terms of speed, whereas they did in terms of accuracy. Also, rats with lesions did not improve further in overall performance as compared to pre-lesion values, whereas controls did. These results support previous results that neostriatal dopamine is involved in instrumental behaviour in general. Also, these lesions are not sufficient to completely abolish sequential performance, at least when acquired before lesion as tested here.

A Bayesian sequential design using alpha spending function to control type I error.

PubMed

Zhu, Han; Yu, Qingzhao

2017-10-01

We propose in this article a Bayesian sequential design using alpha spending functions to control the overall type I error in phase III clinical trials. We provide algorithms to calculate critical values, power, and sample sizes for the proposed design. Sensitivity analysis is implemented to check the effects from different prior distributions, and conservative priors are recommended. We compare the power and actual sample sizes of the proposed Bayesian sequential design with different alpha spending functions through simulations. We also compare the power of the proposed method with frequentist sequential design using the same alpha spending function. Simulations show that, at the same sample size, the proposed method provides larger power than the corresponding frequentist sequential design. It also has larger power than traditional Bayesian sequential design which sets equal critical values for all interim analyses. When compared with other alpha spending functions, O'Brien-Fleming alpha spending function has the largest power and is the most conservative in terms that at the same sample size, the null hypothesis is the least likely to be rejected at early stage of clinical trials. And finally, we show that adding a step of stop for futility in the Bayesian sequential design can reduce the overall type I error and reduce the actual sample sizes.

Teacher's Guide: Social Studies, 10.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

This teacher's guide, part of a sequential K-12 series, provides objectives and learning activities for social studies students in grade 10. Five major sections focus on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical…

A-Track: Detecting Moving Objects in FITS images

NASA Astrophysics Data System (ADS)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2017-04-01

A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

A study on the performance comparison of metaheuristic algorithms on the learning of neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2017-08-01

The learning or training process of neural networks entails the task of finding the most optimal set of parameters, which includes translation vectors, dilation parameter, synaptic weights, and bias terms. Apart from the traditional gradient descent-based methods, metaheuristic methods can also be used for this learning purpose. Since the inception of genetic algorithm half a century ago, the last decade witnessed the explosion of a variety of novel metaheuristic algorithms, such as harmony search algorithm, bat algorithm, and whale optimization algorithm. Despite the proof of the no free lunch theorem in the discipline of optimization, a survey in the literature of machine learning gives contrasting results. Some researchers report that certain metaheuristic algorithms are superior to the others, whereas some others argue that different metaheuristic algorithms give comparable performance. As such, this paper aims to investigate if a certain metaheuristic algorithm will outperform the other algorithms. In this work, three metaheuristic algorithms, namely genetic algorithms, particle swarm optimization, and harmony search algorithm are considered. The algorithms are incorporated in the learning of neural networks and their classification results on the benchmark UCI machine learning data sets are compared. It is found that all three metaheuristic algorithms give similar and comparable performance, as captured in the average overall classification accuracy. The results corroborate the findings reported in the works done by previous researchers. Several recommendations are given, which include the need of statistical analysis to verify the results and further theoretical works to support the obtained empirical results.

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

PubMed

González-Recio, O; Jiménez-Montero, J A; Alenda, R

2013-01-01

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy and bias. This modification may be used to speed the calculus of genome-assisted evaluation in large data sets such us those obtained from consortiums. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A Stabilized Sparse-Matrix U-D Square-Root Implementation of a Large-State Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Boggs, D.; Ghil, M.; Keppenne, C.

1995-01-01

The full nonlinear Kalman filter sequential algorithm is, in theory, well-suited to the four-dimensional data assimilation problem in large-scale atmospheric and oceanic problems. However, it was later discovered that this algorithm can be very sensitive to computer roundoff, and that results may cease to be meaningful as time advances. Implementations of a modified Kalman filter are given.

Recurrent procedure for constructing nonisotropic matrix elements of the collision integral of the nonlinear Boltzmann equation

NASA Astrophysics Data System (ADS)

Ender, I. A.; Bakaleinikov, L. A.; Flegontova, E. Yu.; Gerasimenko, A. B.

2017-08-01

We have proposed an algorithm for the sequential construction of nonisotropic matrix elements of the collision integral, which are required to solve the nonlinear Boltzmann equation using the moments method. The starting elements of the matrix are isotropic and assumed to be known. The algorithm can be used for an arbitrary law of interactions for any ratio of the masses of colliding particles.

An improved optimization algorithm and Bayes factor termination criterion for sequential projection pursuit

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

Webb-Robertson, Bobbie-Jo M.; Jarman, Kristin H.; Harvey, Scott D.

2005-05-28

A fundamental problem in analysis of highly multivariate spectral or chromatographic data is reduction of dimensionality. Principal components analysis (PCA), concerned with explaining the variance-covariance structure of the data, is a commonly used approach to dimension reduction. Recently an attractive alternative to PCA, sequential projection pursuit (SPP), has been introduced. Designed to elicit clustering tendencies in the data, SPP may be more appropriate when performing clustering or classification analysis. However, the existing genetic algorithm (GA) implementation of SPP has two shortcomings, computation time and inability to determine the number of factors necessary to explain the majority of the structure inmore » the data. We address both these shortcomings. First, we introduce a new SPP algorithm, a random scan sampling algorithm (RSSA), that significantly reduces computation time. We compare the computational burden of the RSS and GA implementation for SPP on a dataset containing Raman spectra of twelve organic compounds. Second, we propose a Bayes factor criterion, BFC, as an effective measure for selecting the number of factors needed to explain the majority of the structure in the data. We compare SPP to PCA on two datasets varying in type, size, and difficulty; in both cases SPP achieves a higher accuracy with a lower number of latent variables.« less

On the Modularity of Implicit Sequence Learning: Independent Acquisition of Spatial, Symbolic, and Manual Sequences

ERIC Educational Resources Information Center

Goschke, Thomas; Bolte, Annette

2012-01-01

Learning sequential structures is of fundamental importance for a wide variety of human skills. While it has long been debated whether implicit sequence learning is perceptual or response-based, here we propose an alternative framework that cuts across this dichotomy and assumes that sequence learning rests on associative changes that can occur…

Executive function skills and academic achievement gains in prekindergarten: Contributions of learning-related behaviors.

PubMed

Nesbitt, Kimberly Turner; Farran, Dale Clark; Fuhs, Mary Wagner

2015-07-01

Although research suggests associations between children's executive function skills and their academic achievement, the specific mechanisms that may help explain these associations in early childhood are unclear. This study examined whether children's (N = 1,103; M age = 54.5 months) executive function skills at the beginning of prekindergarten (pre-K) predict their learning-related behaviors in the classroom and whether these behaviors then mediate associations between children's executive function skills and their pre-K literacy, language, and mathematic gains. Learning-related behaviors were quantified in terms of (a) higher levels of involvement in learning opportunities; (b) greater frequency of participation in activities that require sequential steps; (c) more participation in social-learning interactions; and (d) less instances of being unoccupied, disruptive, or in time out. Results indicated that children's learning-related behaviors mediated associations between executive function skills and literacy and mathematics gains through children's level of involvement, sequential learning behaviors, and disengagement from the classroom. The implications of the findings for early childhood education are discussed. (c) 2015 APA, all rights reserved).

Implicit transfer of reversed temporal structure in visuomotor sequence learning.

PubMed

Tanaka, Kanji; Watanabe, Katsumi

2014-04-01

Some spatio-temporal structures are easier to transfer implicitly in sequential learning. In this study, we investigated whether the consistent reversal of triads of learned components would support the implicit transfer of their temporal structure in visuomotor sequence learning. A triad comprised three sequential button presses ([1][2][3]) and seven consecutive triads comprised a sequence. Participants learned sequences by trial and error, until they could complete it 20 times without error. Then, they learned another sequence, in which each triad was reversed ([3][2][1]), partially reversed ([2][1][3]), or switched so as not to overlap with the other conditions ([2][3][1] or [3][1][2]). Even when the participants did not notice the alternation rule, the consistent reversal of the temporal structure of each triad led to better implicit transfer; this was confirmed in a subsequent experiment. These results suggest that the implicit transfer of the temporal structure of a learned sequence can be influenced by both the structure and consistency of the change. Copyright © 2013 Cognitive Science Society, Inc.

Suppressing correlations in massively parallel simulations of lattice models

NASA Astrophysics Data System (ADS)

Kelling, Jeffrey; Ódor, Géza; Gemming, Sibylle

2017-11-01

For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems and long simulation time feasible, while sequential simulations remain the gold-standard for correlation-free dynamics. Here, various domain decomposition schemes are compared, concluding with one which delivers virtually correlation-free simulations on GPUs. Extensive simulations of the octahedron model for 2 + 1 dimensional Kardar-Parisi-Zhang surface growth, which is very sensitive to correlation in the site-selection dynamics, were performed to show self-consistency of the parallel runs and agreement with the sequential algorithm. We present a GPU implementation providing a speedup of about 30 × over a parallel CPU implementation on a single socket and at least 180 × with respect to the sequential reference.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

On-Line Algorithms and Reverse Mathematics

NASA Astrophysics Data System (ADS)

Harris, Seth

In this thesis, we classify the reverse-mathematical strength of sequential problems. If we are given a problem P of the form ∀X(alpha(X) → ∃Zbeta(X,Z)) then the corresponding sequential problem, SeqP, asserts the existence of infinitely many solutions to P: ∀X(∀nalpha(Xn) → ∃Z∀nbeta(X n,Zn)). P is typically provable in RCA0 if all objects involved are finite. SeqP, however, is only guaranteed to be provable in ACA0. In this thesis we exactly characterize which sequential problems are equivalent to RCA0, WKL0, or ACA0.. We say that a problem P is solvable by an on-line algorithm if P can be solved according to a two-player game, played by Alice and Bob, in which Bob has a winning strategy. Bob wins the game if Alice's sequence of plays 〈a0, ..., ak〉 and Bob's sequence of responses 〈 b0, ..., bk〉 constitute a solution to P. Formally, an on-line algorithm A is a function that inputs an admissible sequence of plays 〈a 0, b0, ..., aj〉 and outputs a new play bj for Bob. (This differs from the typical definition of "algorithm", though quite often a concrete set of instructions can be easily deduced from A.). We show that SeqP is provable in RCA0 precisely when P is solvable by an on-line algorithm. Schmerl proved this result specifically for the graph coloring problem; we generalize Schmerl's result to any problem that is on-line solvable. To prove our separation, we introduce a principle called Predictk(r) that is equivalent to -WKL0 for standard k, r.. We show that WKL0 is sufficient to prove SeqP precisely when P has a solvable closed kernel. This means that a solution exists, and each initial segment of this solution is a solution to the corresponding initial segment of the problem. (Certain bounding conditions are necessary as well.) If no such solution exists, then SeqP is equivalent to ACA0 over RCA 0 + ISigma02; RCA0 alone suffices if only sequences of standard length are considered. We use different techniques from Schmerl to prove this separation, and in the process we improve some of Schmerl's results on Grundy colorings. In Chapter 4 we analyze a variety of applications, classifying their sequential forms by reverse-mathematical strength. This builds upon similar work by Dorais and Hirst and Mummert. We consider combinatorial applications such as matching problems and Dilworth's theorems, and we also consider classic algorithms such as the task scheduling and paging problems. Tables summarizing our findings can be found at the end of Chapter 4.

Learning Orthographic Structure with Sequential Generative Neural Networks

ERIC Educational Resources Information Center

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-01-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

Estimating Soil Moisture Using Polsar Data: a Machine Learning Approach

NASA Astrophysics Data System (ADS)

Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.

2017-09-01

Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.

Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

ERIC Educational Resources Information Center

Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

2008-01-01

The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-01-01

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

RACE/A: An Architectural Account of the Interactions between Learning, Task Control, and Retrieval Dynamics

ERIC Educational Resources Information Center

van Maanen, Leendert; van Rijn, Hedderik; Taatgen, Niels

2012-01-01

This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use…

Sequential Pattern Analysis: Method and Application in Exploring How Students Develop Concept Maps

ERIC Educational Resources Information Center

Chiu, Chiung-Hui; Lin, Chien-Liang

2012-01-01

Concept mapping is a technique that represents knowledge in graphs. It has been widely adopted in science education and cognitive psychology to aid learning and assessment. To realize the sequential manner in which students develop concept maps, most research relies upon human-dependent, qualitative approaches. This article proposes a method for…

Technology: Digital Photography in an Inner-City Fifth Grade, Part 1

ERIC Educational Resources Information Center

Riner, Phil

2005-01-01

Research tells us we can learn complex tasks most easily if they are taught in "small sequential steps." This column is about the small sequential steps that unlocked the powers of digital photography, of portraiture, and of student creativity. The strategies and ideas described in this article came as a result of working with…

Harmonizing and Improvising in the Choral Rehearsal: A Sequential Approach

ERIC Educational Resources Information Center

Bell, Cindy L.

2004-01-01

This article challenges choral teachers to motivate their choirs to a new level of choral singing and harmonic creativity and outlines a sequential process for introducing improvisation into the daily warm-up. It argues that students can learn to harmonize and improvise by ear as part of each day's warm-up period. Sections include: (1) Chord…

Lexical and Grammatical Associations in Sequential Bilingual Preschoolers

ERIC Educational Resources Information Center

Kohnert, Kathryn; Kan, Pui Fong; Conboy, Barbara T.

2010-01-01

Purpose: The authors investigated potential relationships between traditional linguistic domains (words, grammar) in the first (L1) and second (L2) languages of young sequential bilingual preschool children. Method: Participants were 19 children, ages 2;11 (years;months) to 5;2 (M = 4;3) who began learning Hmong as the L1 from birth and English as…

Segmentation of remotely sensed data using parallel region growing

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Cox, S. C.

1983-01-01

The improved spatial resolution of the new earth resources satellites will increase the need for effective utilization of spatial information in machine processing of remotely sensed data. One promising technique is scene segmentation by region growing. Region growing can use spatial information in two ways: only spatially adjacent regions merge together, and merging criteria can be based on region-wide spatial features. A simple region growing approach is described in which the similarity criterion is based on region mean and variance (a simple spatial feature). An effective way to implement region growing for remote sensing is as an iterative parallel process on a large parallel processor. A straightforward parallel pixel-based implementation of the algorithm is explored and its efficiency is compared with sequential pixel-based, sequential region-based, and parallel region-based implementations. Experimental results from on aircraft scanner data set are presented, as is a discussioon of proposed improvements to the segmentation algorithm.

Sequential quadratic programming-based fast path planning algorithm subject to no-fly zone constraints

NASA Astrophysics Data System (ADS)

Liu, Wei; Ma, Shunjian; Sun, Mingwei; Yi, Haidong; Wang, Zenghui; Chen, Zengqiang

2016-08-01

Path planning plays an important role in aircraft guided systems. Multiple no-fly zones in the flight area make path planning a constrained nonlinear optimization problem. It is necessary to obtain a feasible optimal solution in real time. In this article, the flight path is specified to be composed of alternate line segments and circular arcs, in order to reformulate the problem into a static optimization one in terms of the waypoints. For the commonly used circular and polygonal no-fly zones, geometric conditions are established to determine whether or not the path intersects with them, and these can be readily programmed. Then, the original problem is transformed into a form that can be solved by the sequential quadratic programming method. The solution can be obtained quickly using the Sparse Nonlinear OPTimizer (SNOPT) package. Mathematical simulations are used to verify the effectiveness and rapidity of the proposed algorithm.

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations

PubMed Central

Ho, ThienLuan; Oh, Seung-Rohk

2017-01-01

Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs). In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively. PMID:29016700

Active learning for clinical text classification: is it better than random sampling?

PubMed

Figueroa, Rosa L; Zeng-Treitler, Qing; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.

Active learning for clinical text classification: is it better than random sampling?

PubMed Central

Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics.

PubMed

Awan, Muaaz Gul; Saeed, Fahad

2017-08-01

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS) . The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Guided particle swarm optimization method to solve general nonlinear optimization problems

NASA Astrophysics Data System (ADS)

Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr

2018-04-01

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Treating convection in sequential solvers

NASA Technical Reports Server (NTRS)

Shyy, Wei; Thakur, Siddharth

1992-01-01

The treatment of the convection terms in the sequential solver, a standard procedure found in virtually all pressure based algorithms, to compute the flow problems with sharp gradients and source terms is investigated. Both scalar model problems and one-dimensional gas dynamics equations have been used to study the various issues involved. Different approaches including the use of nonlinear filtering techniques and adoption of TVD type schemes have been investigated. Special treatments of the source terms such as pressure gradients and heat release have also been devised, yielding insight and improved accuracy of the numerical procedure adopted.

An Initial Analysis of Learning Styles Exhibited by High School Science Students

NASA Astrophysics Data System (ADS)

Donelson, Frederick; Bensel, H.; Miller, D.; Seebode, S.; Ciardi, D. R.; Howell, S. B.

2014-01-01

Educational research magazines are filled with information on learning styles and how they affect the learning process, but few studies have been conducted to specifically look at learning styles exhibited by high school science students. This project attempted to obtain a general “snapshot” of learning styles found in the high school science classroom, and then compare that to one derived from a subgroup of highly motivated science students involved in a NITARP student team. Control students (N=54) from elective science courses at four high schools (urban, suburban, and rural) were administered the Felder Learning Style (FLS) assessment and rated on Likert scales in four learning constructs: Active/Reflective, Sensing/Intuitive, Visual/Verbal, and Sequential/Global. NITARP student team members (N=7) were given the FLS before project work began, and then re-tested approximately three months later, after project work concluded. Chi Square Analysis showed no clear significant difference between the general group and the NITARP group (p = .52). Both groups tended to be very visual and sequential, but more reflective than active. The results suggest several concerns that science teachers may need to address: (1) Research shows best practice science classes often are hands on, yet a majority of students are more reflective than active; (2) Big ideas tend to be better understood by global students, but a majority are more sequential; (3) Since a majority of students are visual, information given verbally may not be very effective. Further research is indicated for these areas of discontinuity. This research was conducted as part of the NASA/IPAC Training in Archival Research Project (NITARP) and was funded by NASA Astrophysics Data Program and Archive Outreach funds.

Proactive and retroactive transfer of middle age adults in a sequential motor learning task.

PubMed

Verneau, Marion; van der Kamp, John; Savelsbergh, Geert J P; de Looze, Michiel P

2015-03-01

We assessed the effects of aging in the transfer of motor learning in a sequential manual assembly task that is representative for real working conditions. On two different days, young (18-30 years) and middle-aged adults (50-65 years) practiced to build two products that consisted of the same six components but which had to be assembled in a partly different order. Assembly accuracy and movement time during tests, which were performed before and after the practice sessions, were compared to determine proactive and retroactive transfer. The results showed proactive facilitation (i.e., benefits from having learned the first product on learning the second one) in terms of an overall shortening of movement time in both age-groups. In addition, only the middle-aged adults were found to show sequence-specific proactive facilitation, in which the shortening of movement time was limited to components that had the same the order in the two products. Most likely, however, the sequence-specific transfer was an epiphenomenon of the comparatively low rate of learning among the middle-aged adults. The results, however, did reveal genuine differences between the groups for retroactive transfer (i.e., effects from learning the second product on performance of the first). Middle-aged adults tended to show more pronounced retroactive interference in terms of a general decrease in accuracy, while younger adults showed sequence-specific retroactive facilitation (i.e., shortening of movement times for components that had the same order in the two products), but only when they were fully accurate. Together this suggests that in the learning of sequential motor tasks the effects of age are more marked for retroactive transfer than for proactive transfer. Copyright © 2015 Elsevier B.V. All rights reserved.

Evaluating the Reliability and Impact of a Quality Assurance System for E-Learning Courseware

ERIC Educational Resources Information Center

Sung, Yao-Ting; Chang, Kuo-En; Yu, Wen-Cheng

2011-01-01

Assuring e-learning quality is of interest worldwide. This paper introduces the methods of e-learning courseware quality assurance (a quality certification system) adopted by the eLQSC (e-Learning Quality Service Centre) in Taiwan. A sequential/explanatory design with a mixed methodology was used to gather research data and conduct data analyses.…

A Growth Curve Analysis of Novel Word Learning by Sequential Bilingual Preschool Children

ERIC Educational Resources Information Center

Kan, Pui Fong; Kohnert, Kathryn

2012-01-01

Longitudinal word learning studies which control for experience can advance understanding of language learning and potential intra- and inter-language relationships in developing bilinguals. We examined novel word learning in both the first (L1) and the second (L2) languages of bilingual children. The rate and shape of change as well as the role…

Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

ERIC Educational Resources Information Center

Sun, Jerry Chih-Yuan; Kuo, Cian-Yu; Hou, Huei-Tse; Lin, Yu-Yan

2017-01-01

The purposes of this study were to provide a game-based anti-phishing lesson to 110 elementary school students in Taiwan, explore their learning behavioral patterns, and investigate the effects of the flow states on their learning behavioral patterns and learning achievement. The study recorded behaviour logs, and applied a pre- and post-test on…

An online semi-supervised brain-computer interface.

PubMed

Gu, Zhenghui; Yu, Zhuliang; Shen, Zhifang; Li, Yuanqing

2013-09-01

Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

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

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. Tomore » alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.« less

Classification and assessment tools for structural motif discovery algorithms.

PubMed

Badr, Ghada; Al-Turaiki, Isra; Mathkour, Hassan

2013-01-01

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case. In this paper, we survey, classify, and compare different algorithms that solve the structural motif discovery problem, where the underlying sequences may be different. We highlight their strengths and weaknesses. We start by proposing a benchmark dataset and a measurement tool that can be used to evaluate different motif discovery approaches. Then, we proceed by proposing our experimental setup. Finally, results are obtained using the proposed benchmark to compare available tools. To the best of our knowledge, this is the first attempt to compare tools solely designed for structural motif discovery. Results show that the accuracy of discovered motifs is relatively low. The results also suggest a complementary behavior among tools where some tools perform well on simple structures, while other tools are better for complex structures. We have classified and evaluated the performance of available structural motif discovery tools. In addition, we have proposed a benchmark dataset with tools that can be used to evaluate newly developed tools.

The implementation of e-learning tools to enhance undergraduate bioinformatics teaching and learning: a case study in the National University of Singapore

PubMed Central

2009-01-01

Background The rapid advancement of computer and information technology in recent years has resulted in the rise of e-learning technologies to enhance and complement traditional classroom teaching in many fields, including bioinformatics. This paper records the experience of implementing e-learning technology to support problem-based learning (PBL) in the teaching of two undergraduate bioinformatics classes in the National University of Singapore. Results Survey results further established the efficiency and suitability of e-learning tools to supplement PBL in bioinformatics education. 63.16% of year three bioinformatics students showed a positive response regarding the usefulness of the Learning Activity Management System (LAMS) e-learning tool in guiding the learning and discussion process involved in PBL and in enhancing the learning experience by breaking down PBL activities into a sequential workflow. On the other hand, 89.81% of year two bioinformatics students indicated that their revision process was positively impacted with the use of LAMS for guiding the learning process, while 60.19% agreed that the breakdown of activities into a sequential step-by-step workflow by LAMS enhances the learning experience Conclusion We show that e-learning tools are useful for supplementing PBL in bioinformatics education. The results suggest that it is feasible to develop and adopt e-learning tools to supplement a variety of instructional strategies in the future. PMID:19958511

Galileo spacecraft autonomous attitude determination using a V-slit star scanner

NASA Technical Reports Server (NTRS)

Mobasser, Sohrab; Lin, Shuh-Ren

1991-01-01

The autonomous attitude determination system of Galileo spacecraft, consisting of a radiation hardened star scanner and a processing algorithm is presented. The algorithm applying to this system are the sequential star identification and attitude estimation. The star scanner model is reviewed in detail and the flight software parameters that must be updated frequently during flight, due to degradation of the scanner response and the star background change are identified.

Patterns and Practices for Future Architectures

DTIC Science & Technology

2014-08-01

14. SUBJECT TERMS computing architecture, graph algorithms, high-performance computing, big data , GPU 15. NUMBER OF PAGES 44 16. PRICE CODE 17...at Vertex 1 6 Figure 4: Data Structures Created by Kernel 1 of Single CPU, List Implementation Using the Graph in the Example from Section 1.2 9...Figure 5: Kernel 2 of Graph500 BFS Reference Implementation: Single CPU, List 10 Figure 6: Data Structures for Sequential CSR Algorithm 12 Figure 7

Dealing Out Sequentiality.

ERIC Educational Resources Information Center

Mugane, John

2001-01-01

Looks at the elaboration and exploration of content in the Swahili learning site SALAMA. Identifies within SALAMA design and implementation methods that ardently and perennially seek to lead learners to evolve learning habits of their own beyond formal instruction and textbooks. (Author/VWL)

On adaptive learning rate that guarantees convergence in feedforward networks.

PubMed

Behera, Laxmidhar; Kumar, Swagat; Patnaik, Awhan

2006-09-01

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

Fast Solution in Sparse LDA for Binary Classification

NASA Technical Reports Server (NTRS)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Post-processing techniques to enhance reliability of assignment algorithm based performance measures.

DOT National Transportation Integrated Search

2011-01-01

This study develops an enhanced transportation planning framework by augmenting the sequential four-step : planning process with post-processing techniques. The post-processing techniques are incorporated through a feedback : mechanism and aim to imp...

The Relationships between Language Learning Strategies and Positive Emotions among Malaysian ESL Undergraduates

ERIC Educational Resources Information Center

Mohammadipour, Mohammad; Rashid, Sabariah Md; Rafik-Galea, Shameem; Thai, Yap Ngee

2018-01-01

Emotions are an indispensable part of second language learning. The aim of this study is to determine the relationship between the use of language learning strategies and positive emotions. The present study adopted a sequential mixed methods design. The participants were 300 Malaysian ESL undergraduates selected through stratified random sampling…

Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques

ERIC Educational Resources Information Center

Sonnenberg, Christoph; Bannert, Maria

2015-01-01

According to research examining self-regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing…

Non-Adjacent Dependency Learning in Infants at Familial Risk of Dyslexia

ERIC Educational Resources Information Center

Kerkhoff, Annemarie; de Bree, Elise; de Klerk, Maartje; Wijnen, Frank

2013-01-01

This study tests the hypothesis that developmental dyslexia is (partly) caused by a deficit in implicit sequential learning, by investigating whether infants at familial risk of dyslexia can track non-adjacent dependencies in an artificial language. An implicit learning deficit would hinder detection of such dependencies, which mark grammatical…

Neural Correlates of Sequence Learning with Stochastic Feedback

ERIC Educational Resources Information Center

Averbeck, Bruno B.; Kilner, James; Frith, Christopher D.

2011-01-01

Although much is known about decision making under uncertainty when only a single step is required in the decision process, less is known about sequential decision making. We carried out a stochastic sequence learning task in which subjects had to use noisy feedback to learn sequences of button presses. We compared flat and hierarchical behavioral…

Implementation of Ptechls Modules in Rural Malaysian Secondary School: A Needs Analysis

ERIC Educational Resources Information Center

Alias, Norlidah; DeWitt, Dorothy; Siraj, Saedah; Rahman, Mohd Nazri Abdul; Gelamdin, Rashidah Begum; Rauf, Rose Amnah Abd

2014-01-01

Research has shown that the strategy of matching learning style with certain technology enhances students' learning experience. This study seeks to identify the learning styles among students in a rural secondary school, based on the Felder Silverman Model (1988) which comprises four dimensions (visual/verbal, active/reflective, sequential/global,…

The Philosophical and Pedagogical Underpinnings of Active Learning in Engineering Education

ERIC Educational Resources Information Center

Christie, Michael; de Graaff, Erik

2017-01-01

In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving…

Lecture Recording: Structural and Symbolic Information vs. Flexibility of Presentation

ERIC Educational Resources Information Center

Stolzenberg, Daniel; Pforte, Stefan

2007-01-01

Rapid eLearning is an ongoing trend which enables flexible and cost-effective creation of learning materials. Especially, lecture recording has turned out to be a lightweight method particularly suited for existing lectures and blended learning strategies. In order to not only sequentially play back but offer full fledged navigation, search and…

Implicit Learning of Predictive Relationships in Three-element Visual Sequences by Young and Old Adults

PubMed Central

Howard, James H.; Howard, Darlene V.; Dennis, Nancy A.; Kelly, Andrew J.

2008-01-01

Knowledge of sequential relationships enables future events to be anticipated and processed efficiently. Research with the serial reaction time task (SRTT) has shown that sequence learning often occurs implicitly without effort or awareness. Here we report four experiments that use a triplet-learning task (TLT) to investigate sequence learning in young and older adults. In the TLT people respond only to the last target event in a series of discrete, three-event sequences or triplets. Target predictability is manipulated by varying the triplet frequency (joint probability) and/or the statistical relationships (conditional probabilities) among events within the triplets. Results revealed that both groups learned, though older adults showed less learning of both joint and conditional probabilities. Young people used the statistical information in both cues, but older adults relied primarily on information in the second cue alone. We conclude that the TLT complements and extends the SRTT and other tasks by offering flexibility in the kinds of sequential statistical regularities that may be studied as well as by controlling event timing and eliminating motor response sequencing. PMID:18763897

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Reduction of cognitive conflict and learning style impact towards student-teacher's misconception load

NASA Astrophysics Data System (ADS)

A'yun, Kurroti; Suyono, Poedjiastoeti, Sri; Bin-Tahir, Saidna Zulfiqar

2017-08-01

The most crucial issue in education is a misconception that is caused by the misconception of the students themselves. Therefore, this study provided the solution to improve the quality of teaching chemistry in the schools through the remediation of misconceptions to the chemistry teacher candidates. This study employed a mixed method approach using concurrent embedded designs where it tended more to the qualitative research, but it still relied on the quantitative research in the assessment of the learning impact. The results of this study were the students with higher levels of cognitive conflict still have high loads of misconceptions (MC), it possibly due to the type of students' learning styles that is the sequential-global balanced. To facilitate the cognitive conflict character and the learning style of sequential-global balanced, the researchers created an integrated worksheet conceptual change with peer learning (WCCPL). The peer learning undertaken in the last stages of conceptual change of WCCPL can increase the resistance of students' concept in a category of knowing the concept significantly, but it should be examined in an in-depth study related to the long-term memory.

Language experience changes subsequent learning

PubMed Central

Onnis, Luca; Thiessen, Erik

2013-01-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. PMID:23200510

Incremental Bayesian Category Learning From Natural Language.

PubMed

Frermann, Lea; Lapata, Mirella

2016-08-01

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., chair is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata .). Copyright © 2015 Cognitive Science Society, Inc.

Challenges in the Verification of Reinforcement Learning Algorithms

NASA Technical Reports Server (NTRS)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

PubMed

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

Sequential causal inference: Application to randomized trials of adaptive treatment strategies

PubMed Central

Dawson, Ree; Lavori, Philip W.

2009-01-01

SUMMARY Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal ‘one-step’ estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies. PMID:17914714

Efficient Learning Algorithms with Limited Information

ERIC Educational Resources Information Center

De, Anindya

2013-01-01

The thesis explores efficient learning algorithms in settings which are more restrictive than the PAC model of learning (Valiant) in one of the following two senses: (i) The learning algorithm has a very weak access to the unknown function, as in, it does not get labeled samples for the unknown function (ii) The error guarantee required from the…

Prime Numbers Comparison using Sieve of Eratosthenes and Sieve of Sundaram Algorithm

NASA Astrophysics Data System (ADS)

Abdullah, D.; Rahim, R.; Apdilah, D.; Efendi, S.; Tulus, T.; Suwilo, S.

2018-03-01

Prime numbers are numbers that have their appeal to researchers due to the complexity of these numbers, many algorithms that can be used to generate prime numbers ranging from simple to complex computations, Sieve of Eratosthenes and Sieve of Sundaram are two algorithm that can be used to generate Prime numbers of randomly generated or sequential numbered random numbers, testing in this study to find out which algorithm is better used for large primes in terms of time complexity, the test also assisted with applications designed using Java language with code optimization and Maximum memory usage so that the testing process can be simultaneously and the results obtained can be objective

Recognition of strong earthquake-prone areas with a single learning class

NASA Astrophysics Data System (ADS)

Gvishiani, A. D.; Agayan, S. M.; Dzeboev, B. A.; Belov, I. O.

2017-05-01

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one "pure" high-seismic class. The new algorithm operates in the space of absolute values of the geological-geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with M ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.

The Neurodevelopmental Evaluation in a Private Pediatric Setting.

ERIC Educational Resources Information Center

Fomalont, Robert

1986-01-01

A comprehensive neurodevelopment evaluation technique known as PEERAMID is recommended for pediatricians in the evaluation of learning disabilities. This multifaceted system assesses the learning process individually, analyzing: minor neurological indicators, fine and gross motor function, language ability, temporal-sequential organization,…

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Guided color consistency optimization for image mosaicking

NASA Astrophysics Data System (ADS)

Xie, Renping; Xia, Menghan; Yao, Jian; Li, Li

2018-01-01

This paper studies the problem of color consistency correction for sequential images with diverse color characteristics. Existing algorithms try to adjust all images to minimize color differences among images under a unified energy framework, however, the results are prone to presenting a consistent but unnatural appearance when the color difference between images is large and diverse. In our approach, this problem is addressed effectively by providing a guided initial solution for the global consistency optimization, which avoids converging to a meaningless integrated solution. First of all, to obtain the reliable intensity correspondences in overlapping regions between image pairs, we creatively propose the histogram extreme point matching algorithm which is robust to image geometrical misalignment to some extents. In the absence of the extra reference information, the guided initial solution is learned from the major tone of the original images by searching some image subset as the reference, whose color characteristics will be transferred to the others via the paths of graph analysis. Thus, the final results via global adjustment will take on a consistent color similar to the appearance of the reference image subset. Several groups of convincing experiments on both the synthetic dataset and the challenging real ones sufficiently demonstrate that the proposed approach can achieve as good or even better results compared with the state-of-the-art approaches.

A Comparison of PETSC Library and HPF Implementations of an Archetypal PDE Computation

NASA Technical Reports Server (NTRS)

Hayder, M. Ehtesham; Keyes, David E.; Mehrotra, Piyush

1997-01-01

Two paradigms for distributed-memory parallel computation that free the application programmer from the details of message passing are compared for an archetypal structured scientific computation a nonlinear, structured-grid partial differential equation boundary value problem using the same algorithm on the same hardware. Both paradigms, parallel libraries represented by Argonne's PETSC, and parallel languages represented by the Portland Group's HPF, are found to be easy to use for this problem class, and both are reasonably effective in exploiting concurrency after a short learning curve. The level of involvement required by the application programmer under either paradigm includes specification of the data partitioning (corresponding to a geometrically simple decomposition of the domain of the PDE). Programming in SPAM style for the PETSC library requires writing the routines that discretize the PDE and its Jacobian, managing subdomain-to-processor mappings (affine global- to-local index mappings), and interfacing to library solver routines. Programming for HPF requires a complete sequential implementation of the same algorithm, introducing concurrency through subdomain blocking (an effort similar to the index mapping), and modest experimentation with rewriting loops to elucidate to the compiler the latent concurrency. Correctness and scalability are cross-validated on up to 32 nodes of an IBM SP2.

Analyzing Kernel Matrices for the Identification of Differentially Expressed Genes

PubMed Central

Xia, Xiao-Lei; Xing, Huanlai; Liu, Xueqin

2013-01-01

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing -like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate. PMID:24349110

A biometric authentication model using hand gesture images.

PubMed

Fong, Simon; Zhuang, Yan; Fister, Iztok; Fister, Iztok

2013-10-30

A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i' , 'l' , 'o' , 'v' , 'e' , and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

A strategy for quantum algorithm design assisted by machine learning

NASA Astrophysics Data System (ADS)

Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

2014-07-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

Three parameters optimizing closed-loop control in sequential segmental neuromuscular stimulation.

PubMed

Zonnevijlle, E D; Somia, N N; Perez Abadia, G; Stremel, R W; Maldonado, C J; Werker, P M; Kon, M; Barker, J H

1999-05-01

In conventional dynamic myoplasties, the force generation is poorly controlled. This causes unnecessary fatigue of the transposed/transplanted electrically stimulated muscles and causes damage to the involved tissues. We introduced sequential segmental neuromuscular stimulation (SSNS) to reduce muscle fatigue by allowing part of the muscle to rest periodically while the other parts work. Despite this improvement, we hypothesize that fatigue could be further reduced in some applications of dynamic myoplasty if the muscles were made to contract according to need. The first necessary step is to gain appropriate control over the contractile activity of the dynamic myoplasty. Therefore, closed-loop control was tested on a sequentially stimulated neosphincter to strive for the best possible control over the amount of generated pressure. A selection of parameters was validated for optimizing control. We concluded that the frequency of corrections, the threshold for corrections, and the transition time are meaningful parameters in the controlling algorithm of the closed-loop control in a sequentially stimulated myoplasty.

Research on sparse feature matching of improved RANSAC algorithm

NASA Astrophysics Data System (ADS)

Kong, Xiangsi; Zhao, Xian

2018-04-01

In this paper, a sparse feature matching method based on modified RANSAC algorithm is proposed to improve the precision and speed. Firstly, the feature points of the images are extracted using the SIFT algorithm. Then, the image pair is matched roughly by generating SIFT feature descriptor. At last, the precision of image matching is optimized by the modified RANSAC algorithm,. The RANSAC algorithm is improved from three aspects: instead of the homography matrix, this paper uses the fundamental matrix generated by the 8 point algorithm as the model; the sample is selected by a random block selecting method, which ensures the uniform distribution and the accuracy; adds sequential probability ratio test(SPRT) on the basis of standard RANSAC, which cut down the overall running time of the algorithm. The experimental results show that this method can not only get higher matching accuracy, but also greatly reduce the computation and improve the matching speed.

Impaired sequential and partially compensated probabilistic skill learning in Parkinson's disease.

PubMed

Kemény, Ferenc; Demeter, Gyula; Racsmány, Mihály; Valálik, István; Lukács, Ágnes

2018-06-08

The striatal dopaminergic dysfunction in Parkinson's disease (PD) has been associated with deficits in skill learning in numerous studies, but some of the findings remain controversial. Our aim was to explore the generality of the learning deficit using two widely reported skill learning tasks in the same group of Parkinson's patients. Thirty-four patients with PD (mean age: 62.83 years, SD: 7.67) were compared to age-matched healthy adults. Two tasks were employed: the Serial Reaction Time Task (SRT), testing the learning of motor sequences, and the Weather Prediction (WP) task, testing non-sequential probabilistic category learning. On the SRT task, patients with PD showed no significant evidence for sequence learning. These results support and also extend previous findings, suggesting that motor skill learning is vulnerable in PD. On the WP task, the PD group showed the same amount of learning as controls, but they exploited qualitatively different strategies in predicting the target categories. While controls typically combined probabilities from multiple predicting cues, patients with PD instead focused on individual cues. We also found moderate to high correlations between the different measures of skill learning. These findings support our hypothesis that skill learning is generally impaired in PD, and can in some cases be compensated by relying on alternative learning strategies. © 2018 The Authors. Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

NASA Astrophysics Data System (ADS)

Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

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

Chang, X; Yang, D

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was usedmore » to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.« less

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Update on Bayesian Blocks: Segmented Models for Sequential Data

NASA Technical Reports Server (NTRS)

Scargle, Jeff

2017-01-01

The Bayesian Block algorithm, in wide use in astronomy and other areas, has been improved in several ways. The model for block shape has been generalized to include other than constant signal rate - e.g., linear, exponential, or other parametric models. In addition the computational efficiency has been improved, so that instead of O(N**2) the basic algorithm is O(N) in most cases. Other improvements in the theory and application of segmented representations will be described.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)

PubMed Central

Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

2016-01-01

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.

PubMed

Zikmund, T; Kvasnica, L; Týč, M; Křížová, A; Colláková, J; Chmelík, R

2014-11-01

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Real-time implementations of image segmentation algorithms on shared memory multicore architecture: a survey (Conference Presentation)

NASA Astrophysics Data System (ADS)

Akil, Mohamed

2017-05-01

The real-time processing is getting more and more important in many image processing applications. Image segmentation is one of the most fundamental tasks image analysis. As a consequence, many different approaches for image segmentation have been proposed. The watershed transform is a well-known image segmentation tool. The watershed transform is a very data intensive task. To achieve acceleration and obtain real-time processing of watershed algorithms, parallel architectures and programming models for multicore computing have been developed. This paper focuses on the survey of the approaches for parallel implementation of sequential watershed algorithms on multicore general purpose CPUs: homogeneous multicore processor with shared memory. To achieve an efficient parallel implementation, it's necessary to explore different strategies (parallelization/distribution/distributed scheduling) combined with different acceleration and optimization techniques to enhance parallelism. In this paper, we give a comparison of various parallelization of sequential watershed algorithms on shared memory multicore architecture. We analyze the performance measurements of each parallel implementation and the impact of the different sources of overhead on the performance of the parallel implementations. In this comparison study, we also discuss the advantages and disadvantages of the parallel programming models. Thus, we compare the OpenMP (an application programming interface for multi-Processing) with Ptheads (POSIX Threads) to illustrate the impact of each parallel programming model on the performance of the parallel implementations.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

PubMed

Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

2016-07-13

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

Promote the Vocational Education Program. Competency-Based Vocational Education Administrator Module Series. Leadership and Training Series No. 58B-9.

ERIC Educational Resources Information Center

Quinn, Karen M.; And Others

Designed to provide pre- and inservice administrators with the skills necessary to select appropriate program development and implementation, and monitor and evaluate their success, this competency-based learning module consists of an introduction and four sequential learning experiences. Each learning experience contains an overview, required and…

Learning a Generative Probabilistic Grammar of Experience: A Process-Level Model of Language Acquisition

ERIC Educational Resources Information Center

Kolodny, Oren; Lotem, Arnon; Edelman, Shimon

2015-01-01

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given…

Performance of Children with Developmental Dyslexia on High and Low Topological Entropy Artificial Grammar Learning Task

ERIC Educational Resources Information Center

Katan, Pesia; Kahta, Shani; Sasson, Ayelet; Schiff, Rachel

2017-01-01

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine…

Speech Motor Sequence Learning: Acquisition and Retention in Parkinson Disease and Normal Aging

ERIC Educational Resources Information Center

Whitfield, Jason A.; Goberman, Alexander M.

2017-01-01

Purpose: The aim of the current investigation was to examine speech motor sequence learning in neurologically healthy younger adults, neurologically healthy older adults, and individuals with Parkinson disease (PD) over a 2-day period. Method: A sequential nonword repetition task was used to examine learning over 2 days. Participants practiced a…

Hyper-Binding across Time: Age Differences in the Effect of Temporal Proximity on Paired-Associate Learning

ERIC Educational Resources Information Center

Campbell, Karen L.; Trelle, Alexandra; Hasher, Lynn

2014-01-01

Older adults show hyper- (or excessive) binding effects for simultaneously and sequentially presented distraction. Here, we addressed the potential role of hyper-binding in paired-associate learning. Older and younger adults learned a list of word pairs and then received an associative recognition task in which rearranged pairs were formed from…

Implicit Learning of Predictive Relationships in Three-Element Visual Sequences by Young and Old Adults

ERIC Educational Resources Information Center

Howard, James H., Jr.; Howard, Darlene V.; Dennis, Nancy A.; Kelly, Andrew J.

2008-01-01

Knowledge of sequential relationships enables future events to be anticipated and processed efficiently. Research with the serial reaction time task (SRTT) has shown that sequence learning often occurs implicitly without effort or awareness. Here, the authors report 4 experiments that use a triplet-learning task (TLT) to investigate sequence…

Sharing "Cat Games" and Cookies: Special Education Students Investigate Division

ERIC Educational Resources Information Center

Taber, Susan B.; Canonica, Michele

2008-01-01

Learning mathematics has traditionally been thought of as a sequential progression. Children learn to count to 10, then to 20, and then to 100. They learn to add without regrouping and then with regrouping. The authors teach addition before multiplication and the two-times table before the six-times table. They usually teach division as a separate…

Learning by Teaching: Can It Be Utilized to Develop Inquiry Skills?

ERIC Educational Resources Information Center

Aslan, Safiye

2017-01-01

This study aims to investigate the effect of learning by teaching on inquiry skills. With its explanatory sequential design, this particular study focuses on interrogating whether learning by teaching has an effect on prospective science teachers' inquiry skills and to unveil how it does so, only if it had an effect. The current research is…

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

Acetylcholine-modulated plasticity in reward-driven navigation: a computational study.

PubMed

Zannone, Sara; Brzosko, Zuzanna; Paulsen, Ole; Clopath, Claudia

2018-06-21

Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

Implementing Quality Criteria in Designing and Conducting a Sequential Quan [right arrow] Qual Mixed Methods Study of Student Engagement with Learning Applied Research Methods Online

ERIC Educational Resources Information Center

Ivankova, Nataliya V.

2014-01-01

In spite of recent methodological developments related to quality assurance in mixed methods research, practical examples of how to implement quality criteria in designing and conducting sequential QUAN [right arrow] QUAL mixed methods studies to ensure the process is systematic and rigorous remain scarce. This article discusses a three-step…

Gradient descent learning algorithm overview: a general dynamical systems perspective.

PubMed

Baldi, P

1995-01-01

Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

PubMed

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies

PubMed Central

Liu, Ying; ZENG, Donglin; WANG, Yuanjia

2014-01-01

Summary Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. PMID:25642116

Algorithm Visualization System for Teaching Spatial Data Algorithms

ERIC Educational Resources Information Center

Nikander, Jussi; Helminen, Juha; Korhonen, Ari

2010-01-01

TRAKLA2 is a web-based learning environment for data structures and algorithms. The system delivers automatically assessed algorithm simulation exercises that are solved using a graphical user interface. In this work, we introduce a novel learning environment for spatial data algorithms, SDA-TRAKLA2, which has been implemented on top of the…

Constrained Metric Learning by Permutation Inducing Isometries.

PubMed

Bosveld, Joel; Mahmood, Arif; Huynh, Du Q; Noakes, Lyle

2016-01-01

The choice of metric critically affects the performance of classification and clustering algorithms. Metric learning algorithms attempt to improve performance, by learning a more appropriate metric. Unfortunately, most of the current algorithms learn a distance function which is not invariant to rigid transformations of images. Therefore, the distances between two images and their rigidly transformed pair may differ, leading to inconsistent classification or clustering results. We propose to constrain the learned metric to be invariant to the geometry preserving transformations of images that induce permutations in the feature space. The constraint that these transformations are isometries of the metric ensures consistent results and improves accuracy. Our second contribution is a dimension reduction technique that is consistent with the isometry constraints. Our third contribution is the formulation of the isometry constrained logistic discriminant metric learning (IC-LDML) algorithm, by incorporating the isometry constraints within the objective function of the LDML algorithm. The proposed algorithm is compared with the existing techniques on the publicly available labeled faces in the wild, viewpoint-invariant pedestrian recognition, and Toy Cars data sets. The IC-LDML algorithm has outperformed existing techniques for the tasks of face recognition, person identification, and object classification by a significant margin.

Teacher's Guide: Social Studies, 3.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

Part of a K-12 sequential series, this teacher's guide presents objectives and activities for social studies students in grade 3. Five major sections concentrate on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical…

Teacher's Guide: Social Studies, 5.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

Part of a sequential K-12 program, this teacher's guide provides objectives and activities for students in grade 5. Five major sections correspond to learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills include listening, speaking, viewing, reading, writing, map, and statistical abilities. Students…

Discrete Circuits Support Generalized versus Context-Specific Vocal Learning in the Songbird.

PubMed

Tian, Lucas Y; Brainard, Michael S

2017-12-06

Motor skills depend on the reuse of individual gestures in multiple sequential contexts (e.g., a single phoneme in different words). Yet optimal performance requires that a given gesture be modified appropriately depending on the sequence in which it occurs. To investigate the neural architecture underlying such context-dependent modifications, we studied Bengalese finch song, which, like speech, consists of variable sequences of "syllables." We found that when birds are instructed to modify a syllable in one sequential context, learning generalizes across contexts; however, if unique instruction is provided in different contexts, learning is specific for each context. Using localized inactivation of a cortical-basal ganglia circuit specialized for song, we show that this balance between generalization and specificity reflects a hierarchical organization of neural substrates. Primary motor circuitry encodes a core syllable representation that contributes to generalization, while top-down input from cortical-basal ganglia circuitry biases this representation to enable context-specific learning. Copyright © 2017 Elsevier Inc. All rights reserved.

An incremental approach to genetic-algorithms-based classification.

PubMed

Guan, Sheng-Uei; Zhu, Fangming

2005-04-01

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

Does Language Help Regularity Learning? The Influence of Verbalizations on Implicit Sequential Regularity Learning and the Emergence of Explicit Knowledge in Children, Younger and Older Adults

ERIC Educational Resources Information Center

Ferdinand, Nicola K.; Kray, Jutta

2017-01-01

This study aimed at investigating the ability to learn regularities across the life span and examine whether this learning process can be supported or hampered by verbalizations. For this purpose, children (aged 8-10 years) and younger (aged 19-30 years) and older (aged 70-80 years) adults took part in a sequence learning experiment. We found that…

On the error probability of general tree and trellis codes with applications to sequential decoding

NASA Technical Reports Server (NTRS)

Johannesson, R.

1973-01-01

An upper bound on the average error probability for maximum-likelihood decoding of the ensemble of random binary tree codes is derived and shown to be independent of the length of the tree. An upper bound on the average error probability for maximum-likelihood decoding of the ensemble of random L-branch binary trellis codes of rate R = 1/n is derived which separates the effects of the tail length T and the memory length M of the code. It is shown that the bound is independent of the length L of the information sequence. This implication is investigated by computer simulations of sequential decoding utilizing the stack algorithm. These simulations confirm the implication and further suggest an empirical formula for the true undetected decoding error probability with sequential decoding.

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Designing the Architecture of Hierachical Neural Networks Model Attention, Learning and Goal-Oriented Behavior

DTIC Science & Technology

1993-12-31

19,23,25,26,27,28,32,33,35,41]) - A new cost function is postulated and an algorithm that employs this cost function is proposed for the learning of...updates the controller parameters from time to time [53]. The learning control algorithm consist of updating the parameter estimates as used in the...proposed cost function with the other learning type algorithms , such as based upon learning of iterative tasks [Kawamura-85], variable structure

Enhancing membrane protein subcellular localization prediction by parallel fusion of multi-view features.

PubMed

Yu, Dongjun; Wu, Xiaowei; Shen, Hongbin; Yang, Jian; Tang, Zhenmin; Qi, Yong; Yang, Jingyu

2012-12-01

Membrane proteins are encoded by ~ 30% in the genome and function importantly in the living organisms. Previous studies have revealed that membrane proteins' structures and functions show obvious cell organelle-specific properties. Hence, it is highly desired to predict membrane protein's subcellular location from the primary sequence considering the extreme difficulties of membrane protein wet-lab studies. Although many models have been developed for predicting protein subcellular locations, only a few are specific to membrane proteins. Existing prediction approaches were constructed based on statistical machine learning algorithms with serial combination of multi-view features, i.e., different feature vectors are simply serially combined to form a super feature vector. However, such simple combination of features will simultaneously increase the information redundancy that could, in turn, deteriorate the final prediction accuracy. That's why it was often found that prediction success rates in the serial super space were even lower than those in a single-view space. The purpose of this paper is investigation of a proper method for fusing multiple multi-view protein sequential features for subcellular location predictions. Instead of serial strategy, we propose a novel parallel framework for fusing multiple membrane protein multi-view attributes that will represent protein samples in complex spaces. We also proposed generalized principle component analysis (GPCA) for feature reduction purpose in the complex geometry. All the experimental results through different machine learning algorithms on benchmark membrane protein subcellular localization datasets demonstrate that the newly proposed parallel strategy outperforms the traditional serial approach. We also demonstrate the efficacy of the parallel strategy on a soluble protein subcellular localization dataset indicating the parallel technique is flexible to suite for other computational biology problems. The software and datasets are available at: http://www.csbio.sjtu.edu.cn/bioinf/mpsp.

Efficient Record Linkage Algorithms Using Complete Linkage Clustering.

PubMed

Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

2016-01-01

Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

PubMed Central

Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

2016-01-01

Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. PMID:27124604

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Method of Real-Time Principal-Component Analysis

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu

2005-01-01

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Community Detection on the GPU

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

Naim, Md; Manne, Fredrik; Halappanavar, Mahantesh

We present and evaluate a new GPU algorithm based on the Louvain method for community detection. Our algorithm is the first for this problem that parallelizes the access to individual edges. In this way we can fine tune the load balance when processing networks with nodes of highly varying degrees. This is achieved by scaling the number of threads assigned to each node according to its degree. Extensive experiments show that we obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order ofmore » magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.« less

Quantum Color Image Encryption Algorithm Based on A Hyper-Chaotic System and Quantum Fourier Transform

NASA Astrophysics Data System (ADS)

Tan, Ru-Chao; Lei, Tong; Zhao, Qing-Min; Gong, Li-Hua; Zhou, Zhi-Hong

2016-12-01

To improve the slow processing speed of the classical image encryption algorithms and enhance the security of the private color images, a new quantum color image encryption algorithm based on a hyper-chaotic system is proposed, in which the sequences generated by the Chen's hyper-chaotic system are scrambled and diffused with three components of the original color image. Sequentially, the quantum Fourier transform is exploited to fulfill the encryption. Numerical simulations show that the presented quantum color image encryption algorithm possesses large key space to resist illegal attacks, sensitive dependence on initial keys, uniform distribution of gray values for the encrypted image and weak correlation between two adjacent pixels in the cipher-image.

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Numerical study of a matrix-free trust-region SQP method for equality constrained optimization.

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

Heinkenschloss, Matthias; Ridzal, Denis; Aguilo, Miguel Antonio

2011-12-01

This is a companion publication to the paper 'A Matrix-Free Trust-Region SQP Algorithm for Equality Constrained Optimization' [11]. In [11], we develop and analyze a trust-region sequential quadratic programming (SQP) method that supports the matrix-free (iterative, in-exact) solution of linear systems. In this report, we document the numerical behavior of the algorithm applied to a variety of equality constrained optimization problems, with constraints given by partial differential equations (PDEs).

Hardware Acceleration of Adaptive Neural Algorithms.

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

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

Fragmenting networks by targeting collective influencers at a mesoscopic level.

PubMed

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-25

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

NASA Astrophysics Data System (ADS)

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

PubMed Central

Kobayashi, Teruyoshi; Masuda, Naoki

2016-01-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure. PMID:27886251

The contour-buildup algorithm to calculate the analytical molecular surface.

PubMed

Totrov, M; Abagyan, R

1996-01-01

A new algorithm is presented to calculate the analytical molecular surface defined as a smooth envelope traced out by the surface of a probe sphere rolled over the molecule. The core of the algorithm is the sequential build up of multi-arc contours on the van der Waals spheres. This algorithm yields substantial reduction in both memory and time requirements of surface calculations. Further, the contour-buildup principle is intrinsically "local", which makes calculations of the partial molecular surfaces even more efficient. Additionally, the algorithm is equally applicable not only to convex patches, but also to concave triangular patches which may have complex multiple intersections. The algorithm permits the rigorous calculation of the full analytical molecular surface for a 100-residue protein in about 2 seconds on an SGI indigo with R4400++ processor at 150 Mhz, with the performance scaling almost linearly with the protein size. The contour-buildup algorithm is faster than the original Connolly algorithm an order of magnitude.

Sequential detection of influenza epidemics by the Kolmogorov-Smirnov test

PubMed Central

2012-01-01

Background Influenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year. Despite Influenza variability, fast and reliable outbreak detection is required for health resource planning. Clinical health records, as published by the Diagnosticat database in Catalonia, host useful data for probabilistic detection of influenza outbreaks. Methods This paper proposes a statistical method to detect influenza epidemic activity. Non-epidemic incidence rates are modeled against the exponential distribution, and the maximum likelihood estimate for the decaying factor λ is calculated. The sequential detection algorithm updates the parameter as new data becomes available. Binary epidemic detection of weekly incidence rates is assessed by Kolmogorov-Smirnov test on the absolute difference between the empirical and the cumulative density function of the estimated exponential distribution with significance level 0 ≤ α ≤ 1. Results The main advantage with respect to other approaches is the adoption of a statistically meaningful test, which provides an indicator of epidemic activity with an associated probability. The detection algorithm was initiated with parameter λ0 = 3.8617 estimated from the training sequence (corresponding to non-epidemic incidence rates of the 2008-2009 influenza season) and sequentially updated. Kolmogorov-Smirnov test detected the following weeks as epidemic for each influenza season: 50−10 (2008-2009 season), 38−50 (2009-2010 season), weeks 50−9 (2010-2011 season) and weeks 3 to 12 for the current 2011-2012 season. Conclusions Real medical data was used to assess the validity of the approach, as well as to construct a realistic statistical model of weekly influenza incidence rates in non-epidemic periods. For the tested data, the results confirmed the ability of the algorithm to detect the start and the end of epidemic periods. In general, the proposed test could be applied to other data sets to quickly detect influenza outbreaks. The sequential structure of the test makes it suitable for implementation in many platforms at a low computational cost without requiring to store large data sets. PMID:23031321

Investigating the Effect of the Activities Based on Explanation Assisted REACT Strategy on Learning Impulse, Momentum and Collisions Topics

ERIC Educational Resources Information Center

Ültay, Eser; Alev, Nedim

2017-01-01

The purpose of this study was to investigate the effect of explanation assisted REACT strategy which was based on context-based learning approach on prospective science teachers' (PSTs) learning in impulse, momentum and collisions topics. The sequential explanatory strategy within mixed methods design was employed in this study. The first phase of…

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Javascript Library for Developing Interactive Micro-Level Animations for Teaching and Learning Algorithms on One-Dimensional Arrays

ERIC Educational Resources Information Center

Végh, Ladislav

2016-01-01

The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…

Language experience changes subsequent learning.

PubMed

Onnis, Luca; Thiessen, Erik

2013-02-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. Copyright © 2012 Elsevier B.V. All rights reserved.

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Tsakmalis, Anestis; Chatzinotas, Symeon; Ottersten, Bjorn

2018-02-01

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

PubMed

Forsström, J

1992-01-01

The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm

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

Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E

2009-01-01

Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, ormore » retrieving relevant data for the user.« less

Multigrid methods with space–time concurrency

DOE PAGES

Falgout, R. D.; Friedhoff, S.; Kolev, Tz. V.; ...

2017-10-06

Here, we consider the comparison of multigrid methods for parabolic partial differential equations that allow space–time concurrency. With current trends in computer architectures leading towards systems with more, but not faster, processors, space–time concurrency is crucial for speeding up time-integration simulations. In contrast, traditional time-integration techniques impose serious limitations on parallel performance due to the sequential nature of the time-stepping approach, allowing spatial concurrency only. This paper considers the three basic options of multigrid algorithms on space–time grids that allow parallelism in space and time: coarsening in space and time, semicoarsening in the spatial dimensions, and semicoarsening in the temporalmore » dimension. We develop parallel software and performance models to study the three methods at scales of up to 16K cores and introduce an extension of one of them for handling multistep time integration. We then discuss advantages and disadvantages of the different approaches and their benefit compared to traditional space-parallel algorithms with sequential time stepping on modern architectures.« less

Acoustical source reconstruction from non-synchronous sequential measurements by Fast Iterative Shrinkage Thresholding Algorithm

NASA Astrophysics Data System (ADS)

Yu, Liang; Antoni, Jerome; Leclere, Quentin; Jiang, Weikang

2017-11-01

Acoustical source reconstruction is a typical inverse problem, whose minimum frequency of reconstruction hinges on the size of the array and maximum frequency depends on the spacing distance between the microphones. For the sake of enlarging the frequency of reconstruction and reducing the cost of an acquisition system, Cyclic Projection (CP), a method of sequential measurements without reference, was recently investigated (JSV,2016,372:31-49). In this paper, the Propagation based Fast Iterative Shrinkage Thresholding Algorithm (Propagation-FISTA) is introduced, which improves CP in two aspects: (1) the number of acoustic sources is no longer needed and the only making assumption is that of a "weakly sparse" eigenvalue spectrum; (2) the construction of the spatial basis is much easier and adaptive to practical scenarios of acoustical measurements benefiting from the introduction of propagation based spatial basis. The proposed Propagation-FISTA is first investigated with different simulations and experimental setups and is next illustrated with an industrial case.

Multigrid methods with space–time concurrency

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

Falgout, R. D.; Friedhoff, S.; Kolev, Tz. V.

Here, we consider the comparison of multigrid methods for parabolic partial differential equations that allow space–time concurrency. With current trends in computer architectures leading towards systems with more, but not faster, processors, space–time concurrency is crucial for speeding up time-integration simulations. In contrast, traditional time-integration techniques impose serious limitations on parallel performance due to the sequential nature of the time-stepping approach, allowing spatial concurrency only. This paper considers the three basic options of multigrid algorithms on space–time grids that allow parallelism in space and time: coarsening in space and time, semicoarsening in the spatial dimensions, and semicoarsening in the temporalmore » dimension. We develop parallel software and performance models to study the three methods at scales of up to 16K cores and introduce an extension of one of them for handling multistep time integration. We then discuss advantages and disadvantages of the different approaches and their benefit compared to traditional space-parallel algorithms with sequential time stepping on modern architectures.« less

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

PubMed

Li, Xin; Wang, Jin; Fung, Richard Y K

2018-04-01

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Applications of colored petri net and genetic algorithms to cluster tool scheduling

NASA Astrophysics Data System (ADS)

Liu, Tung-Kuan; Kuo, Chih-Jen; Hsiao, Yung-Chin; Tsai, Jinn-Tsong; Chou, Jyh-Horng

2005-12-01

In this paper, we propose a method, which uses Coloured Petri Net (CPN) and genetic algorithm (GA) to obtain an optimal deadlock-free schedule and to solve re-entrant problem for the flexible process of the cluster tool. The process of the cluster tool for producing a wafer usually can be classified into three types: 1) sequential process, 2) parallel process, and 3) sequential parallel process. But these processes are not economical enough to produce a variety of wafers in small volume. Therefore, this paper will propose the flexible process where the operations of fabricating wafers are randomly arranged to achieve the best utilization of the cluster tool. However, the flexible process may have deadlock and re-entrant problems which can be detected by CPN. On the other hand, GAs have been applied to find the optimal schedule for many types of manufacturing processes. Therefore, we successfully integrate CPN and GAs to obtain an optimal schedule with the deadlock and re-entrant problems for the flexible process of the cluster tool.

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Teacher's Guide: Social Studies, 8.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

This teacher's guide, part of a sequential K-12 series, provides objectives and activities for social studies students in grade 8. Five major sections focus on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical abilities.…

Teacher's Guide: Social Studies, 7.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

This teacher's guide, part of a sequential K-12 series, provides objectives and activities for social studies students in grade 7. Five major sections focus on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical abilities.…

Teacher's Guide: Social Studies, 9.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

This teacher's guide, part of a sequential K-12 series, provides objectives and activities for social studies students in grade 9. Five major sections focus on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical abilities.…

Teacher's Guide: Social Studies, 4.

ERIC Educational Resources Information Center

Cortland-Madison Board of Cooperative Educational Services, Cortland, NY.

This teacher's guide, part of a K-12 sequential series, provides objectives and activities for students in grade 4. Five major sections focus on learning, inquiry, and discussion skills, concepts, and values and moral reasoning. Learning skills stress listening, speaking, viewing, reading, writing, map, and statistical abilities. Students role…

Master Skills Check List and Diagnostics.

ERIC Educational Resources Information Center

Cherokee Nation of Oklahoma, Tahlequah.

This publication contains master skills checklists originating from a project to develop instructional materials which are geared to individualized, self-paced learning strategies for Cherokee adults. These checklists break down learning into discrete components that can be sequentially mastered by the student. The master skills checklists are a…