sparse multitask regression: Topics by Science.gov

Sample records for sparse multitask regression

Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Xiang-ru

2017-07-01

The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.
Effect of missing data on multitask prediction methods.

PubMed

de la Vega de León, Antonio; Chen, Beining; Gillet, Valerie J

2018-05-22

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises.
Probabilistic Low-Rank Multitask Learning.

PubMed

Kong, Yu; Shao, Ming; Li, Kang; Fu, Yun

2018-03-01

In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.
Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso.

PubMed

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

2018-06-01

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

PubMed

Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

2017-06-01

Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.
Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

PubMed Central

Zhang, Jie; Li, Qingyang; Caselli, Richard J.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin

2017-01-01

Alzheimer’s Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms. PMID:28943731
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

PubMed Central

Chen, Jianhui; Liu, Ji; Ye, Jieping

2013-01-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms. PMID:24077658
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.

PubMed

Chen, Jianhui; Liu, Ji; Ye, Jieping

2012-02-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.
Face recognition from unconstrained three-dimensional face images using multitask sparse representation

NASA Astrophysics Data System (ADS)

Bentaieb, Samia; Ouamri, Abdelaziz; Nait-Ali, Amine; Keche, Mokhtar

2018-01-01

We propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets.
Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning

NASA Astrophysics Data System (ADS)

Huang, Yong; Li, Hui; Wu, Stephen; Yang, Yongchao

2018-07-01

Sensitivity to damage and robustness to noise are critical requirements for the effectiveness of structural damage detection. In this study, a two-stage damage identification method based on the fractal dimension analysis and multi-task Bayesian learning is presented. The Higuchi’s fractal dimension (HFD) based damage index is first proposed, directly examining the time-frequency characteristic of local free vibration data of structures based on the irregularity sensitivity and noise robustness analysis of HFD. Katz’s fractal dimension is then presented to analyze the abrupt irregularity change of the spatial curve of the displacement mode shape along the structure. At the second stage, the multi-task sparse Bayesian learning technique is employed to infer the final damage localization vector, which borrow the dependent strength of the two fractal dimension based damage indication information and also incorporate the prior knowledge that structural damage occurs at a limited number of locations in a structure in the absence of its collapse. To validate the capability of the proposed method, a steel beam and a bridge, named Yonghe Bridge, are analyzed as illustrative examples. The damage identification results demonstrate that the proposed method is capable of localizing single and multiple damages regardless of its severity, and show superior robustness under heavy noise as well.
Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence.

PubMed

Redick, Thomas S; Shipstead, Zach; Meier, Matthew E; Montroy, Janelle J; Hicks, Kenny L; Unsworth, Nash; Kane, Michael J; Hambrick, D Zachary; Engle, Randall W

2016-11-01

Previous research has identified several cognitive abilities that are important for multitasking, but few studies have attempted to measure a general multitasking ability using a diverse set of multitasks. In the final dataset, 534 young adult subjects completed measures of working memory (WM), attention control, fluid intelligence, and multitasking. Correlations, hierarchical regression analyses, confirmatory factor analyses, structural equation models, and relative weight analyses revealed several key findings. First, although the complex tasks used to assess multitasking differed greatly in their task characteristics and demands, a coherent construct specific to multitasking ability was identified. Second, the cognitive ability predictors accounted for substantial variance in the general multitasking construct, with WM and fluid intelligence accounting for the most multitasking variance compared to attention control. Third, the magnitude of the relationships among the cognitive abilities and multitasking varied as a function of the complexity and structure of the various multitasks assessed. Finally, structural equation models based on a multifaceted model of WM indicated that attention control and capacity fully mediated the WM and multitasking relationship. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.
Media multitasking is associated with symptoms of depression and social anxiety.

PubMed

Becker, Mark W; Alzahabi, Reem; Hopwood, Christopher J

2013-02-01

We investigated whether multitasking with media was a unique predictor of depression and social anxiety symptoms. Participants (N=318) completed measures of their media use, personality characteristics, depression, and social anxiety. Regression analyses revealed that increased media multitasking was associated with higher depression and social anxiety symptoms, even after controlling for overall media use and the personality traits of neuroticism and extraversion. The unique association between media multitasking and these measures of psychosocial dysfunction suggests that the growing trend of multitasking with media may represent a unique risk factor for mental health problems related to mood and anxiety. Further, the results strongly suggest that future research investigating the impact of media use on mental health needs to consider the role that multitasking with media plays in the relationship.
Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.
Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.
A Mixed-Methods Study Investigating the Relationship between Media Multitasking Orientation and Grade Point Average

ERIC Educational Resources Information Center

Lee, Jennifer

2012-01-01

The intent of this study was to examine the relationship between media multitasking orientation and grade point average. The study utilized a mixed-methods approach to investigate the research questions. In the quantitative section of the study, the primary method of statistical analyses was multiple regression. The independent variables for the…
Application of Multi-task Lasso Regression in the Stellar Parametrization

NASA Astrophysics Data System (ADS)

Chang, L. N.; Zhang, P. A.

2015-01-01

The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only makes different physical parameters share the common features, but also can effectively preserve their own peculiar features. Experiments were done based on the ELODIE data simulated with the stellar atmospheric simulation model, and on the SDSS data released by the American large survey Sloan. The precision of the model is better than those of the methods in the related literature, especially for the acceleration of gravity (lg g) and the chemical abundance ([Fe/H]). In the experiments, we changed the resolution of the spectrum, and applied the noises with different signal-to-noise ratio (SNR) to the spectrum, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, has a strong stability, and also can improve the overall accuracy of the model.
Is Media Multitasking Good for Cybersecurity? Exploring the Relationship Between Media Multitasking and Everyday Cognitive Failures on Self-Reported Risky Cybersecurity Behaviors.

PubMed

Hadlington, Lee; Murphy, Karen

2018-03-01

The current study focused on how engaging in media multitasking (MMT) and the experience of everyday cognitive failures impact on the individual's engagement in risky cybersecurity behaviors (RCsB). In total, 144 participants (32 males, 112 females) completed an online survey. The age range for participants was 18 to 43 years (M = 20.63, SD = 4.04). Participants completed three scales which included an inventory of weekly MMT, a measure of everyday cognitive failures, and RCsB. There was a significant difference between heavy media multitaskers (HMM), average media multitaskers (AMM), and light media multitaskers (LMM) in terms of RCsB, with HMM demonstrating more frequent risky behaviors than LMM or AMM. The HMM group also reported more cognitive failures in everyday life than the LMM group. A regression analysis showed that everyday cognitive failures and MMT acted as significant predictors for RCsB. These results expand our current understanding of the relationship between human factors and cybersecurity behaviors, which are useful to inform the design of training and intervention packages to mitigate RCsB.
Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation

PubMed Central

Zanto, Theodore P.; van Schouwenburg, Martine R.; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4–7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13–30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement. PMID:28562642
Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation.

PubMed

Hsu, Wan-Yu; Zanto, Theodore P; van Schouwenburg, Martine R; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4-7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13-30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement.

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

DTIC Science & Technology

2013-08-14

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

NASA Astrophysics Data System (ADS)

Chang, Li-Na; Zhang, Pei-Ai

2015-07-01

The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only can obtain the information about the common features of the different physical parameters, but also can preserve effectively their own peculiar features. Experiments were done based on the ELODIE synthetic spectral data simulated with the stellar atmospheric model, and on the SDSS data released by the American large-scale survey Sloan. The estimation precision of our model is better than those of the methods in the related literature, especially for the estimates of the gravitational acceleration (lg g) and the chemical abundance ([Fe/H]). In the experiments we changed the spectral resolution, and applied the noises with different signal-to-noise ratios (SNRs) to the spectral data, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, it has a strong stability, and can also improve the overall prediction accuracy of the model.
Is Media Multitasking Good for Cybersecurity? Exploring the Relationship Between Media Multitasking and Everyday Cognitive Failures on Self-Reported Risky Cybersecurity Behaviors

PubMed Central

Murphy, Karen

2018-01-01

Abstract The current study focused on how engaging in media multitasking (MMT) and the experience of everyday cognitive failures impact on the individual's engagement in risky cybersecurity behaviors (RCsB). In total, 144 participants (32 males, 112 females) completed an online survey. The age range for participants was 18 to 43 years (M = 20.63, SD = 4.04). Participants completed three scales which included an inventory of weekly MMT, a measure of everyday cognitive failures, and RCsB. There was a significant difference between heavy media multitaskers (HMM), average media multitaskers (AMM), and light media multitaskers (LMM) in terms of RCsB, with HMM demonstrating more frequent risky behaviors than LMM or AMM. The HMM group also reported more cognitive failures in everyday life than the LMM group. A regression analysis showed that everyday cognitive failures and MMT acted as significant predictors for RCsB. These results expand our current understanding of the relationship between human factors and cybersecurity behaviors, which are useful to inform the design of training and intervention packages to mitigate RCsB. PMID:29638157
Multitasking in multiple sclerosis: can it inform vocational functioning?

PubMed

Morse, Chelsea L; Schultheis, Maria T; McKeever, Joshua D; Leist, Thomas

2013-12-01

To examine associations between multitasking ability defined by performance on a complex task integrating multiple cognitive domains and vocational functioning in multiple sclerosis (MS). Survey data collection. Laboratory with referrals from an outpatient clinic. Community-dwelling individuals with MS (N=30) referred between October 2011 and June 2012. Not applicable. The modified Six Elements Test (SET) to measure multitasking ability, Fatigue Severity Scale to measure fatigue, several neuropsychological measures of executive functioning, and vocational status. Among the sample, 60% of individuals have reduced their work hours because of MS symptoms (cutback employment group) and 40% had maintained their work hours. Among both groups, SET performance was significantly associated with performance on several measures of neuropsychological functioning. Individuals in the cutback employment group demonstrated significantly worse overall performance on the SET (P=.041). Logistic regression was used to evaluate associations between SET performance and vocational status, while accounting for neuropsychological performance and fatigue. The overall model was significant (χ(2)3=8.65, P=.032), with fatigue [Exp(B)=.83, P=.01] and multitasking ability [Exp(B)=.60, P=.043] retained as significant predictors. Multitasking ability may play an important role in performance at work for individuals with MS. Given that multitasking was associated with vocational functioning, future efforts should assess the usefulness of incorporating multitasking ability into rehabilitation planning. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints

DTIC Science & Technology

2013-06-16

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

PubMed Central

Huo, Zhouyuan; Shen, Dinggang

2018-01-01

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896
Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

PubMed

Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo

2018-02-15

Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.
Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.
A Null Relationship between Media Multitasking and Well-Being

PubMed Central

Shih, Shui-I

2013-01-01

There is a rapidly increasing trend in media-media multitasking or MMM (using two or more media concurrently). In a recent conference, scholars from diverse disciplines expressed concerns that indulgence in MMM may compromise well-being and/or cognitive abilities. However, research on MMM's impacts is too sparse to inform the general public and policy makers whether MMM should be encouraged, managed, or minimized. The primary purpose of the present study was to develop an innovative computerized instrument – the Survey of the Previous Day (SPD) – to quantify MMM as well as media-nonmedia and nonmedia-nonmedia multitasking and sole-tasking. The secondary purpose was to examine whether these indices could predict a sample of well-being related, psychosocial measures. In the SPD, participants first recalled (typed) what they did during each hour of the previous day. In later parts of the SPD, participants analysed activities and their timing and duration for each hour of the previous day, while relevant recall was on display. Participants also completed the Media Use Questionnaire. The results showed non-significant relationship between tasking measures and well-being related measures. Given how little is known about the associations between MMM and well-being, the null results may offer some general reassurance to those who are apprehensive about negative impacts of MMM. PMID:23691236
Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

PubMed

Li, Xiang; Xu, Youjun; Lai, Luhua; Pei, Jianfeng

2018-05-30

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .
Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.
Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

2014-03-01

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
The Relationship Between Trait Procrastination, Internet Use, and Psychological Functioning: Results From a Community Sample of German Adolescents.

PubMed

Reinecke, Leonard; Meier, Adrian; Beutel, Manfred E; Schemer, Christian; Stark, Birgit; Wölfling, Klaus; Müller, Kai W

2018-01-01

Adolescents with a strong tendency for irrational task delay (i.e., high trait procrastination) may be particularly prone to use Internet applications simultaneously to other tasks (e.g., during homework) and in an insufficiently controlled fashion. Both Internet multitasking and insufficiently controlled Internet usage may thus amplify the negative mental health implications that have frequently been associated with trait procrastination. The present study explored this role of Internet multitasking and insufficiently controlled Internet use for the relationship between trait procrastination and impaired psychological functioning in a community sample of N = 818 early and middle adolescents. Results from multiple regression analyses indicate that trait procrastination was positively related to Internet multitasking and insufficiently controlled Internet use. Insufficiently controlled Internet use, but not Internet multitasking, was found to partially statistically mediate the association between trait procrastination and adolescents' psychological functioning (i.e., stress, sleep quality, and relationship satisfaction with parents). The study underlines that adolescents with high levels of trait procrastination may have an increased risk for negative outcomes of insufficiently controlled Internet use.
The Relationship Between Trait Procrastination, Internet Use, and Psychological Functioning: Results From a Community Sample of German Adolescents

PubMed Central

Reinecke, Leonard; Meier, Adrian; Beutel, Manfred E.; Schemer, Christian; Stark, Birgit; Wölfling, Klaus; Müller, Kai W.

2018-01-01

Adolescents with a strong tendency for irrational task delay (i.e., high trait procrastination) may be particularly prone to use Internet applications simultaneously to other tasks (e.g., during homework) and in an insufficiently controlled fashion. Both Internet multitasking and insufficiently controlled Internet usage may thus amplify the negative mental health implications that have frequently been associated with trait procrastination. The present study explored this role of Internet multitasking and insufficiently controlled Internet use for the relationship between trait procrastination and impaired psychological functioning in a community sample of N = 818 early and middle adolescents. Results from multiple regression analyses indicate that trait procrastination was positively related to Internet multitasking and insufficiently controlled Internet use. Insufficiently controlled Internet use, but not Internet multitasking, was found to partially statistically mediate the association between trait procrastination and adolescents’ psychological functioning (i.e., stress, sleep quality, and relationship satisfaction with parents). The study underlines that adolescents with high levels of trait procrastination may have an increased risk for negative outcomes of insufficiently controlled Internet use. PMID:29942268
Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

PubMed

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

2015-03-01

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

PubMed

Kapellusch, Jay M; Silverstein, Barbara A; Bao, Stephen S; Thiese, Mathew S; Merryweather, Andrew S; Hegmann, Kurt T; Garg, Arun

2018-02-01

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value for hand activity level (TLV for HAL) have been shown to be associated with prevalence of distal upper-limb musculoskeletal disorders such as carpal tunnel syndrome (CTS). The SI and TLV for HAL disagree on more than half of task exposure classifications. Similarly, time-weighted average (TWA), peak, and typical exposure techniques used to quantity physical exposure from multi-task jobs have shown between-technique agreement ranging from 61% to 93%, depending upon whether the SI or TLV for HAL model was used. This study compared exposure-response relationships between each model-technique combination and prevalence of CTS. Physical exposure data from 1,834 workers (710 with multi-task jobs) were analyzed using the SI and TLV for HAL and the TWA, typical, and peak multi-task job exposure techniques. Additionally, exposure classifications from the SI and TLV for HAL were combined into a single measure and evaluated. Prevalent CTS cases were identified using symptoms and nerve-conduction studies. Mixed effects logistic regression was used to quantify exposure-response relationships between categorized (i.e., low, medium, and high) physical exposure and CTS prevalence for all model-technique combinations, and for multi-task workers, mono-task workers, and all workers combined. Except for TWA TLV for HAL, all model-technique combinations showed monotonic increases in risk of CTS with increased physical exposure. The combined-models approach showed stronger association than the SI or TLV for HAL for multi-task workers. Despite differences in exposure classifications, nearly all model-technique combinations showed exposure-response relationships with prevalence of CTS for the combined sample of mono-task and multi-task workers. Both the TLV for HAL and the SI, with the TWA or typical techniques, appear useful for epidemiological studies and surveillance. However, the utility of TWA, typical, and peak techniques for job design and intervention is dubious.
Deep ensemble learning of sparse regression models for brain disease diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.
Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2018-01-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394
Using Deep Learning for Compound Selectivity Prediction.

PubMed

Zhang, Ruisheng; Li, Juan; Lu, Jingjing; Hu, Rongjing; Yuan, Yongna; Zhao, Zhili

2016-01-01

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.
Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

Multitasking the Davidson algorithm for the large, sparse eigenvalue problem

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

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

1989-01-01

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

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.
Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611
Media multitasking and implicit learning.

PubMed

Edwards, Kathleen S; Shin, Myoungju

2017-07-01

Media multitasking refers to the simultaneous use of different forms of media. Previous research comparing heavy media multitaskers and light media multitaskers suggests that heavy media multitaskers have a broader scope of attention. The present study explored whether these differences in attentional scope would lead to a greater degree of implicit learning for heavy media multitaskers. The study also examined whether media multitasking behaviour is associated with differences in visual working memory, and whether visual working memory differentially affects the ability to process contextual information. In addition to comparing extreme groups (heavy and light media multitaskers) the study included analysis of people who media multitask in moderation (intermediate media multitaskers). Ninety-four participants were divided into groups based on responses to the media use questionnaire, and completed the contextual cueing and n-back tasks. Results indicated that the speed at which implicit learning occurred was slower in heavy media multitaskers relative to both light and intermediate media multitaskers. There was no relationship between working memory performance and media multitasking group, and no relationship between working memory and implicit learning. There was also no evidence for superior performance of intermediate media multitaskers. A deficit in implicit learning observed in heavy media multitaskers is consistent with previous literature, which suggests that heavy media multitaskers perform more poorly than light media multitaskers in attentional tasks due to their wider attentional scope.
Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking.

PubMed

Sanbonmatsu, David M; Strayer, David L; Medeiros-Ward, Nathan; Watson, Jason M

2013-01-01

The present study examined the relationship between personality and individual differences in multi-tasking ability. Participants enrolled at the University of Utah completed measures of multi-tasking activity, perceived multi-tasking ability, impulsivity, and sensation seeking. In addition, they performed the Operation Span in order to assess their executive control and actual multi-tasking ability. The findings indicate that the persons who are most capable of multi-tasking effectively are not the persons who are most likely to engage in multiple tasks simultaneously. To the contrary, multi-tasking activity as measured by the Media Multitasking Inventory and self-reported cell phone usage while driving were negatively correlated with actual multi-tasking ability. Multi-tasking was positively correlated with participants' perceived ability to multi-task ability which was found to be significantly inflated. Participants with a strong approach orientation and a weak avoidance orientation--high levels of impulsivity and sensation seeking--reported greater multi-tasking behavior. Finally, the findings suggest that people often engage in multi-tasking because they are less able to block out distractions and focus on a singular task. Participants with less executive control--low scorers on the Operation Span task and persons high in impulsivity--tended to report higher levels of multi-tasking activity.
Who Multitasks on Smartphones? Smartphone Multitaskers' Motivations and Personality Traits.

PubMed

Lim, Sohye; Shim, Hongjin

2016-03-01

This study aimed to explore the psychological determinants of smartphone multitasking. Smartphone multitasking comprises the following three different subtypes: multitasking with nonmedia activities, cross-media multitasking with nonsmartphone media, and single-device multitasking within the smartphone. The primary motivations for smartphone multitasking were first identified--efficiency, utility, and positive affect--and the ways in which they are associated with the three subtypes were examined; among the primary motivations, efficiency and positive affect predicted the degree of total smartphone-multitasking behavior. The personality traits that are pertinent to all of the primary motivations--need for cognition (NFC) and sensation seeking (SS)--were also investigated. Further analyses revealed that the motivations for and the extent of smartphone multitasking can vary as functions of a user's NFC and SS. In this study, NFC was not only a meaningful predictor of the cognitive needs that drive smartphone multitasking but also increased the likelihood of multitasking through its interaction with SS.
A compressive sensing-based computational method for the inversion of wide-band ground penetrating radar data

NASA Astrophysics Data System (ADS)

Gelmini, A.; Gottardi, G.; Moriyama, T.

2017-10-01

This work presents an innovative computational approach for the inversion of wideband ground penetrating radar (GPR) data. The retrieval of the dielectric characteristics of sparse scatterers buried in a lossy soil is performed by combining a multi-task Bayesian compressive sensing (MT-BCS) solver and a frequency hopping (FH) strategy. The developed methodology is able to benefit from the regularization capabilities of the MT-BCS as well as to exploit the multi-chromatic informative content of GPR measurements. A set of numerical results is reported in order to assess the effectiveness of the proposed GPR inverse scattering technique, as well as to compare it to a simpler single-task implementation.
Multitasking simulation: Present application and future directions.

PubMed

Adams, Traci Nicole; Rho, Jason C

2017-02-01

The Accreditation Council for Graduate Medical Education lists multi-tasking as a core competency in several medical specialties due to increasing demands on providers to manage the care of multiple patients simultaneously. Trainees often learn multitasking on the job without any formal curriculum, leading to high error rates. Multitasking simulation training has demonstrated success in reducing error rates among trainees. Studies of multitasking simulation demonstrate that this type of simulation is feasible, does not hinder the acquisition of procedural skill, and leads to better performance during subsequent periods of multitasking. Although some healthcare agencies have discouraged multitasking due to higher error rates among multitasking providers, it cannot be eliminated entirely in settings such as the emergency department in which providers care for more than one patient simultaneously. Simulation can help trainees to identify situations in which multitasking is inappropriate, while preparing them for situations in which multitasking is inevitable.
Multiplicative Multitask Feature Learning

PubMed Central

Wang, Xin; Bi, Jinbo; Yu, Shipeng; Sun, Jiangwen; Song, Minghu

2016-01-01

We investigate a general framework of multiplicative multitask feature learning which decomposes individual task’s model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. PMID:28428735
Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.
Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...
MultitaskProtDB: a database of multitasking proteins.

PubMed

Hernández, Sergio; Ferragut, Gabriela; Amela, Isaac; Perez-Pons, JosepAntoni; Piñol, Jaume; Mozo-Villarias, Angel; Cedano, Juan; Querol, Enrique

2014-01-01

We have compiled MultitaskProtDB, available online at http://wallace.uab.es/multitask, to provide a repository where the many multitasking proteins found in the literature can be stored. Multitasking or moonlighting is the capability of some proteins to execute two or more biological functions. Usually, multitasking proteins are experimentally revealed by serendipity. This ability of proteins to perform multitasking functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Even so, the study of this phenomenon is complex because, among other things, there is no database of moonlighting proteins. The existence of such a tool facilitates the collection and dissemination of these important data. This work reports the database, MultitaskProtDB, which is designed as a friendly user web page containing >288 multitasking proteins with their NCBI and UniProt accession numbers, canonical and additional biological functions, monomeric/oligomeric states, PDB codes when available and bibliographic references. This database also serves to gain insight into some characteristics of multitasking proteins such as frequencies of the different pairs of functions, phylogenetic conservation and so forth.
Teaching and evaluating multitasking ability in emergency medicine residents - what is the best practice?

PubMed Central

2014-01-01

Multitasking is an essential skill to develop during Emergency Medicine (EM) residency. Residents who struggle to cope in a multitasking environment risk fatigue, stress, and burnout. Improper management of interruption has been causally linked with medical errors. Formal teaching and evaluation of multitasking is often lacking in EM residency programs. This article reviewed the literature on multitasking in EM to identify best practices for teaching and evaluating multitasking amongst EM residents. With the advancement in understanding of what multitasking is, deliberate attempts should be made to teach residents pitfalls and coping strategies. This can be taught through a formal curriculum, role modeling by faculty, and simulation training. The best way to evaluate multitasking ability in residents is by direct observation. The EM Milestone Project provides a framework by which multitasking can be evaluated. EM residents should be deployed in work environments commiserate with their multitasking ability and their progress should be graduated after identified deficiencies are remediated. PMID:25635201
Teaching and evaluating multitasking ability in emergency medicine residents - what is the best practice?

PubMed

Heng, Kenneth Wj

2014-01-01

Multitasking is an essential skill to develop during Emergency Medicine (EM) residency. Residents who struggle to cope in a multitasking environment risk fatigue, stress, and burnout. Improper management of interruption has been causally linked with medical errors. Formal teaching and evaluation of multitasking is often lacking in EM residency programs. This article reviewed the literature on multitasking in EM to identify best practices for teaching and evaluating multitasking amongst EM residents. With the advancement in understanding of what multitasking is, deliberate attempts should be made to teach residents pitfalls and coping strategies. This can be taught through a formal curriculum, role modeling by faculty, and simulation training. The best way to evaluate multitasking ability in residents is by direct observation. The EM Milestone Project provides a framework by which multitasking can be evaluated. EM residents should be deployed in work environments commiserate with their multitasking ability and their progress should be graduated after identified deficiencies are remediated.
STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.
STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560
Task Speed and Accuracy Decrease When Multitasking

ERIC Educational Resources Information Center

Lin, Lin; Cockerham, Deborah; Chang, Zhengsi; Natividad, Gloria

2016-01-01

As new technologies increase the opportunities for multitasking, the need to understand human capacities for multitasking continues to grow stronger. Is multitasking helping us to be more efficient? This study investigated the multitasking abilities of 168 participants, ages 6-72, by measuring their task accuracy and completion time when they…
Multitasking With Television Among Adolescents

PubMed Central

Christensen, Claire G.; Bickham, David; Ross, Craig S.; Rich, Michael

2015-01-01

Using Ecological Momentary Assessment, we explored predictors of adolescents’ television (TV) multitasking behaviors. We investigated whether demographic characteristics (age, gender, race/ethnicity, and maternal education) predict adolescents’ likelihood of multitasking with TV. We also explored whether characteristics of the TV-multitasking moment (affect, TV genre, attention to people, and media multitasking) predict adolescents’ likelihood of paying primary versus secondary attention to TV. Demographic characteristics do not predict TV multitasking. In TV-multitasking moments, primary attention to TV was more likely if adolescents experienced negative affect, watched a drama, or attended to people; it was less likely if they used computers or video games. PMID:26549930
Multitasking With Television Among Adolescents.

PubMed

Christensen, Claire G; Bickham, David; Ross, Craig S; Rich, Michael

Using Ecological Momentary Assessment, we explored predictors of adolescents' television (TV) multitasking behaviors. We investigated whether demographic characteristics (age, gender, race/ethnicity, and maternal education) predict adolescents' likelihood of multitasking with TV. We also explored whether characteristics of the TV-multitasking moment (affect, TV genre, attention to people, and media multitasking) predict adolescents' likelihood of paying primary versus secondary attention to TV. Demographic characteristics do not predict TV multitasking. In TV-multitasking moments, primary attention to TV was more likely if adolescents experienced negative affect, watched a drama, or attended to people; it was less likely if they used computers or video games.
MultitaskProtDB: a database of multitasking proteins

PubMed Central

Hernández, Sergio; Ferragut, Gabriela; Amela, Isaac; Perez-Pons, JosepAntoni; Piñol, Jaume; Mozo-Villarias, Angel; Cedano, Juan; Querol, Enrique

2014-01-01

We have compiled MultitaskProtDB, available online at http://wallace.uab.es/multitask, to provide a repository where the many multitasking proteins found in the literature can be stored. Multitasking or moonlighting is the capability of some proteins to execute two or more biological functions. Usually, multitasking proteins are experimentally revealed by serendipity. This ability of proteins to perform multitasking functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Even so, the study of this phenomenon is complex because, among other things, there is no database of moonlighting proteins. The existence of such a tool facilitates the collection and dissemination of these important data. This work reports the database, MultitaskProtDB, which is designed as a friendly user web page containing >288 multitasking proteins with their NCBI and UniProt accession numbers, canonical and additional biological functions, monomeric/oligomeric states, PDB codes when available and bibliographic references. This database also serves to gain insight into some characteristics of multitasking proteins such as frequencies of the different pairs of functions, phylogenetic conservation and so forth. PMID:24253302

Managing competing demands through task-switching and multitasking: a multi-setting observational study of 200 clinicians over 1000 hours.

PubMed

Walter, Scott R; Li, Ling; Dunsmuir, William T M; Westbrook, Johanna I

2014-03-01

To provide a detailed characterisation of clinicians' work management strategies. 1002.3 h of observational data were derived from three previous studies conducted in a teaching hospital in Sydney, Australia, among emergency department (ED) doctors (n=40), ward doctors (n=57) and ward nurses (n=104). The rates of task-switching (pausing a task to handle an incoming task) and multitasking (adding a task in parallel to an existing task) were compared in each group. Random intercepts logistic regression was used to determine factors significantly associated with clinicians' use of task-switching over multitasking and to quantify variation between individual clinicians. Task-switching rates were higher among ED doctors (6.0 per hour) than ward staff (2.2 and 1.8 per hour for doctors and nurses, respectively) and vice versa for multitasking rates (9.2 vs 17.3 and 14.1 per hour). Clinicians' strategy use was significantly related to the nature and complexity of work and to the person they were working with. In some settings, time of day, day of the week or previous chosen strategy affected a clinician's strategy. Independent of these factors, there was significant variation between individual clinicians in their use of strategies in a given situation (ED doctors p=0.04, ward staff p=0.03). Despite differences in factors associated with work management strategy use among ED doctors, ward doctors and ward nurses, clinicians in all settings appeared to prioritise certain types of tasks over others. Documentation was generally given low priority in all groups, while the arrival of direct care tasks tended to be treated with high priority. These findings suggest that considerations of safety may be implicit in task-switching and multitasking decisions. Although these strategies have been cast in a negative light, future research should consider their role in optimising competing quality and efficiency demands.
Multitasking Web Searching and Implications for Design.

ERIC Educational Resources Information Center

Ozmutlu, Seda; Ozmutlu, H. C.; Spink, Amanda

2003-01-01

Findings from a study of users' multitasking searches on Web search engines include: multitasking searches are a noticeable user behavior; multitasking search sessions are longer than regular search sessions in terms of queries per session and duration; both Excite and AlltheWeb.com users search for about three topics per multitasking session and…
Media multitasking and failures of attention in everyday life.

PubMed

Ralph, Brandon C W; Thomson, David R; Cheyne, James Allan; Smilek, Daniel

2014-09-01

Using a series of online self-report measures, we examine media multitasking, a particularly pervasive form of multitasking, and its relations to three aspects of everyday attention: (1) failures of attention and cognitive errors (2) mind wandering, and (3) attentional control with an emphasis on attentional switching and distractibility. We observed a positive correlation between levels of media multitasking and self-reports of attentional failures, as well as with reports of both spontaneous and deliberate mind wandering. No correlation was observed between media multitasking and self-reported memory failures, lending credence to the hypothesis that media multitasking may be specifically related to problems of inattention, rather than cognitive errors in general. Furthermore, media multitasking was not related with self-reports of difficulties in attention switching or distractibility. We offer a plausible causal structural model assessing both direct and indirect effects among media multitasking, attentional failures, mind wandering, and cognitive errors, with the heuristic goal of constraining and motivating theories of the effects of media multitasking on inattention.
Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.
Cognitive control in media multitaskers

PubMed Central

Ophir, Eyal; Nass, Clifford; Wagner, Anthony D.

2009-01-01

Chronic media multitasking is quickly becoming ubiquitous, although processing multiple incoming streams of information is considered a challenge for human cognition. A series of experiments addressed whether there are systematic differences in information processing styles between chronically heavy and light media multitaskers. A trait media multitasking index was developed to identify groups of heavy and light media multitaskers. These two groups were then compared along established cognitive control dimensions. Results showed that heavy media multitaskers are more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory. This led to the surprising result that heavy media multitaskers performed worse on a test of task-switching ability, likely due to reduced ability to filter out interference from the irrelevant task set. These results demonstrate that media multitasking, a rapidly growing societal trend, is associated with a distinct approach to fundamental information processing. PMID:19706386
Take a Break: Examining College Students' Media Multitasking Activities and Motivations during Study- or Work-Related Tasks

ERIC Educational Resources Information Center

Kononova, Anastasia G.; Yuan, Shupei

2017-01-01

A survey (N = 524) examined how frequently college students engage in multitasking with social media, texting/instant messaging (IM), and music while studying/working and what motivates them to multitask with each medium. Four out of five participants multitasked with Facebook and texting/IM, and two out of three multitasked with music. Habit was…
Structural neural correlates of multitasking: A voxel-based morphometry study.

PubMed

Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

2016-12-01

Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.
FORTRAN multitasking library for use on the ELXSI 6400 and the CRAY XMP

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

Montry, G.R.

1985-07-16

A library of FORTRAN-based multitasking routines has been written for the ELXSI 6400 and the CRAY XMP. This library is designed to make multitasking codes easily transportable between machines with different hardware configurations. The library provides enhanced error checking and diagnostics over vendor-supplied multitasking intrinsics. The library also contains multitasking control structures not normally supplied by the vendor.
I want to media multitask and I want to do it now: Individual differences in media multitasking predict delay of gratification and system-1 thinking.

PubMed

Schutten, Dan; Stokes, Kirk A; Arnell, Karen M

2017-01-01

Media multitasking, the concurrent use of multiple media forms, has been shown to be related to greater self-reported impulsivity and less self-control. These measures are both hallmarks of the need for immediate gratification which has been associated with fast, intuitive 'system-1' decision making, as opposed to more deliberate and effortful 'system-2' decision making. In Study 1, we used the Cognitive Reflection Task (CRT) to examine whether individuals who engage heavily in media multitasking differ from those who are light media multitaskers in their degree of system-1 versus system-2 thinking. In Study 2 we examined whether heavy and light media multitaskers differ in delay of gratification, using the delay discounting measure which estimates the preference for smaller immediate rewards, relative to larger delayed rewards in a hypothetical monetary choice task. We found that heavy media multitaskers were more likely than light media multitaskers to endorse intuitive, but wrong, decisions on the CRT indicating a greater reliance on 'system-1' thinking. Heavy media multitaskers were also willing to settle for less money immediately relative to light media multitaskers who were more willing to wait for the larger delayed reward. These results suggest that heavy media multitaskers have a reactive decision-making style that promotes current desires (money, ease of processing) at the expense of accuracy and future rewards. These findings highlight the potential for heavy media multitaskers to be at risk for problematic behaviors associated with delay discounting - behaviors such as substance abuse, overeating, problematic gambling, and poor financial management.
Predictors of media multitasking in Chinese adolescents.

PubMed

Yang, Xiaohui; Zhu, Liqi

2016-12-01

We examined predictors of media multitasking in Chinese adolescents from 3 contexts: characteristics of the media user, types of media use and family media contexts. Three hundred and twenty adolescents, 11-18 years of age, completed questionnaires to measure media use, impulsivity, sensation seeking, time management disposition and family media environment. The results showed that media multitasking was positively correlated with age and total media use time. Participants with high levels of impulsivity and sensation seeking reported more multitasking behaviour. Multitasking was negatively correlated with time management. Children from media-oriented families often engage in more multitasking. What's more, social networking sites use and music use can mediate the effect of individual and family factors on media multitasking. © 2015 International Union of Psychological Science.
Media multitasking and behavioral measures of sustained attention.

PubMed

Ralph, Brandon C W; Thomson, David R; Seli, Paul; Carriere, Jonathan S A; Smilek, Daniel

2015-02-01

In a series of four studies, self-reported media multitasking (using the media multitasking index; MMI) and general sustained-attention ability, through performance on three sustained-attention tasks: the metronome response task (MRT), the sustained-attention-to-response task (SART), and a vigilance task (here, a modified version of the SART). In Study 1, we found that higher reports of media multitasking were associated with increased response variability (i.e., poor performance) on the MRT. However, in Study 2, no association between reported media multitasking and performance on the SART was observed. These findings were replicated in Studies 3a and 3b, in which we again assessed the relation between media multitasking and performance on both the MRT and SART in two large online samples. Finally, in Study 4, using a large online sample, we tested whether media multitasking was associated with performance on a vigilance task. Although standard vigilance decrements were observed in both sensitivity (A') and response times, media multitasking was not associated with the size of these decrements, nor was media multitasking associated with overall performance, in terms of either sensitivity or response times. Taken together, the results of the studies reported here failed to demonstrate a relation between habitual engagement in media multitasking in everyday life and a general deficit in sustained-attention processes.
Nurses' perceptions of multitasking in the emergency department: effective, fun and unproblematic (at least for me) – a qualitative study.

PubMed

Forsberg, Helena Hvitfeldt; Muntlin Athlin, Åsa; von Thiele Schwarz, Ulrica

2015-04-01

The aim was to understand how multitasking is experienced by registered nurses and how it relates to their everyday practice in the emergency department. Interviews with open-ended questions were conducted with registered nurses (n = 9) working in one of two included emergency departments in Sweden. Data were analyzed using Schilling's structured model for qualitative content analysis. Three core concepts related to multitasking emerged from the interviews: 'multitasking - an attractive prerequisite for ED care'; 'multitasking implies efficiency' and 'multitasking is not stressful'. From these core concepts an additional theme emerged: '… and does not cause errors – at least for me', related to patient safety. This study shows how the patient load and the unreflected multitasking that follows relate to nurses' perceived efficiency and job satisfaction. It also shows that the relationship between multitasking and errors is perceived to be mediated by whom the actor is, and his or her level of experience. Findings from this study add value to the discourse on multitasking and the emergency department context, as few studies go beyond examining the quantitative aspect of interruptions and multitasking and how it is experienced by the staff in their everyday practice. Copyright © 2014 Elsevier Ltd. All rights reserved.
Media multitasking and memory: Differences in working memory and long-term memory

PubMed Central

Thieu, Monica K.; Wagner, Anthony D.

2015-01-01

Increasing access to media in the 21st century has led to a rapid rise in the prevalence of media multitasking (simultaneous use of multiple media streams). Such behavior is associated with various cognitive differences, such as difficulty filtering distracting information and increased trait impulsivity. Given the rise in media multitasking by children, adolescents, and adults, a full understanding of the cognitive profile of media multitaskers is imperative. Here we investigated the relationship between chronic media multitasking and working memory (WM) and long-term memory (LTM) performance. Four key findings are reported (1) heavy media multitaskers (HMMs) exhibited lower WM performance, regardless of whether external distraction was present or absent; (2) lower performance on multiple WM tasks predicted lower LTM performance; (3) media multitasking-related differences in memory reflected differences in discriminability rather than decision bias; and (4) attentional impulsivity correlated with media multitasking behavior and reduced WM performance. These findings suggest that chronic media multitasking is associated with a wider attentional scope/higher attentional impulsivity, which may allow goal-irrelevant information to compete with goal-relevant information. As a consequence, heavy media multitaskers are able to hold fewer or less precise goal-relevant representations in WM. HMMs’ wider attentional scope, combined with their diminished WM performance, propagates forward to yield lower LTM performance. As such, chronic media multitasking is associated with a reduced ability to draw on the past—be it very recent or more remote—to inform present behavior. PMID:26223469
Individual differences in multitasking ability and adaptability.

PubMed

Morgan, Brent; D'Mello, Sidney; Abbott, Robert; Radvansky, Gabriel; Haass, Michael; Tamplin, Andrea

2013-08-01

The aim of this study was to identify the cognitive factors that predictability and adaptability during multitasking with a flight simulator. Multitasking has become increasingly prevalent as most professions require individuals to perform multiple tasks simultaneously. Considerable research has been undertaken to identify the characteristics of people (i.e., individual differences) that predict multitasking ability. Although working memory is a reliable predictor of general multitasking ability (i.e., performance in normal conditions), there is the question of whether different cognitive faculties are needed to rapidly respond to changing task demands (adaptability). Participants first completed a battery of cognitive individual differences tests followed by multitasking sessions with a flight simulator. After a baseline condition, difficulty of the flight simulator was incrementally increased via four experimental manipulations, and performance metrics were collected to assess multitasking ability and adaptability. Scholastic aptitude and working memory predicted general multitasking ability (i.e., performance at baseline difficulty), but spatial manipulation (in conjunction with working memory) was a major predictor of adaptability (performance in difficult conditions after accounting for baseline performance). Multitasking ability and adaptability may be overlapping but separate constructs that draw on overlapping (but not identical) sets of cognitive abilities. The results of this study are applicable to practitioners and researchers in human factors to assess multitasking performance in real-world contexts and with realistic task constraints. We also present a framework for conceptualizing multitasking adaptability on the basis of five adaptability profiles derived from performance on tasks with consistent versus increased difficulty.
Media multitasking and memory: Differences in working memory and long-term memory.

PubMed

Uncapher, Melina R; K Thieu, Monica; Wagner, Anthony D

2016-04-01

Increasing access to media in the 21st century has led to a rapid rise in the prevalence of media multitasking (simultaneous use of multiple media streams). Such behavior is associated with various cognitive differences, such as difficulty filtering distracting information and increased trait impulsivity. Given the rise in media multitasking by children, adolescents, and adults, a full understanding of the cognitive profile of media multitaskers is imperative. Here we investigated the relationship between chronic media multitasking and working memory (WM) and long-term memory (LTM) performance. Four key findings are reported (1) heavy media multitaskers (HMMs) exhibited lower WM performance, regardless of whether external distraction was present or absent; (2) lower performance on multiple WM tasks predicted lower LTM performance; (3) media multitasking-related differences in memory reflected differences in discriminability rather than decision bias; and (4) attentional impulsivity correlated with media multitasking behavior and reduced WM performance. These findings suggest that chronic media multitasking is associated with a wider attentional scope/higher attentional impulsivity, which may allow goal-irrelevant information to compete with goal-relevant information. As a consequence, heavy media multitaskers are able to hold fewer or less precise goal-relevant representations in WM. HMMs' wider attentional scope, combined with their diminished WM performance, propagates forward to yield lower LTM performance. As such, chronic media multitasking is associated with a reduced ability to draw on the past--be it very recent or more remote--to inform present behavior.
Neural sources of performance decline during continuous multitasking

PubMed Central

Al-Hashimi, Omar; Zanto, Theodore P.; Gazzaley, Adam

2018-01-01

Multitasking performance costs have largely been characterized by experiments that involve two overlapping and punctuated perceptual stimuli, as well as punctuated responses to each task. Here, participants engaged in a continuous performance paradigm during fMRI recording to identify neural signatures associated with multitasking costs under more natural conditions. Our results demonstrated that only a single brain region, the superior parietal lobule (SPL), exhibited a significant relationship with multitasking performance, such that increased activation in the multitasking condition versus the singletasking condition was associated with higher task performance (i.e., least multitasking cost). Together, these results support previous research indicating that parietal regions underlie multitasking abilities and that performance costs are related to a bottleneck in control processes involving the SPL that serves to divide attention between two tasks. PMID:26159323
Does media multitasking always hurt? A positive correlation between multitasking and multisensory integration.

PubMed

Lui, Kelvin F H; Wong, Alan C-N

2012-08-01

Heavy media multitaskers have been found to perform poorly in certain cognitive tasks involving task switching, selective attention, and working memory. An account for this is that with a breadth-biased style of cognitive control, multitaskers tend to pay attention to various information available in the environment, without sufficient focus on the information most relevant to the task at hand. This cognitive style, however, may not cause a general deficit in all kinds of tasks. We tested the hypothesis that heavy media multitaskers would perform better in a multisensory integration task than would others, due to their extensive experience in integrating information from different modalities. Sixty-three participants filled out a questionnaire about their media usage and completed a visual search task with and without synchronous tones (pip-and-pop paradigm). It was found that a higher degree of media multitasking was correlated with better multisensory integration. The fact that heavy media multitaskers are not deficient in all kinds of cognitive tasks suggests that media multitasking does not always hurt.
SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844
Improving our understanding of multi-tasking in healthcare: Drawing together the cognitive psychology and healthcare literature.

PubMed

Douglas, Heather E; Raban, Magdalena Z; Walter, Scott R; Westbrook, Johanna I

2017-03-01

Multi-tasking is an important skill for clinical work which has received limited research attention. Its impacts on clinical work are poorly understood. In contrast, there is substantial multi-tasking research in cognitive psychology, driver distraction, and human-computer interaction. This review synthesises evidence of the extent and impacts of multi-tasking on efficiency and task performance from health and non-healthcare literature, to compare and contrast approaches, identify implications for clinical work, and to develop an evidence-informed framework for guiding the measurement of multi-tasking in future healthcare studies. The results showed healthcare studies using direct observation have focused on descriptive studies to quantify concurrent multi-tasking and its frequency in different contexts, with limited study of impact. In comparison, non-healthcare studies have applied predominantly experimental and simulation designs, focusing on interleaved and concurrent multi-tasking, and testing theories of the mechanisms by which multi-tasking impacts task efficiency and performance. We propose a framework to guide the measurement of multi-tasking in clinical settings that draws together lessons from these siloed research efforts. Copyright Â© 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Neural sources of performance decline during continuous multitasking.

PubMed

Al-Hashimi, Omar; Zanto, Theodore P; Gazzaley, Adam

2015-10-01

Multitasking performance costs have largely been characterized by experiments that involve two overlapping and punctuated perceptual stimuli, as well as punctuated responses to each task. Here, participants engaged in a continuous performance paradigm during fMRI recording to identify neural signatures associated with multitasking costs under more natural conditions. Our results demonstrated that only a single brain region, the superior parietal lobule (SPL), exhibited a significant relationship with multitasking performance, such that increased activation in the multitasking condition versus the singletasking condition was associated with higher task performance (i.e., least multitasking cost). Together, these results support previous research indicating that parietal regions underlie multitasking abilities and that performance costs are related to a bottleneck in control processes involving the SPL that serves to divide attention between two tasks. Copyright © 2015. Published by Elsevier Ltd.
Intelligence, Working Memory, and Multitasking Performance

ERIC Educational Resources Information Center

Colom, Roberto; Martinez-Molina, Agustin; Shih, Pei Chun; Santacreu, Jose

2010-01-01

Multitasking performance is relevant in everyday life and job analyses highlight the influence of multitasking over several diverse occupations. Intelligence is the best single predictor of overall job performance and it is also related to individual differences in multitasking. However, it has been shown that working memory capacity (WMC) is…
Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.
Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

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

2014-01-01

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

PubMed

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

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.
Multitasking Abilities in Adolescents With 22q11.2 Deletion Syndrome: Results From an Experimental Ecological Paradigm.

PubMed

Schneider, Maude; Eliez, Stephan; Birr, Julie; Menghetti, Sarah; Debbané, Martin; Van der Linden, Martial

2016-03-01

The 22q11.2 deletion syndrome (22q11.2DS) is associated with cognitive and functional impairments and increased risk for schizophrenia. We characterized multitasking abilities of adolescents with 22q11.2DS using an experimental naturalistic setting and examined whether multitasking impairments were associated with real-world functioning and negative symptoms. Thirty-nine adolescents (19 with 22q11.2DS and 20 controls) underwent the Multitasking Evaluation for Adolescents. Real-world functioning and clinical symptoms were assessed in participants with 22q11.2DS. Adolescents with 22q11.2DS performed poorly in the multitasking evaluation. Our data also suggest that multitasking abilities are related to adaptive functioning in the practical domain and negative symptoms. This study shows that adolescents with 22q11.2DS are characterized by multitasking impairments, which may be relevant for several aspects of the clinical phenotype.
Training conquers multitasking costs by dividing task representations in the frontoparietal-subcortical system

PubMed Central

Garner, K. G.; Dux, Paul E.

2015-01-01

Negotiating the information-rich sensory world often requires the concurrent management of multiple tasks. Despite this requirement, humans are thought to be poor at multitasking because of the processing limitations of frontoparietal and subcortical (FP-SC) brain regions. Although training is known to improve multitasking performance, it is unknown how the FP-SC system functionally changes to support improved multitasking. To address this question, we characterized the FP-SC changes that predict training outcomes using an individual differences approach. Participants (n = 100) performed single and multiple tasks in pre- and posttraining magnetic resonance imaging (fMRI) sessions interspersed by either a multitasking or an active-control training regimen. Multivoxel pattern analyses (MVPA) revealed that training induced multitasking improvements were predicted by divergence in the FP-SC blood oxygen level-dependent (BOLD) response patterns to the trained tasks. Importantly, this finding was only observed for participants who completed training on the component (single) tasks and their combination (multitask) and not for the control group. Therefore, the FP-SC system supports multitasking behavior by segregating constituent task representations. PMID:26460014
“Women Are Better Than Men”–Public Beliefs on Gender Differences and Other Aspects in Multitasking

PubMed Central

Szameitat, André J.; Hamaida, Yasmin; Tulley, Rebecca S.; Saylik, Rahmi; Otermans, Pauldy C. J.

2015-01-01

Reports in public media suggest the existence of a stereotype that women are better at multitasking than men. The present online survey aimed at supporting this incidental observation by empirical data. For this, 488 participants from various ethnic backgrounds (US, UK, Germany, the Netherlands, Turkey, and others) filled out a self-developed online-questionnaire. Results showed that overall more than 50% of the participants believed in gender differences in multitasking abilities. Of those who believed in gender differences, a majority of 80% believed that women were better at multitasking. The main reasons for this were believed to be an evolutionary advantage and more multitasking practice in women, mainly due to managing children and household and/or family and job. Findings were consistent across the different countries, thus supporting the existence of a widespread gender stereotype that women are better at multitasking than men. Further questionnaire results provided information about the participants’ self-rated own multitasking abilities, and how they conceived multitasking activities such as childcare, phoning while driving, and office work. PMID:26479359
Training conquers multitasking costs by dividing task representations in the frontoparietal-subcortical system.

PubMed

Garner, K G; Dux, Paul E

2015-11-17

Negotiating the information-rich sensory world often requires the concurrent management of multiple tasks. Despite this requirement, humans are thought to be poor at multitasking because of the processing limitations of frontoparietal and subcortical (FP-SC) brain regions. Although training is known to improve multitasking performance, it is unknown how the FP-SC system functionally changes to support improved multitasking. To address this question, we characterized the FP-SC changes that predict training outcomes using an individual differences approach. Participants (n = 100) performed single and multiple tasks in pre- and posttraining magnetic resonance imaging (fMRI) sessions interspersed by either a multitasking or an active-control training regimen. Multivoxel pattern analyses (MVPA) revealed that training induced multitasking improvements were predicted by divergence in the FP-SC blood oxygen level-dependent (BOLD) response patterns to the trained tasks. Importantly, this finding was only observed for participants who completed training on the component (single) tasks and their combination (multitask) and not for the control group. Therefore, the FP-SC system supports multitasking behavior by segregating constituent task representations.
"Women Are Better Than Men"-Public Beliefs on Gender Differences and Other Aspects in Multitasking.

PubMed

Szameitat, André J; Hamaida, Yasmin; Tulley, Rebecca S; Saylik, Rahmi; Otermans, Pauldy C J

2015-01-01

Reports in public media suggest the existence of a stereotype that women are better at multitasking than men. The present online survey aimed at supporting this incidental observation by empirical data. For this, 488 participants from various ethnic backgrounds (US, UK, Germany, the Netherlands, Turkey, and others) filled out a self-developed online-questionnaire. Results showed that overall more than 50% of the participants believed in gender differences in multitasking abilities. Of those who believed in gender differences, a majority of 80% believed that women were better at multitasking. The main reasons for this were believed to be an evolutionary advantage and more multitasking practice in women, mainly due to managing children and household and/or family and job. Findings were consistent across the different countries, thus supporting the existence of a widespread gender stereotype that women are better at multitasking than men. Further questionnaire results provided information about the participants' self-rated own multitasking abilities, and how they conceived multitasking activities such as childcare, phoning while driving, and office work.
Intimacy and Smartphone Multitasking-A New Oxymoron?

PubMed

Amichai-Hamburger, Yair; Etgar, Shir

2016-12-01

This study investigated the relationship between smartphone multitasking and romantic intimacy. Participants currently in a romantic relationship (N = 128; 98 women; M age = 26.7 years, SD = 4.3) filled out two sets of questionnaires: The Emotional Intimacy Scale, measuring romantic intimacy, and the mobile phone interference in life scale, measuring multitasking on a smartphone. Participants filled out each questionnaire twice, once in relation to themselves and once in relation to their partner (for the partner questionnaire, statements were altered from the first person to the third person singular, he/she instead of I). Results suggested that only the partners' smartphone multitasking scores were negatively related to ratings of romantic intimacy, whereas participants' own smartphone multitasking scores were not related to ratings of romantic intimacy. These results can be explained by the actor-observer asymmetry, suggesting that participants attributed their multitasking behaviors to situations, but attributed their partners multitasking behaviors to behavior patterns or intentionality. This research suggests that smartphone multitasking has a negative association with face-to-face interactions. People should attend to the costs of smartphone use during face-to-face interactions. © The Author(s) 2016.
Higher Media Multi-Tasking Activity Is Associated with Smaller Gray-Matter Density in the Anterior Cingulate Cortex

PubMed Central

Loh, Kep Kee; Kanai, Ryota

2014-01-01

Media multitasking, or the concurrent consumption of multiple media forms, is increasingly prevalent in today’s society and has been associated with negative psychosocial and cognitive impacts. Individuals who engage in heavier media-multitasking are found to perform worse on cognitive control tasks and exhibit more socio-emotional difficulties. However, the neural processes associated with media multi-tasking remain unexplored. The present study investigated relationships between media multitasking activity and brain structure. Research has demonstrated that brain structure can be altered upon prolonged exposure to novel environments and experience. Thus, we expected differential engagements in media multitasking to correlate with brain structure variability. This was confirmed via Voxel-Based Morphometry (VBM) analyses: Individuals with higher Media Multitasking Index (MMI) scores had smaller gray matter density in the anterior cingulate cortex (ACC). Functional connectivity between this ACC region and the precuneus was negatively associated with MMI. Our findings suggest a possible structural correlate for the observed decreased cognitive control performance and socio-emotional regulation in heavy media-multitaskers. While the cross-sectional nature of our study does not allow us to specify the direction of causality, our results brought to light novel associations between individual media multitasking behaviors and ACC structure differences. PMID:25250778
Individual differences in media multitasking and performance on the n-back.

PubMed

Ralph, Brandon C W; Smilek, Daniel

2017-02-01

A number of studies have recently examined the link between individual differences in media multitasking (using the MMI) and performance on working memory paradigms. However, these studies have yielded mixed results. Here we examine the relation between media multitasking and one particular working memory paradigm-the n-back (2- and 3-back)-improving upon previous research by (a) treating media multitasking as a continuous variable and adopting a correlational approach as well as (b) using a large sample of participants. First, we found that higher scores on the MMI were associated with a greater proportion of omitted trials on both the 2-back and 3-back, indicating that heavier media multitaskers were more disengaged during the n-back. In line with such a claim, heavier media multitaskers were also more likely to confess to responding randomly during various portions of the experiment, and to report media multitasking during the experiment itself. Importantly, when controlling for the relation between MMI scores and omissions, higher scores on the MMI were associated with an increase in false alarms, but not with a change in hits. These findings refine the extant literature on media multitasking and working memory performance (specifically, performance on the n-back), and suggest that media multitasking may be related to the propensity to disengage from ongoing tasks.
[Multitasking: An Asset or a “Time Trap”? Overview of Media Multitasking in Children and Adolescents].

PubMed

Frölich, Jan; Lehmkuhl, Gerd

2018-03-05

The development of modern digital media, especially smartphones, has contributed to a fundamental change in the leisure activities and communication practices of adolescents. Besides the technical possibilities, the amount of multitasking, i.e., the parallel use of several media alone or in combination with nonmedia activities, has gained in importance. This article addresses the bidirectional relationships between multitasking and cognitive processes, consequences for performance, and the potentially negative effects on psychosocial health. This review article is based on a Medline research involving studies and reviews published on multitasking in digital media since 2000 concerning adolescents and adults. Multitasking is involved in specific neuropsychological processes of the frontal cortex and, in part, the corpus striatum. Up to an individually defined level and an objectively defined performance capacity, multitasking does not necessarily haven a negative impact on the quality of work. However, if excessive individual or objective stress occurs, especially in very young children, respective reactions and negative consequences for psychosocial health occur. According to present research results, multitasking should not be exercised in tasks requiring complex cognitive conditions. Many further studies will be required to assess the relationship between multitasking and specific psychiatric diseases, especially addictive disorders and ADHD, but also its useful implementation in educational settings has to be explored.
Multitasking behaviors of osteopathic medical students.

PubMed

Shah, Ankit V; Mullens, Dustin J; Van Duyn, Lindsey J; Januchowski, Ronald P

2014-08-01

To the authors' knowledge, few studies have investigated the relationship between electronic media multitasking by undergraduate and graduate students during lecture and their academic performance, and reports that have looked into this behavior have neglected to investigate factors that may influence students' multitasking during lecture. To determine the extent to which medical students multitask during lecture; the types of multitasking; the frequency of multitasking and factors that influence frequency; and the correlation between multitasking and knowledge acquisition as assessed by a postlecture quiz. A 1-page survey assessing students' multitasking behavior was administered to 125 second-year students at Edward Via College of Osteopathic Medicine and collected at the onset of a standard 50-minute lecture. On completion of the 50-minute lecture, an unannounced 10-question multiple-choice quiz was given to assess knowledge acquisition during those lectures. On a separate date, after a standard 50-minute lecture, a second quiz was administered. The 1-page survey revealed that 98% of students check e-mail, 81% use social media, and 74% study for another class. Students spent the most time studying for another class (23 minutes) followed by using social media (13 minutes) and checking e-mail (7 minutes). The most influential factors behind multitasking were examination schedule (91%), lecturer (90%), and the number of lectures in the day (65%). The mean score for quiz 1 (the day after an examination) was 75%, and the mean score for quiz 2 (the day before an examination) was 60%. Multitasking during lecture is prominent among medical students, and examination schedule is the most influential factor. Although a robust drop in mean score on a lecture-based, unannounced quiz was identified 1 day before a scheduled examination, the effect from multitasking on this process remains unclear. © 2014 The American Osteopathic Association.
Multitasking Information Behaviour in Public Libraries: A Survey Study

ERIC Educational Resources Information Center

Spink, Amanda; Alvarado-Albertorio, Frances; Narayan, Bhuva; Brumfield, Jean; Park, Minsoo

2007-01-01

Multitasking information behaviour is the human ability to handle the demands of multiple information tasks concurrently. When we multitask, we work on two or more tasks and switch between those tasks. Multitasking is the way most of us deal with the complex environment we all live in, and recent studies show that people often engage in…
Multitasking in a data acquisition system

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

Larsen, J.E.

1980-01-01

Microprocessors and microcomputers have been employed widely in data acquisition applications due to low cost and the ease of adapting the microcomputer to changing or altered requirements. Multitasking offers ways of getting more performance from a microcomputer and also a means of designing a system which by its nature is easily changed to meet new requirements. The term multitasking is used to include definitions of multitasking and multiprogramming: multitasking-performing various related functions of the same job, e.g. data acquisition and data logging (recording); multiprogramming-performing possibly unrelated jobs concurrently.
Technology consumption and cognitive control: Contrasting action video game experience with media multitasking.

PubMed

Cardoso-Leite, Pedro; Kludt, Rachel; Vignola, Gianluca; Ma, Wei Ji; Green, C Shawn; Bavelier, Daphne

2016-01-01

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: (1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such a texting while watching TV) and (2) playing action video games (a particular subtype of video games). Previous work has outlined an association between high levels of media multitasking and specific deficits in handling distracting information, whereas playing action video games has been associated with enhanced attentional control. Because these two factors are linked with reasonably opposing effects, failing to take them jointly into account may result in inappropriate conclusions as to the impacts of technology use on attention. Across four tasks (AX-continuous performance, N-back, task-switching, and filter tasks), testing different aspects of attention and cognition, we showed that heavy media multitaskers perform worse than light media multitaskers. Contrary to previous reports, though, the performance deficit was not specifically tied to distractors, but was instead more global in nature. Interestingly, participants with intermediate levels of media multitasking sometimes performed better than both light and heavy media multitaskers, suggesting that the effects of increasing media multitasking are not monotonic. Action video game players, as expected, outperformed non-video-game players on all tasks. However, surprisingly, this was true only for participants with intermediate levels of media multitasking, suggesting that playing action video games does not protect against the deleterious effect of heavy media multitasking. Taken together, these findings show that media consumption can have complex and counterintuitive effects on attentional control.
Technology consumption and cognitive control: Contrasting action video game experience with media multitasking

PubMed Central

Cardoso-Leite, Pedro; Kludt, Rachel; Vignola, Gianluca; Ma, Wei Ji; Green, C. Shawn; Bavelier, Daphne

2015-01-01

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: 1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such a texting while watching TV) and 2) playing action video games (a particular sub-type of video game). Previous work has outlined an association between high levels of media multitasking and specific deficits in handling distracting information, while playing action video games has been associated with enhanced attentional control. As these two factors are linked with reasonably opposing effects, failing to take them jointly into account may result in inappropriate conclusions as to the impact of technology use on attention. Across four experiments (AX-CPT, N-back, Task-switching and Filter task), testing different aspects of attention and cognition, we show that heavy media multitaskers perform worse than light media multitaskers. Contrary to previous reports though, the performance deficit was not specifically tied to distractors, but was instead more global in nature. Interestingly, participants with intermediate levels of media multitasking occasionally performed better than both light and heavy media multitaskers suggesting that the effects of increasing media multitasking are not monotonic. Action video game players, as expected, outperformed non-video game players on all tasks. However, surprisingly this was true only for participants with intermediate levels of media multitasking, suggesting that playing action video games does not protect against the deleterious effect of heavy media multitasking. Taken together this study shows that media consumption can have complex and counter-intuitive effects on attentional control. PMID:26474982
Media Multitasking among American Youth: Prevalence, Predictors and Pairings

ERIC Educational Resources Information Center

Foehr, Ulla G.

2006-01-01

In the past, multitasking was a juggling act performed by busy adults, as they tried to manage jobs, chores, carpools, and PTA meetings. But recently, teens and tweens have turned into the real experts at multitasking, as their lives become chock-full of organized activities. For them, multitasking has simply become a way of life: "If I couldn't…

Media multitasking in adolescence.

PubMed

Cain, Matthew S; Leonard, Julia A; Gabrieli, John D E; Finn, Amy S

2016-12-01

Media use has been on the rise in adolescents overall, and in particular, the amount of media multitasking-multiple media consumed simultaneously, such as having a text message conversation while watching TV-has been increasing. In adults, heavy media multitasking has been linked with poorer performance on a number of laboratory measures of cognition, but no relationship has yet been established between media-multitasking behavior and real-world outcomes. Examining individual differences across a group of adolescents, we found that more frequent media multitasking in daily life was associated with poorer performance on statewide standardized achievement tests of math and English in the classroom, poorer performance on behavioral measures of executive function (working memory capacity) in the laboratory, and traits of greater impulsivity and lesser growth mindset. Greater media multitasking had a relatively circumscribed set of associations, and was not related to behavioral measures of cognitive processing speed, implicit learning, or manual dexterity, or to traits of grit and conscientiousness. Thus, individual differences in adolescent media multitasking were related to specific differences in executive function and in performance on real-world academic achievement measures: More media multitasking was associated with poorer executive function ability, worse academic achievement, and a reduced growth mindset.
The development of multitasking in children aged 7-12years: Evidence from cross-sectional and longitudinal data.

PubMed

Yang, Tian-Xiao; Xie, Weizhen; Chen, Chu-Sheng; Altgassen, Mareike; Wang, Ya; Cheung, Eric F C; Chan, Raymond C K

2017-09-01

This study investigated the development of multitasking ability across childhood. A sample of 65 typically developing children aged 7, 9, and 11years completed two multitasking tests across three time points within a year. Cross-sectional and longitudinal data consistently indicated continuous linear growth in children's multitasking ability. By the age of 12years, children could effectively perform a simple multitasking scenario comprising six equally important tasks, although their ability to strategically organize assorted tasks with varied values and priorities in a complex multitasking setting had not reached proficiency yet. Cognitive functions underlying a complex multitasking scenario varied in their developmental trajectories. Retrospective memory developed continuously from 7 to 12years of age, suggesting its supporting role in the development of multitasking. Planning skills developed slowly and showed practice effects for older children but not for younger children. The ability to adhere to plans also developed slowly, and children of all age groups benefited from practice. This study offers a preliminary benchmark for future comparison with clinical populations and may help to inform the development of targeted interventions. Copyright © 2017 Elsevier Inc. All rights reserved.
Functional brain networks reconstruction using group sparsity-regularized learning.

PubMed

Zhao, Qinghua; Li, Will X Y; Jiang, Xi; Lv, Jinglei; Lu, Jianfeng; Liu, Tianming

2018-06-01

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.
Multitasking runtime systems for the Cedar Multiprocessor

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

Guzzi, M.D.

1986-07-01

The programming of a MIMD machine is more complex than for SISD and SIMD machines. The multiple computational resources of the machine must be made available to the programming language compiler and to the programmer so that multitasking programs may be written. This thesis will explore the additional complexity of programming a MIMD machine, the Cedar Multiprocessor specifically, and the multitasking runtime system necessary to provide multitasking resources to the user. First, the problem will be well defined: the Cedar machine, its operating system, the programming language, and multitasking concepts will be described. Second, a solution to the problem, calledmore » macrotasking, will be proposed. This solution provides multitasking facilities to the programmer at a very coarse level with many visible machine dependencies. Third, an alternate solution, called microtasking, will be proposed. This solution provides multitasking facilities of a much finer grain. This solution does not depend so rigidly on the specific architecture of the machine. Finally, the two solutions will be compared for effectiveness. 12 refs., 16 figs.« less
Efficient multitasking: parallel versus serial processing of multiple tasks

PubMed Central

Fischer, Rico; Plessow, Franziska

2015-01-01

In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling. PMID:26441742
Gender differences in multitasking reflect spatial ability.

PubMed

Mäntylä, Timo

2013-04-01

Demands involving the scheduling and interleaving of multiple activities have become increasingly prevalent, especially for women in both their paid and unpaid work hours. Despite the ubiquity of everyday requirements to multitask, individual and gender-related differences in multitasking have gained minimal attention in past research. In two experiments, participants completed a multitasking session with four gender-fair monitoring tasks and separate tasks measuring executive functioning (working memory updating) and spatial ability (mental rotation). In both experiments, males outperformed females in monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of monitoring accuracy, but only spatial ability mediated gender differences in multitasking. Menstrual changes accentuated these effects, such that gender differences in multitasking (and spatial ability) were eliminated between males and females who were in the menstrual phase of the menstrual cycle but not between males and females who were in the luteal phase. These findings suggest that multitasking involves spatiotemporal task coordination and that gender differences in multiple-task performance reflect differences in spatial ability.
Short-term mindfulness intervention reduces the negative attentional effects associated with heavy media multitasking.

PubMed

Gorman, Thomas E; Green, C Shawn

2016-04-18

Recent research suggests that frequently switching between various forms of media (i.e. 'media multitasking') is associated with diminished attentional abilities, a disconcerting result given the prevalence of media multitasking in today's society. In the present study, we sought to investigate the extent to which the deficits associated with frequent media multitasking can be temporarily ameliorated via a short-term mindfulness intervention previously shown to produce beneficial effects on the attentional abilities of normally functioning individuals. Consistent with previous work, we found: (1) that heavy media multitaskers showed generally poorer attentional abilities than light media multitaskers and (2) that all participants showed benefits from the short-term mindfulness intervention. Furthermore, we found that the benefits of the short-term mindfulness intervention were not equivalently large across participants. Instead, these benefits were disproportionately large in the heavy media multitaskers. While the positive outcomes were short-lived, this opens the possibility of performing long-term interventions with the goal of realizing lasting gains in this population.
Efficient multitasking: parallel versus serial processing of multiple tasks.

PubMed

Fischer, Rico; Plessow, Franziska

2015-01-01

In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling.
Using Media While Interacting Face-to-Face Is Associated With Psychosocial Well-Being and Personality Traits.

PubMed

Lee, Mindy; Murphy, Karen; Andrews, Glenda

2018-01-01

Positive face-to-face human interactions are known to benefit well-being. Drawing upon previous work regarding the interference of media (via technological devices or print) in social interaction, the aim of this study was to identify whether using media during face-to-face interaction could potentially limit the positive effect of interaction on well-being. Participants were 437 university students who completed an online survey which assessed media multitasking behaviors, well-being (trait depression, trait anxiety, social anxiety, empathy, and psychological well-being), and personality traits (Big-5 and narcissism). Face-to-face interaction was positively associated with well-being. However, when media use during face-to-face interaction was considered, there was a negative relationship with well-being (more depression, more anxiety, and less psychological well-being). Those who used certain media types, such as phone or video chatting, listening to music, and gaming, while interacting with others, also had lower scores on measures of empathy. Regression analyses showed significant contributions by these media types to empathy levels, even after controlling for age, gender, and personality traits. Face-to-face media multitasking was related to higher levels of narcissism and neuroticism, and lower levels of agreeableness, conscientiousness, and openness. This study provides insight into the possible role of media multitasking during face-to-face interaction on psychosocial outcomes.
An assessment of emergency medicine residents' ability to perform in a multitasking environment.

PubMed

Ledrick, David; Fisher, Susan; Thompson, Justin; Sniadanko, Mark

2009-09-01

Multitasking (MT) is a term often applied to emergency medicine (EM), but it is still poorly understood. In an effort to facilitate MT research in EM, the authors conducted this pilot study to describe EM residents' scores on a Multi-Tasking Assessment Tool (MTAT) and compare these scores with the residents' work efficiency in the emergency department. The authors administered a previously developed test of MT ability to EM residents. They performed a multiple regression analysis to determine the effect of MT ability on resident work efficiency, defining efficiency as the number of relative value units billed per hour. They controlled the analysis for year of training and medical knowledge using as a standard the in-service exam administered by the American Board of Emergency Medicine. Complete data for 35 residents were available for analysis. Work efficiency was multivariately correlated with MTAT scores and year of training (P < .05). Whereas year of training explained the majority of the variance, a resident's MT ability accounted for a smaller but still significant portion. This pilot study further validates the MTAT and lays the groundwork for further research in MT in EM. Resident year of training and MTAT scores explain the variability in resident work efficiency significantly more than medical knowledge. Understanding MT ability may ultimately help in resident selection, education, and remediation as well as career counseling and improvement of practice systems in EM.
Media use, face-to-face communication, media multitasking, and social well-being among 8- to 12-year-old girls.

PubMed

Pea, Roy; Nass, Clifford; Meheula, Lyn; Rance, Marcus; Kumar, Aman; Bamford, Holden; Nass, Matthew; Simha, Aneesh; Stillerman, Benjamin; Yang, Steven; Zhou, Michael

2012-03-01

An online survey of 3,461 North American girls ages 8-12 conducted in the summer of 2010 through Discovery Girls magazine examined the relationships between social well-being and young girls' media use--including video, video games, music listening, reading/homework, e-mailing/posting on social media sites, texting/instant messaging, and talking on phones/video chatting--and face-to-face communication. This study introduced both a more granular measure of media multitasking and a new comparative measure of media use versus time spent in face-to-face communication. Regression analyses indicated that negative social well-being was positively associated with levels of uses of media that are centrally about interpersonal interaction (e.g., phone, online communication) as well as uses of media that are not (e.g., video, music, and reading). Video use was particularly strongly associated with negative social well-being indicators. Media multitasking was also associated with negative social indicators. Conversely, face-to-face communication was strongly associated with positive social well-being. Cell phone ownership and having a television or computer in one's room had little direct association with children's socioemotional well-being. We hypothesize possible causes for these relationships, call for research designs to address causality, and outline possible implications of such findings for the social well-being of younger adolescents. PsycINFO Database Record (c) 2012 APA, all rights reserved.
Doing many things at a time: Lack of power decreases the ability to multitask.

PubMed

Cai, Ran Alice; Guinote, Ana

2017-09-01

Three studies investigated the effects of power on the ability to pursue multiple, concomitant goals, also known as multitasking. It was predicted that powerless participants will show lower multitasking ability than control and powerful participants. Study 1 focused on self-reported ability to multitask in a sample of executives and subordinate employees. Studies 2 and 3 investigated the ability to dual-task and to switch between tasks, respectively, using dual-task and task-switching paradigms. Across the studies, powerless individuals were less able to effectively multitask compared with control and powerful participants, suggesting that the detrimental effects of lack of power extend beyond single-task environments, shown in past research, into multitasking environments. Underlying mechanisms are discussed. © 2017 The British Psychological Society.
Sparse Regression as a Sparse Eigenvalue Problem

NASA Technical Reports Server (NTRS)

Moghaddam, Baback; Gruber, Amit; Weiss, Yair; Avidan, Shai

2008-01-01

We extend the l0-norm "subspectral" algorithms for sparse-LDA [5] and sparse-PCA [6] to general quadratic costs such as MSE in linear (kernel) regression. The resulting "Sparse Least Squares" (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem (e.g., binary sparse-LDA [7]). Specifically, for a general quadratic cost we use a highly-efficient technique for direct eigenvalue computation using partitioned matrix inverses which leads to dramatic x103 speed-ups over standard eigenvalue decomposition. This increased efficiency mitigates the O(n4) scaling behaviour that up to now has limited the previous algorithms' utility for high-dimensional learning problems. Moreover, the new computation prioritizes the role of the less-myopic backward elimination stage which becomes more efficient than forward selection. Similarly, branch-and-bound search for Exact Sparse Least Squares (ESLS) also benefits from partitioned matrix inverse techniques. Our Greedy Sparse Least Squares (GSLS) generalizes Natarajan's algorithm [9] also known as Order-Recursive Matching Pursuit (ORMP). Specifically, the forward half of GSLS is exactly equivalent to ORMP but more efficient. By including the backward pass, which only doubles the computation, we can achieve lower MSE than ORMP. Experimental comparisons to the state-of-the-art LARS algorithm [3] show forward-GSLS is faster, more accurate and more flexible in terms of choice of regularization
Dynamic Multitasking Countermeasures to Improve Sustained Attention

DTIC Science & Technology

2013-07-12

Apr-2012 31-Dec-2012 Approved for Public Release; Distribution Unlimited Final Report: Dynamic multitasking countermeasures to improve sustained...ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 vigilance, multitasking REPORT DOCUMENTATION PAGE 11. SPONSOR... multitasking countermeasures to improve sustained attention while driving Report Title The unknown at the outset of this work was if high cognitive load
Neuroticism Negatively Affects Multitasking Performance through State Anxiety

DTIC Science & Technology

2009-02-01

threshold for performance (e.g., Eysenck, 1982; Humphreys & Revelle, 1984). As was noted above, multitasking is generally viewed as being a highly...2001) focused on introversion in the context of interpersonal communication, which is viewed as a type of multitasking due to the need for...authors concluded that introversion was related to poorer 2 nonverbal decoding due to deficits in multitasking ability—but only when the
Multitasking in older adults with type 2 diabetes: A cross-sectional analysis

PubMed Central

McDowd, Joan M.; Mahnken, Jonathan D.; Burns, Jeffrey M.; Sabus, Carla H.; Britton-Carpenter, Amanda J.; Utech, Nora B.; Kluding, Patricia M.

2017-01-01

Background and purpose Deficits in the ability to multitask contribute to gait abnormalities and falls in many at-risk populations. However, it is unclear whether older adults with type 2 diabetes mellitus (DM) also demonstrate impairments in multitasking. The purpose of this study was to compare multitasking performance in cognitively intact older adults with and without DM and explore its relationship to measures of gait and functional ability. Methods We performed a cross-sectional analysis of 40 individuals aged 60 and older with type 2 DM and a matched group of 40 cognitively intact older adults without DM. Multitasking was examined via the ambulatory Walking and Remembering Test (WART) and seated Pursuit Rotor Test (PRT). Self-selected normal and fast walking speed and stride length variability were quantitatively measured, and self-reported functional ability was assessed via the Late Life Function and Disability Index (LLFDI). Results Participants with DM walked slower and took more steps off path when multitasking during the WART. No between-group differences in multitasking performance were observed on the PRT. Multitasking performance demonstrated little correlation with gait and functional ability in either group. Discussion and conclusions Older adults with DM appear to perform poorly on an ambulatory measure of multitasking. However, we analyzed a relatively small, homogenous sample of older adults with and without type 2 DM and factors such as peripheral neuropathy and the use of multiple comparisons complicate interpretation of the data. Future research should explore the interactions between multitasking and safety, fall risk, and function in this vulnerable population. Clinicians should recognize that an array of factors may contribute to gait and physical dysfunction in older adults with type 2 diabetes, and be prepared to assess and intervene appropriately. PMID:29045492
Multitasking in older adults with type 2 diabetes: A cross-sectional analysis.

PubMed

Rucker, Jason L; McDowd, Joan M; Mahnken, Jonathan D; Burns, Jeffrey M; Sabus, Carla H; Britton-Carpenter, Amanda J; Utech, Nora B; Kluding, Patricia M

2017-01-01

Deficits in the ability to multitask contribute to gait abnormalities and falls in many at-risk populations. However, it is unclear whether older adults with type 2 diabetes mellitus (DM) also demonstrate impairments in multitasking. The purpose of this study was to compare multitasking performance in cognitively intact older adults with and without DM and explore its relationship to measures of gait and functional ability. We performed a cross-sectional analysis of 40 individuals aged 60 and older with type 2 DM and a matched group of 40 cognitively intact older adults without DM. Multitasking was examined via the ambulatory Walking and Remembering Test (WART) and seated Pursuit Rotor Test (PRT). Self-selected normal and fast walking speed and stride length variability were quantitatively measured, and self-reported functional ability was assessed via the Late Life Function and Disability Index (LLFDI). Participants with DM walked slower and took more steps off path when multitasking during the WART. No between-group differences in multitasking performance were observed on the PRT. Multitasking performance demonstrated little correlation with gait and functional ability in either group. Older adults with DM appear to perform poorly on an ambulatory measure of multitasking. However, we analyzed a relatively small, homogenous sample of older adults with and without type 2 DM and factors such as peripheral neuropathy and the use of multiple comparisons complicate interpretation of the data. Future research should explore the interactions between multitasking and safety, fall risk, and function in this vulnerable population. Clinicians should recognize that an array of factors may contribute to gait and physical dysfunction in older adults with type 2 diabetes, and be prepared to assess and intervene appropriately.
Impairments of Motor Function While Multitasking in HIV

PubMed Central

Kronemer, Sharif I.; Mandel, Jordan A.; Sacktor, Ned C.; Marvel, Cherie L.

2017-01-01

Human immunodeficiency virus (HIV) became a treatable illness with the introduction of combination antiretroviral therapy (CART). As a result, patients with regular access to CART are expected to live decades with HIV. Long-term HIV infection presents unique challenges, including neurocognitive impairments defined by three major stages of HIV-associated neurocognitive disorders (HAND). The current investigation aimed to study cognitive and motor impairments in HIV using a novel multitasking paradigm. Unlike current standard measures of cognitive and motor performance in HIV, multitasking increases real-world validity by mimicking the dual motor and cognitive demands that are part of daily professional and personal settings (e.g., driving, typing and writing). Moreover, multitask assessments can unmask compensatory mechanisms, normally used under single task conditions, to maintain performance. This investigation revealed that HIV+ participants were impaired on the motor component of the multitask, while cognitive performance was spared. A patient-specific positive interaction between motor performance and working memory recall was driven by poor HIV+ multitaskers. Surprisingly, HAND stage did not correspond with multitask performance and a variety of commonly used assessments indicated normal motor function among HIV+ participants with poor motor performance during the experimental task. These results support the use of multitasks to reveal otherwise hidden impairment in chronic HIV by expanding the sensitivity of clinical assessments used to determine HAND stage. Future studies should examine the capability of multitasks to predict performance in personal, professional and health-related behaviors and prognosis of patients living with chronic HIV. PMID:28503143
Impairments of Motor Function While Multitasking in HIV.

PubMed

Kronemer, Sharif I; Mandel, Jordan A; Sacktor, Ned C; Marvel, Cherie L

2017-01-01

Human immunodeficiency virus (HIV) became a treatable illness with the introduction of combination antiretroviral therapy (CART). As a result, patients with regular access to CART are expected to live decades with HIV. Long-term HIV infection presents unique challenges, including neurocognitive impairments defined by three major stages of HIV-associated neurocognitive disorders (HAND). The current investigation aimed to study cognitive and motor impairments in HIV using a novel multitasking paradigm. Unlike current standard measures of cognitive and motor performance in HIV, multitasking increases real-world validity by mimicking the dual motor and cognitive demands that are part of daily professional and personal settings (e.g., driving, typing and writing). Moreover, multitask assessments can unmask compensatory mechanisms, normally used under single task conditions, to maintain performance. This investigation revealed that HIV+ participants were impaired on the motor component of the multitask, while cognitive performance was spared. A patient-specific positive interaction between motor performance and working memory recall was driven by poor HIV+ multitaskers. Surprisingly, HAND stage did not correspond with multitask performance and a variety of commonly used assessments indicated normal motor function among HIV+ participants with poor motor performance during the experimental task. These results support the use of multitasks to reveal otherwise hidden impairment in chronic HIV by expanding the sensitivity of clinical assessments used to determine HAND stage. Future studies should examine the capability of multitasks to predict performance in personal, professional and health-related behaviors and prognosis of patients living with chronic HIV.
Deadlines in space: Selective effects of coordinate spatial processing in multitasking.

PubMed

Todorov, Ivo; Del Missier, Fabio; Konke, Linn Andersson; Mäntylä, Timo

2015-11-01

Many everyday activities require coordination and monitoring of multiple deadlines. One way to handle these temporal demands might be to represent future goals and deadlines as a pattern of spatial relations. We examined the hypothesis that spatial ability, in addition to executive functioning, contributes to individual differences in multitasking. In two studies, participants completed a multitasking session in which they monitored four digital clocks running at different rates. In Study 1, we found that individual differences in spatial ability and executive functions were independent predictors of multiple-task performance. In Study 2, we found that individual differences in specific spatial abilities were selectively related to multiple-task performance, as only coordinate spatial processing, but not categorical, predicted multitasking, even beyond executive functioning and numeracy. In both studies, males outperformed females in spatial ability and multitasking and in Study 2 these sex differences generalized to a simulation of everyday multitasking. Menstrual changes moderated the effects on multitasking, in that sex differences in coordinate spatial processing and multitasking were observed between males and females in the luteal phase of the menstrual cycle, but not between males and females at menses. Overall, these findings suggest that multiple-task performance reflects independent contributions of spatial ability and executive functioning. Furthermore, our results support the distinction of categorical versus coordinate spatial processing, and suggest that these two basic relational processes are selectively affected by female sex hormones and differentially effective in transforming and handling temporal patterns as spatial relations in the context of multitasking.

Flexible Modeling of Latent Task Structures in Multitask Learning

DTIC Science & Technology

2012-06-26

Flexible Modeling of Latent Task Structures in Multitask Learning Alexandre Passos† apassos@cs.umass.edu Computer Science Department, University of...of Maryland, College Park, MD USA Abstract Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure...shared by all the tasks. However, it is usually unclear what type of latent task structure is the most ap- propriate for a given multitask learning prob
The relationship between internet-gaming experience and executive functions measured by virtual environment compared with conventional laboratory multitasks.

PubMed

Chen, Yong-Quan; Hsieh, Shulan

2018-01-01

The aim of this study was to investigate if individuals with frequent internet gaming (IG) experience exhibited better or worse multitasking ability compared with those with infrequent IG experience. The individuals' multitasking abilities were measured using virtual environment multitasks, such as Edinburgh Virtual Errands Test (EVET), and conventional laboratory multitasks, such as the dual task and task switching. Seventy-two young healthy college students participated in this study. They were split into two groups based on the time spent on playing online games, as evaluated using the Internet Use Questionnaire. Each participant performed EVET, dual-task, and task-switching paradigms on a computer. The current results showed that the frequent IG group performed better on EVET compared with the infrequent IG group, but their performance on the dual-task and task-switching paradigms did not differ significantly. The results suggest that the frequent IG group exhibited better multitasking efficacy if measured using a more ecologically valid task, but not when measured using a conventional laboratory multitasking task. The differences in terms of the subcomponents of executive function measured by these task paradigms were discussed. The current results show the importance of the task effect while evaluating frequent internet gamers' multitasking ability.
Multitasking as a choice: a perspective.

PubMed

Broeker, Laura; Liepelt, Roman; Poljac, Edita; Künzell, Stefan; Ewolds, Harald; de Oliveira, Rita F; Raab, Markus

2018-01-01

Performance decrements in multitasking have been explained by limitations in cognitive capacity, either modelled as static structural bottlenecks or as the scarcity of overall cognitive resources that prevent humans, or at least restrict them, from processing two tasks at the same time. However, recent research has shown that individual differences, flexible resource allocation, and prioritization of tasks cannot be fully explained by these accounts. We argue that understanding human multitasking as a choice and examining multitasking performance from the perspective of judgment and decision-making (JDM), may complement current dual-task theories. We outline two prominent theories from the area of JDM, namely Simple Heuristics and the Decision Field Theory, and adapt these theories to multitasking research. Here, we explain how computational modelling techniques and decision-making parameters used in JDM may provide a benefit to understanding multitasking costs and argue that these techniques and parameters have the potential to predict multitasking behavior in general, and also individual differences in behavior. Finally, we present the one-reason choice metaphor to explain a flexible use of limited capacity as well as changes in serial and parallel task processing. Based on this newly combined approach, we outline a concrete interdisciplinary future research program that we think will help to further develop multitasking research.
The relationship between internet-gaming experience and executive functions measured by virtual environment compared with conventional laboratory multitasks

PubMed Central

Chen, Yong-Quan

2018-01-01

The aim of this study was to investigate if individuals with frequent internet gaming (IG) experience exhibited better or worse multitasking ability compared with those with infrequent IG experience. The individuals’ multitasking abilities were measured using virtual environment multitasks, such as Edinburgh Virtual Errands Test (EVET), and conventional laboratory multitasks, such as the dual task and task switching. Seventy-two young healthy college students participated in this study. They were split into two groups based on the time spent on playing online games, as evaluated using the Internet Use Questionnaire. Each participant performed EVET, dual-task, and task-switching paradigms on a computer. The current results showed that the frequent IG group performed better on EVET compared with the infrequent IG group, but their performance on the dual-task and task-switching paradigms did not differ significantly. The results suggest that the frequent IG group exhibited better multitasking efficacy if measured using a more ecologically valid task, but not when measured using a conventional laboratory multitasking task. The differences in terms of the subcomponents of executive function measured by these task paradigms were discussed. The current results show the importance of the task effect while evaluating frequent internet gamers’ multitasking ability. PMID:29879150
HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong

2015-01-01

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645
Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672
Mental juggling: when does multitasking impair reading comprehension?

PubMed

Cho, Kit W; Altarriba, Jeanette; Popiel, Maximilian

2015-01-01

The present study investigated the conditions under which multitasking impairs reading comprehension. Participants read prose passages (the primary task), some of which required them to perform a secondary task. In Experiment 1, we compared two different types of secondary tasks (answering trivia questions and solving math problems). Reading comprehension was assessed using a multiple-choice test that measured both factual and conceptual knowledge. The results showed no observable detrimental effects associated with multitasking. In Experiment 2, the secondary task was a cognitive load task that required participants to remember a string of numbers while reading the passages. Performance on the reading comprehension test was lower in the cognitive load conditions relative to the no-load condition. The present study delineates the conditions under which multitasking can impair or have no effect on reading comprehension. These results further our understanding of our capacity to multitask and have practical implications in our technologically advanced society in which multitasking has become commonplace.
An Exploratory Study to Measure Excessive Involvement in Multitasking Interaction with Smart Devices.

PubMed

Zhang, Yubo; Rau, Pei-Luen Patrick

2016-06-01

This study developed a scale measuring excessive involvement in multitasking interaction with smart devices. An online questionnaire was designed and surveyed in a sample of 380 respondents. The sample was split into two groups for exploratory and confirmatory factor analysis, respectively. A four-factor structure was identified with an acceptable goodness of fit. The first two factors, "Obsession and neglect" and "Problematic control," described the obsessive feelings, neglect behaviors, and behavior control problems accompanied by excessive multitasking interaction with smart devices. The latter two factors, "Multitasking preference" and "Polychronic orientation," referred to multitaskers' preference of engaging in multiple media use or interaction tasks rather than a single task from the time orientation perspective. The four-factor structure indicates that excessive involvement in multitasking interaction with smart devices shares some similarities with other behavioral addiction types, but demonstrates uniqueness compared with excessive engagement in single media use.
Single-task fMRI overlap predicts concurrent multitasking interference.

PubMed

Nijboer, Menno; Borst, Jelmer; van Rijn, Hedderik; Taatgen, Niels

2014-10-15

There is no consensus regarding the origin of behavioral interference that occurs during concurrent multitasking. Some evidence points toward a multitasking locus in the brain, while other results imply that interference is the consequence of task interactions in several brain regions. To investigate this issue, we conducted a functional MRI (fMRI) study consisting of three component tasks, which were performed both separately and in combination. The results indicated that no specific multitasking area exists. Instead, different patterns of activation across conditions could be explained by assuming that the interference is a result of task interactions. Additionally, similarity in single-task activation patterns correlated with a decrease in accuracy during dual-task conditions. Taken together, these results support the view that multitasking interference is not due to a bottleneck in a single "multitasking" brain region, but is a result of interactions between concurrently running processes. Copyright © 2014 Elsevier Inc. All rights reserved.
Association between Stereotactic Radiotherapy and Death from Brain Metastases of Epithelial Ovarian Cancer: a Gliwice Data Re-Analysis with Penalization

PubMed

Tukiendorf, Andrzej; Mansournia, Mohammad Ali; Wydmański, Jerzy; Wolny-Rokicka, Edyta

2017-04-01

Background: Clinical datasets for epithelial ovarian cancer brain metastatic patients are usually small in size. When adequate case numbers are lacking, resulting estimates of regression coefficients may demonstrate bias. One of the direct approaches to reduce such sparse-data bias is based on penalized estimation. Methods: A re- analysis of formerly reported hazard ratios in diagnosed patients was performed using penalized Cox regression with a popular SAS package providing additional software codes for a statistical computational procedure. Results: It was found that the penalized approach can readily diminish sparse data artefacts and radically reduce the magnitude of estimated regression coefficients. Conclusions: It was confirmed that classical statistical approaches may exaggerate regression estimates or distort study interpretations and conclusions. The results support the thesis that penalization via weak informative priors and data augmentation are the safest approaches to shrink sparse data artefacts frequently occurring in epidemiological research. Creative Commons Attribution License
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Structured box training improves stability of retraction while multitasking in colorectal surgery simulation.

PubMed

Kobiela, Jarek; Spychalski, Piotr; Łaski, Dariusz; Błażyńska-Spychalska, Agata; Łachiński, Andrzej J; Śledziński, Zbigniew; Hull, Tracy

2018-09-01

Laparoscopic colorectal surgery has an established role. The ability to multitask (use a retraction tool with one hand and navigate a laparoscopic camera with the other) is desired for efficient laparoscopic surgery. Surgical trainees must learn this skill to perform advanced laparoscopic tasks. The aim was to determine whether a box-training protocol improves the stability of retraction while multitasking in colorectal surgery simulation. Fifty-eight medical students were recruited to attend a basic laparoscopic box-training course. Ability to perform steady retraction with and without multitasking was measured initially and at the conclusion of the course. Before training, students demonstrated a decrease in performance while multitasking with a greater maximal exerted force, a greater range of force, and a greater standard deviation for traction and minimal exerted force, range of force and a greater standard deviation for countertraction. Statistically significant improvement (lower maximal exerted force and lower range of force) was observed for traction while multitasking after training. After the training, no statistically significant differences were found when the student performed a single task versus multitasking, both for traction and countertraction. A structured box-training curriculum improved the stability of retraction while multitasking in this colorectal surgery simulation. Although it did not improve stability of retraction as a single task, it did improve stability of retraction while multitasking. After training, this enables the trainee to retract as efficiently while operating the camera as they retract when only focusing on retraction as a single task. Copyright © 2018 Elsevier Inc. All rights reserved.
Multitasking domain decomposition fast Poisson solvers on the Cray Y-MP

NASA Technical Reports Server (NTRS)

Chan, Tony F.; Fatoohi, Rod A.

1990-01-01

The results of multitasking implementation of a domain decomposition fast Poisson solver on eight processors of the Cray Y-MP are presented. The object of this research is to study the performance of domain decomposition methods on a Cray supercomputer and to analyze the performance of different multitasking techniques using highly parallel algorithms. Two implementations of multitasking are considered: macrotasking (parallelism at the subroutine level) and microtasking (parallelism at the do-loop level). A conventional FFT-based fast Poisson solver is also multitasked. The results of different implementations are compared and analyzed. A speedup of over 7.4 on the Cray Y-MP running in a dedicated environment is achieved for all cases.
Improved statistical power with a sparse shape model in detecting an aging effect in the hippocampus and amygdala

NASA Astrophysics Data System (ADS)

Chung, Moo K.; Kim, Seung-Goo; Schaefer, Stacey M.; van Reekum, Carien M.; Peschke-Schmitz, Lara; Sutterer, Matthew J.; Davidson, Richard J.

2014-03-01

The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace- Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Tradition- ally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing a sparse penalty. For dense anatomical data such as deformation fields on a surface mesh, the sparse regression behaves like a smoothing process, which will reduce the error of incorrectly detecting false negatives. Hence the statistical power improves. The sparse shape model is then applied in investigating the influence of age on amygdala and hippocampus shapes in the normal population. The advantage of the LB sparse framework is demonstrated by showing the increased statistical power.
Investigating the relationship between media multitasking and processes involved in task-switching.

PubMed

Alzahabi, Reem; Becker, Mark W; Hambrick, David Z

2017-11-01

Although multitasking with media has increased dramatically in recent years (Rideout, Foehr, & Roberts, 2010), the association between media multitasking and cognitive performance is poorly understood. In addition, the literature on the relationship between media multitasking and task-switching, one measure of cognitive control, has produced mixed results (Alzahabi & Becker, 2013; Minear et al., 2013; Ophir, Nass, & Wagner, 2009). Here we use an individual differences approach to investigate the relationship between media multitasking and task-switching performance by first examining the structure of task-switching and identifying the latent factors that contribute to switch costs. Participants performed a series of 3 different task-switching paradigms, each designed to isolate the effects of a specific putative mechanism (e.g., advanced preparation) related to task-switching performance, as well as a series of surveys to measure media multitasking and intelligence. The results suggest that task-switching performance is related to 2 somewhat independent factors, namely an advanced preparation factor and passive decay factor. In addition, multitasking with media was related to a faster ability to prepare for tasks, resulting in faster task-switching performance without a cost to accuracy. Media multitasking and intelligence were both unrelated to passive decay factors. These findings are consistent with a 2-component model of task-switching (Sohn & Anderson, 2001), as well as an automatic/executive framework of cognitive control (Schneider & Shiffrin, 1977). (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Multitasking Operating Systems for the IBM PC.

ERIC Educational Resources Information Center

Owen, G. Scott

1985-01-01

The ability of a microcomputer to execute several programs at the same time is called "multitasking." The nature and use of one multitasking operating system Concurrent PC-DOS from Digital Research (the developers of the CP/M operating system) are discussed. (JN)
Between Domain Cognitive Dispersion and Functional Abilities in Older Adults

PubMed Central

Fellows, Robert P.; Schmitter-Edgecombe, Maureen

2016-01-01

Objective Within-person variability in cognitive performance is related to neurological integrity, but the association with functional abilities is less clear. The primary aim of this study was to examine the association between cognitive dispersion, or within-person variability, and everyday multitasking and the way in which these variables may influence performance on a naturalistic assessment of functional abilities. Method Participants were 156 community-dwelling adults, age 50 or older. Cognitive dispersion was calculated by measuring within-person variability in cognitive domains, established through principal components analysis. Path analysis was used to determine the independent contribution of cognitive dispersion to functional ability, mediated by multitasking. Results Results of the path analysis revealed that the number of subtasks interweaved (i.e., multitasked) mediated the association between cognitive dispersion and task sequencing and accuracy. Although increased multitasking was associated with worse task performance in the path model, secondary analyses revealed that for individuals with low cognitive dispersion, increased multitasking was associated with better task performance, whereas for those with higher levels of dispersion multitasking was negatively correlated with task performance. Conclusion These results suggest that cognitive dispersion between domains may be a useful indicator of multitasking and daily living skills among older adults. PMID:26300441
The influence of positive vs. negative affect on multitasking.

PubMed

Morgan, Brent; D'Mello, Sidney K

2016-10-01

Considerable research has investigated how affect influences performance on a single task; however, little is known about the role of affect in complex multitasking environments. In this paper, 178 participants multitasked in a synthetic work environment (SYNWORK) consisting of memory, visual monitoring, auditory monitoring, and math tasks. Participants multitasked for a 3-min baseline phase (MT1), following which they were randomly assigned to watch one of three affect-induction videos: positive, neutral, or negative. Participants then resumed multitasking for two additional critical phases (MT2, MT3; 3min each). In MT2, performance of the positive and neutral conditions was statistically equivalent and higher than the negative condition. In MT3, the positive condition performed better than the negative condition, with the neutral condition not significantly different from the other two. The differences in overall multitasking scores were largely driven by errors in the Math task (the most cognitively demanding task) in MT2 and the Memory task in MT3. These findings have implications for how positive and negative affective states influence processing in a cognitively demanding multitasking environment. Copyright © 2016 Elsevier B.V. All rights reserved.
Anodal tDCS applied during multitasking training leads to transferable performance gains.

PubMed

Filmer, Hannah L; Lyons, Maxwell; Mattingley, Jason B; Dux, Paul E

2017-10-11

Cognitive training can lead to performance improvements that are specific to the tasks trained. Recent research has suggested that transcranial direct current stimulation (tDCS) applied during training of a simple response-selection paradigm can broaden performance benefits to an untrained task. Here we assessed the impact of combined tDCS and training on multitasking, stimulus-response mapping specificity, response-inhibition, and spatial attention performance in a cohort of healthy adults. Participants trained over four days with concurrent tDCS - anodal, cathodal, or sham - applied to the left prefrontal cortex. Immediately prior to, 1 day after, and 2 weeks after training, performance was assessed on the trained multitasking paradigm, an untrained multitasking paradigm, a go/no-go inhibition task, and a visual search task. Training combined with anodal tDCS, compared with training plus cathodal or sham stimulation, enhanced performance for the untrained multitasking paradigm and visual search tasks. By contrast, there were no training benefits for the go/no-go task. Our findings demonstrate that anodal tDCS combined with multitasking training can extend to untrained multitasking paradigms as well as spatial attention, but with no extension to the domain of response inhibition.
Multitasking in the University Classroom

ERIC Educational Resources Information Center

Burak, Lydia

2012-01-01

Although research evidence indicates that multitasking results in poorer learning and poorer performance, many students engage with text messaging, Facebook, internet searching, emailing, and instant messaging, while sitting in university classrooms. Research also suggests that multitasking may be related to risk behaviors. This study's purpose…

Unravelling the Complexity of Teams via a Thermodynamics Perspective

DTIC Science & Technology

2014-10-01

potentially irrational behaviors. Multitasking (MT) is an unsolved but key theoretical problem for organizing teams, organizations and systems...While individuals multitask (MT) poorly (Wickens, 1992), multitasking is the function of groups as they pool skills to accomplish goals they are unable
A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

NASA Astrophysics Data System (ADS)

Gillis, Nicolas; Luce, Robert

2018-01-01

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.
Delayed enhancement of multitasking performance: Effects of anodal transcranial direct current stimulation on the prefrontal cortex

PubMed Central

Hsu, Wan-Yu; Zanto, Theodore P.; Anguera, Joaquin A.; Lin, Yung-Yang; Gazzaley, Adam

2015-01-01

Background The dorsolateral prefrontal cortex (DLPFC) has been proposed to play an important role in neural processes that underlie multitasking performance. However, this claim is underexplored in terms of direct causal evidence. Objective The current study aimed to delineate the causal involvement of the DLPFC during multitasking by modulating neural activity with transcranial direct current stimulation (tDCS) prior to engagement in a demanding multitasking paradigm. Methods The study is a single-blind, crossover, sham-controlled experiment. Anodal tDCS or sham tDCS was applied over left DLPFC in forty-one healthy young adults (aged 18–35 years) immediately before they engaged in a 3-D video game designed to assess multitasking performance. Participants were separated into three subgroups: real-sham (i.e., real tDCS in the first session, followed by sham tDCS in the second session one hour later), sham-real (sham tDCS first session, real tDCS second session), and sham-sham (sham tDCS in both sessions). Results The real-sham group showed enhanced multitasking performance and decreased multitasking cost during the second session, compared to first session, suggesting delayed cognitive benefits of tDCS. Interestingly, performance benefits were observed only for multitasking and not on a single-task version of the game. No significant changes were found between the first and second sessions for either the sham-real or the sham-sham groups. Conclusions These results suggest a causal role of left prefrontal cortex in facilitating the simultaneous performance of more than one task, or multitasking. Moreover, these findings reveal that anodal tDCS may have delayed benefits that reflect an enhanced rate of learning. PMID:26073148
Delayed enhancement of multitasking performance: Effects of anodal transcranial direct current stimulation on the prefrontal cortex.

PubMed

Hsu, Wan-Yu; Zanto, Theodore P; Anguera, Joaquin A; Lin, Yung-Yang; Gazzaley, Adam

2015-08-01

The dorsolateral prefrontal cortex (DLPFC) has been proposed to play an important role in neural processes that underlie multitasking performance. However, this claim is underexplored in terms of direct causal evidence. The current study aimed to delineate the causal involvement of the DLPFC during multitasking by modulating neural activity with transcranial direct current stimulation (tDCS) prior to engagement in a demanding multitasking paradigm. The study is a single-blind, crossover, sham-controlled experiment. Anodal tDCS or sham tDCS was applied over left DLPFC in forty-one healthy young adults (aged 18-35 years) immediately before they engaged in a 3-D video game designed to assess multitasking performance. Participants were separated into three subgroups: real-sham (i.e., real tDCS in the first session, followed by sham tDCS in the second session 1 h later), sham-real (sham tDCS first session, real tDCS second session), and sham-sham (sham tDCS in both sessions). The real-sham group showed enhanced multitasking performance and decreased multitasking cost during the second session, compared to first session, suggesting delayed cognitive benefits of tDCS. Interestingly, performance benefits were observed only for multitasking and not on a single-task version of the game. No significant changes were found between the first and second sessions for either the sham-real or the sham-sham groups. These results suggest a causal role of left prefrontal cortex in facilitating the simultaneous performance of more than one task, or multitasking. Moreover, these findings reveal that anodal tDCS may have delayed benefits that reflect an enhanced rate of learning. Copyright © 2015 Elsevier Ltd. All rights reserved.
Sparse brain network using penalized linear regression

NASA Astrophysics Data System (ADS)

Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.

2011-03-01

Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Multitasking Teachers: Mistake or Missing Link?

ERIC Educational Resources Information Center

Eisenwine, Marilyn J.; Hadley, Nancy J.

2011-01-01

This article presents case studies involving graduate students who exhibited multitasking behaviors during their university courses, and explores how those behaviors functioned in their own classrooms. Though these university students appeared to be inattentive, their multitasking proved to be indicative of creativity and flexibility in their…
Small Group Multitasking in Literature Classes

ERIC Educational Resources Information Center

Baurain, Bradley

2007-01-01

Faced with the challenge of teaching American literature to large, multilevel classes in Vietnam, the writer developed a flexible small group framework called "multitasking". "Multitasking" sets up stable task categories which rotate among small groups from lesson to lesson. This framework enabled students to work cooperatively…
Understanding and Visualizing Multitasking and Task Switching Activities: A Time Motion Study to Capture Nursing Workflow

PubMed Central

Yen, Po-Yin; Kelley, Marjorie; Lopetegui, Marcelo; Rosado, Amber L.; Migliore, Elaina M.; Chipps, Esther M.; Buck, Jacalyn

2016-01-01

A fundamental understanding of multitasking within nursing workflow is important in today’s dynamic and complex healthcare environment. We conducted a time motion study to understand nursing workflow, specifically multitasking and task switching activities. We used TimeCaT, a comprehensive electronic time capture tool, to capture observational data. We established inter-observer reliability prior to data collection. We completed 56 hours of observation of 10 registered nurses. We found, on average, nurses had 124 communications and 208 hands-on tasks per 4-hour block of time. They multitasked (having communication and hands-on tasks simultaneously) 131 times, representing 39.48% of all times; the total multitasking duration ranges from 14.6 minutes to 109 minutes, 44.98 minutes (18.63%) on average. We also reviewed workflow visualization to uncover the multitasking events. Our study design and methods provide a practical and reliable approach to conducting and analyzing time motion studies from both quantitative and qualitative perspectives. PMID:28269924
Short-term mindfulness intervention reduces the negative attentional effects associated with heavy media multitasking

PubMed Central

Gorman, Thomas E.; Green, C. Shawn

2016-01-01

Recent research suggests that frequently switching between various forms of media (i.e. ‘media multitasking’) is associated with diminished attentional abilities, a disconcerting result given the prevalence of media multitasking in today’s society. In the present study, we sought to investigate the extent to which the deficits associated with frequent media multitasking can be temporarily ameliorated via a short-term mindfulness intervention previously shown to produce beneficial effects on the attentional abilities of normally functioning individuals. Consistent with previous work, we found: (1) that heavy media multitaskers showed generally poorer attentional abilities than light media multitaskers and (2) that all participants showed benefits from the short-term mindfulness intervention. Furthermore, we found that the benefits of the short-term mindfulness intervention were not equivalently large across participants. Instead, these benefits were disproportionately large in the heavy media multitaskers. While the positive outcomes were short-lived, this opens the possibility of performing long-term interventions with the goal of realizing lasting gains in this population. PMID:27086504
Multitasking the three-dimensional shock wave code CTH on the Cray X-MP/416

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

McGlaun, J.M.; Thompson, S.L.

1988-01-01

CTH is a software system under development at Sandia National Laboratories Albuquerque that models multidimensional, multi-material, large-deformation, strong shock wave physics. CTH was carefully designed to both vectorize and multitask on the Cray X-MP/416. All of the physics routines are vectorized except the thermodynamics and the interface tracer. All of the physics routines are multitasked except the boundary conditions. The Los Alamos National Laboratory multitasking library was used for the multitasking. The resulting code is easy to maintain, easy to understand, gives the same answers as the unitasked code, and achieves a measured speedup of approximately 3.5 on the fourmore » cpu Cray. This document discusses the design, prototyping, development, and debugging of CTH. It also covers the architecture features of CTH that enhances multitasking, granularity of the tasks, and synchronization of tasks. The utility of system software and utilities such as simulators and interactive debuggers are also discussed. 5 refs., 7 tabs.« less
Understanding and Visualizing Multitasking and Task Switching Activities: A Time Motion Study to Capture Nursing Workflow.

PubMed

Yen, Po-Yin; Kelley, Marjorie; Lopetegui, Marcelo; Rosado, Amber L; Migliore, Elaina M; Chipps, Esther M; Buck, Jacalyn

2016-01-01

A fundamental understanding of multitasking within nursing workflow is important in today's dynamic and complex healthcare environment. We conducted a time motion study to understand nursing workflow, specifically multitasking and task switching activities. We used TimeCaT, a comprehensive electronic time capture tool, to capture observational data. We established inter-observer reliability prior to data collection. We completed 56 hours of observation of 10 registered nurses. We found, on average, nurses had 124 communications and 208 hands-on tasks per 4-hour block of time. They multitasked (having communication and hands-on tasks simultaneously) 131 times, representing 39.48% of all times; the total multitasking duration ranges from 14.6 minutes to 109 minutes, 44.98 minutes (18.63%) on average. We also reviewed workflow visualization to uncover the multitasking events. Our study design and methods provide a practical and reliable approach to conducting and analyzing time motion studies from both quantitative and qualitative perspectives.
Improving multitasking assessment in healthy older adults using a prop-based version of the Breakfast task.

PubMed

Kosowicz, Maria; MacPherson, Sarah E

2017-01-01

Computerized cognitive assessment is becoming increasingly more common in clinical neuropsychological assessment and cognitive neuropsychological research. A number of computerized tasks now exist to assess multitasking abilities that are essential for everyday tasks such as cooking, shopping, or driving, but little is known about whether these tasks are appropriate for assessing older adults' multitasking. The present study directly compared age effects on multitasking when assessed using a computerized and a prop-based version of Craik and Bialystok's ( 2006 ) Breakfast task. Twenty participants aged 18 to 24 years and 20 participants aged 60 to 79 years were assessed on both versions of the Breakfast task. While age-related decrements in multitasking performance were found using the computerized task, significant age differences were not found on the majority of measures when the prop-based version was administered. The results suggest that age-related deficits in multitasking will be less when more contextualized, noncomputer based tasks are used.
On supertaskers and the neural basis of efficient multitasking.

PubMed

Medeiros-Ward, Nathan; Watson, Jason M; Strayer, David L

2015-06-01

The present study used brain imaging to determine the neural basis of individual differences in multitasking, the ability to successfully perform at least two attention-demanding tasks at once. Multitasking is mentally taxing and, therefore, should recruit the prefrontal cortex to maintain task goals when coordinating attentional control and managing the cognitive load. To investigate this possibility, we used functional neuroimaging to assess neural activity in both extraordinary multitaskers (Supertaskers) and control subjects who were matched on working memory capacity. Participants performed a challenging dual N-back task in which auditory and visual stimuli were presented simultaneously, requiring independent and continuous maintenance, updating, and verification of the contents of verbal and spatial working memory. With the task requirements and considerable cognitive load that accompanied increasing N-back, relative to the controls, the multitasking of Supertaskers was characterized by more efficient recruitment of anterior cingulate and posterior frontopolar prefrontal cortices. Results are interpreted using neuropsychological and evolutionary perspectives on individual differences in multitasking ability and the neural correlates of attentional control.
Can You Multitask? Evidence and Limitations of Task Switching and Multitasking in Emergency Medicine.

PubMed

Skaugset, L Melissa; Farrell, Susan; Carney, Michele; Wolff, Margaret; Santen, Sally A; Perry, Marcia; Cico, Stephen John

2016-08-01

Emergency physicians work in a fast-paced environment that is characterized by frequent interruptions and the expectation that they will perform multiple tasks efficiently and without error while maintaining oversight of the entire emergency department. However, there is a lack of definition and understanding of the behaviors that constitute effective task switching and multitasking, as well as how to improve these skills. This article reviews the literature on task switching and multitasking in a variety of disciplines-including cognitive science, human factors engineering, business, and medicine-to define and describe the successful performance of task switching and multitasking in emergency medicine. Multitasking, defined as the performance of two tasks simultaneously, is not possible except when behaviors become completely automatic; instead, physicians rapidly switch between small tasks. This task switching causes disruption in the primary task and may contribute to error. A framework is described to enhance the understanding and practice of these behaviors. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.
Metric Identification and Protocol Development for Characterizing DNAPL Source Zone Architecture and Associated Plume Response

DTIC Science & Technology

2013-09-01

M.4.1. Two-dimensional domains cropped out of three-dimensional numerically generated realizations; (a) 3D PCE-NAPL realizations generated by UTCHEM...165 Figure R.3.2. The absolute error vs relative error scatter plots of pM and gM from SGS data set- 4 using multi-task manifold...error scatter plots of pM and gM from TP/MC data set using multi- task manifold regression
Does multitasking mediate the relationships between episodic memory, attention, executive functions and apathetic manifestations in traumatic brain injury?

PubMed

Arnould, Annabelle; Rochat, Lucien; Dromer, Emilie; Azouvi, Philippe; Van der Linden, Martial

2018-03-01

Apathy is frequently described in patients with traumatic brain injury (TBI); its negative consequences particularly affect functional independence. Among apathetic manifestations, lack of initiative and lack of interest have mainly been associated with cognitive impairments. However, few studies have been conducted to precisely identify the underlying cognitive processes. Our aims were (1) to determine the best predictor of apathy from among several cognitive processes, including episodic memory and attention/executive mechanisms and multitasking, and (2) to examine to what extent multitasking could mediate the relationships between specific cognitive processes and lack of initiative/interest. Seventy participants (34 patients with TBI matched with 36 control participants) were given a questionnaire to assess anxio-depressive symptoms, four tasks to assess specific cognitive processes, and one task to assess real-life multitasking. Participants' relatives completed an apathy questionnaire. Multitasking, as assessed by the number of goals not achieved, was the only significant predictor of apathetic manifestations. In addition, the mediation analyses revealed that multitasking performance mediated the relationships between verbal episodic memory and lack of initiative/interest, whereas executive and attentional functions were only indirectly related to lack of initiative/interest due to their significant impacts on multitasking. These results shed new light on the aetiology of apathetic manifestations in patients with TBI, indicating how specific cognitive deficits are expressed in real-life multitasking, and consequently, how they may lead to the development and/or maintenance of apathetic manifestations. © 2016 The British Psychological Society.
Relationships of multitasking, physicians' strain, and performance: an observational study in ward physicians.

PubMed

Weigl, Matthias; Müller, Andreas; Sevdalis, Nick; Angerer, Peter

2013-03-01

Simultaneous task performance ("multitasking") is common in hospital physicians' work and is implicated as a major determinant for enhanced strain and detrimental performance. The aim was to determine the impact of multitasking by hospital physicians on their self reported strain and performance. A prospective observational time-and-motion study in a Community Hospital was conducted. Twenty-seven hospital physicians (surgical and internal specialties) were observed in 40 full-shift observations. Observed physicians reported twice on their self-monitored strain and performance during the observation time. Associations of observed multitasking events and subsequent strain and performance appraisals were calculated. About 21% of the working time physicians were engaged in simultaneous activities. The average time spent in multitasking activities correlated significantly with subsequently reported strain (r = 0.27, P = 0.018). The number of instances of multitasking activities correlated with self-monitored performance to a marginally significant level (r = 0.19, P = 0.098). Physicians who engage in multitasking activities tend to self-report better performance but at the cost of enhanced psychophysical strain. Hence, physicians do not perceive their own multitasking activities as a source for deficient performance, for example, medical errors. Readjustment of workload, improved organization of work for hospital physicians, and training programs to improve physicians' skills in dealing with multiple clinical demands, prioritization, and efficient task allocation may be useful avenues to explore to reduce the potentially negative impact of simultaneous task performance in clinical settings.
Student Off-Task Electronic Multitasking Predictors: Scale Development and Validation

ERIC Educational Resources Information Center

Qian, Yuxia; Li, Li

2017-01-01

In an attempt to better understand factors contributing to students' off-task electronic multitasking behavior in class, the research included two studies that developed a scale of students' off-task electronic multitasking predictors (the SOTEMP scale), and explored relationships between the scale and various classroom communication processes and…
A Multitasking General Executive for Compound Continuous Tasks

ERIC Educational Resources Information Center

Salvucci, Dario D.

2005-01-01

As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the…
The Effect of Multitasking to Faculty Members' Academic Works

ERIC Educational Resources Information Center

Baran, Bahar

2013-01-01

Faculty members in higher education institutions which technology produced in and used actively try to overcome simultaneous one more works because of their intensive works and responsibilities. This study associated simultaneously doing one more academic works to multitasking. Multitasking may have a detrimental effect on academic works since it…

Multitasking with Smartphones in the College Classroom

ERIC Educational Resources Information Center

Grinols, Anne Bradstreet; Rajesh, Rishi

2014-01-01

Although the concept of multitasking itself is under debate, smartphones do enable users to divert attention from the task at hand to nongermane matters. As smartphone use becomes pervasive, extending into our classrooms, educators are concerned that they are becoming a major distraction. Does multitasking with smartphones impede learning? Can…
Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.

PubMed

Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan

2018-06-01

Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
A new virtual-reality training module for laparoscopic surgical skills and equipment handling: can multitasking be trained? A randomized controlled trial.

PubMed

Bongers, Pim J; Diederick van Hove, P; Stassen, Laurents P S; Dankelman, Jenny; Schreuder, Henk W R

2015-01-01

During laparoscopic surgery distractions often occur and multitasking between surgery and other tasks, such as technical equipment handling, is a necessary competence. In psychological research, reduction of adverse effects of distraction is demonstrated when specifically multitasking is trained. The aim of this study was to examine whether multitasking and more specifically task-switching can be trained in a virtual-reality (VR) laparoscopic skills simulator. After randomization, the control group trained separately with an insufflator simulation module and a laparoscopic skills exercise module on a VR simulator. In the intervention group, insufflator module and VR skills exercises were combined to develop a new integrated training in which multitasking was a required competence. At random moments, problems with the insufflator appeared and forced the trainee to multitask. During several repetitions of a different multitask VR skills exercise as posttest, performance parameters (laparoscopy time, insufflator time, and errors) were measured and compared between both the groups as well with a pretest exercise to establish the learning effect. A face-validity questionnaire was filled afterward. University Medical Centre Utrecht, The Netherlands. Medical and PhD students (n = 42) from University Medical Centre Utrecht, without previous experience in laparoscopic simulation, were randomly assigned to either intervention (n = 21) or control group (n = 21). All participants performed better in the posttest exercises without distraction of the insufflator compared with the exercises in which multitasking was necessary to solve the insufflator problems. After training, the intervention group was significantly quicker in solving the insufflator problems (mean = 1.60Log(s) vs 1.70Log(s), p = 0.02). No significant differences between both the groups were seen in laparoscopy time and errors. Multitasking has negative effects on the laparoscopic performance. This study suggests an additional learning effect of training multitasking in VR laparoscopy simulation, because the trainees are able to handle a secondary task (solving insufflator problems) quicker. These results may aid the development of laparoscopy VR training programs in approximating real-life laparoscopic surgery. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.
Estimating the size of an open population using sparse capture-recapture data.

PubMed

Huggins, Richard; Stoklosa, Jakub; Roach, Cameron; Yip, Paul

2018-03-01

Sparse capture-recapture data from open populations are difficult to analyze using currently available frequentist statistical methods. However, in closed capture-recapture experiments, the Chao sparse estimator (Chao, 1989, Biometrics 45, 427-438) may be used to estimate population sizes when there are few recaptures. Here, we extend the Chao (1989) closed population size estimator to the open population setting by using linear regression and extrapolation techniques. We conduct a small simulation study and apply the models to several sparse capture-recapture data sets. © 2017, The International Biometric Society.
Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.
A Naturalistic Investigation of Media Multitasking While Studying and the Effects on Exam Performance

ERIC Educational Resources Information Center

Patterson, Michael C.

2017-01-01

The present study investigated the use of multiple digital media technologies, including social networking platforms, by students while preparing for an examination (media multitasking) and the subsequent effects on exam performance. The level of media multitasking (number of simultaneous media technologies) and duration of study were used as…
The Relationship between Media Multitasking and Executive Function in Early Adolescents

ERIC Educational Resources Information Center

Baumgartner, Susanne E.; Weeda, Wouter D.; van der Heijden, Lisa L.; Huizinga, Mariëtte

2014-01-01

The increasing prevalence of media multitasking among adolescents is concerning because it may be negatively related to goal-directed behavior. This study investigated the relationship between media multitasking and executive function in 523 early adolescents (aged 11-15; 48% girls). The three central components of executive functions (i.e.,…
Reading while Watching Video: The Effect of Video Content on Reading Comprehension and Media Multitasking Ability

ERIC Educational Resources Information Center

Lin, Lin; Lee, Jennifer; Robertson, Tip

2011-01-01

Media multitasking, or engaging in multiple media and tasks simultaneously, is becoming an increasingly popular phenomenon with the development and engagement in social media. This study examines to what extent video content affects students' reading comprehension in media multitasking environments. One hundred and thirty university students were…
You Say Multitasking Like It's a Good Thing

ERIC Educational Resources Information Center

Abaté, Charles J.

2008-01-01

"Multitasking" has developed a certain mantra in our culture, and according to this widely held axiom, people in general and students in particular, can and do function productively and learn efficiently doing several things at once. There also seems to be an unshakable conviction that young students excel in a multitasking environment.…
The Impact of Media Multitasking on Learning

ERIC Educational Resources Information Center

Lee, Jennifer; Lin, Lin; Robertson, Tip

2012-01-01

While multitasking is not a new concept, it has received increasing attention in recent years with the development of new media and technologies. Recent trends appear to suggest that multitasking is on the rise among the younger generation. The purpose of the study is to determine if students obtain more or less information in multitasking…
Time takes space: selective effects of multitasking on concurrent spatial processing.

PubMed

Mäntylä, Timo; Coni, Valentina; Kubik, Veit; Todorov, Ivo; Del Missier, Fabio

2017-08-01

Many everyday activities require coordination and monitoring of complex relations of future goals and deadlines. Cognitive offloading may provide an efficient strategy for reducing control demands by representing future goals and deadlines as a pattern of spatial relations. We tested the hypothesis that multiple-task monitoring involves time-to-space transformational processes, and that these spatial effects are selective with greater demands on coordinate (metric) than categorical (nonmetric) spatial relation processing. Participants completed a multitasking session in which they monitored four series of deadlines, running on different time scales, while making concurrent coordinate or categorical spatial judgments. We expected and found that multitasking taxes concurrent coordinate, but not categorical, spatial processing. Furthermore, males showed a better multitasking performance than females. These findings provide novel experimental evidence for the hypothesis that efficient multitasking involves metric relational processing.
Multitasking kernel for the C and Fortran programming languages

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

Brooks, E.D. III

1984-09-01

A multitasking kernel for the C and Fortran programming languages which runs on the Unix operating system is presented. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the coding, debugging and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessors. The performance evaluation features require no changes in the source code of the application and are implemented as a set of compile and run time options in the kernel.
Comparing host and target environments for distributed Ada programs

NASA Technical Reports Server (NTRS)

Paulk, Mark C.

1986-01-01

The Ada programming language provides a means of specifying logical concurrency by using multitasking. Extending the Ada multitasking concurrency mechanism into a physically concurrent distributed environment which imposes its own requirements can lead to incompatibilities. These problems are discussed. Using distributed Ada for a target system may be appropriate, but when using the Ada language in a host environment, a multiprocessing model may be more suitable than retargeting an Ada compiler for the distributed environment. The tradeoffs between multitasking on distributed targets and multiprocessing on distributed hosts are discussed. Comparisons of the multitasking and multiprocessing models indicate different areas of application.
MultitaskProtDB-II: an update of a database of multitasking/moonlighting proteins

PubMed Central

Franco-Serrano, Luís; Hernández, Sergio; Calvo, Alejandra; Severi, María A; Ferragut, Gabriela; Pérez-Pons, JosepAntoni; Piñol, Jaume; Pich, Òscar; Mozo-Villarias, Ángel; Amela, Isaac

2018-01-01

Abstract Multitasking, or moonlighting, is the capability of some proteins to execute two or more biological functions. MultitaskProtDB-II is a database of multifunctional proteins that has been updated. In the previous version, the information contained was: NCBI and UniProt accession numbers, canonical and additional biological functions, organism, monomeric/oligomeric states, PDB codes and bibliographic references. In the present update, the number of entries has been increased from 288 to 694 moonlighting proteins. MultitaskProtDB-II is continually being curated and updated. The new database also contains the following information: GO descriptors for the canonical and moonlighting functions, three-dimensional structure (for those proteins lacking PDB structure, a model was made using Itasser and Phyre), the involvement of the proteins in human diseases (78% of human moonlighting proteins) and whether the protein is a target of a current drug (48% of human moonlighting proteins). These numbers highlight the importance of these proteins for the analysis and explanation of human diseases and target-directed drug design. Moreover, 25% of the proteins of the database are involved in virulence of pathogenic microorganisms, largely in the mechanism of adhesion to the host. This highlights their importance for the mechanism of microorganism infection and vaccine design. MultitaskProtDB-II is available at http://wallace.uab.es/multitaskII. PMID:29136215
Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults.

PubMed

Moisala, M; Salmela, V; Hietajärvi, L; Salo, E; Carlson, S; Salonen, O; Lonka, K; Hakkarainen, K; Salmela-Aro, K; Alho, K

2016-07-01

The current generation of young people indulges in more media multitasking behavior (e.g., instant messaging while watching videos) in their everyday lives than older generations. Concerns have been raised about how this might affect their attentional functioning, as previous studies have indicated that extensive media multitasking in everyday life may be associated with decreased attentional control. In the current study, 149 adolescents and young adults (aged 13-24years) performed speech-listening and reading tasks that required maintaining attention in the presence of distractor stimuli in the other modality or dividing attention between two concurrent tasks. Brain activity during task performance was measured using functional magnetic resonance imaging (fMRI). We studied the relationship between self-reported daily media multitasking (MMT), task performance and brain activity during task performance. The results showed that in the presence of distractor stimuli, a higher MMT score was associated with worse performance and increased brain activity in right prefrontal regions. The level of performance during divided attention did not depend on MMT. This suggests that daily media multitasking is associated with behavioral distractibility and increased recruitment of brain areas involved in attentional and inhibitory control, and that media multitasking in everyday life does not translate to performance benefits in multitasking in laboratory settings. Copyright © 2016 Elsevier Inc. All rights reserved.
Multitasking scheduler works without OS

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

Howard, D.M.

1982-09-15

Z80 control applications requiring parallel execution of multiple software tasks can use the executive routine described and listed in this article when multitasking is not available via an operating system (OS). Although the routine is not as capable or as transparent to software as the multitasking in a full-scale OS, it is simple to understand and use.
Laptop Multitasking Hinders Classroom Learning for Both Users and Nearby Peers

ERIC Educational Resources Information Center

Sana, Faria; Weston, Tina; Cepeda, Nicholas J.

2013-01-01

Laptops are commonplace in university classrooms. In light of cognitive psychology theory on costs associated with multitasking, we examined the effects of in-class laptop use on student learning in a simulated classroom. We found that participants who multitasked on a laptop during a lecture scored lower on a test compared to those who did not…
Can Students Really Multitask? An Experimental Study of Instant Messaging while Reading

ERIC Educational Resources Information Center

Bowman, Laura L.; Levine, Laura E.; Waite, Bradley M.; Gendron, Michael

2010-01-01

Students often "multitask" with electronic media while doing schoolwork. We examined the effects of one form of media often used in such multitasking, instant messaging (IM). We predicted that students who engaged in IMing while reading a typical academic psychology passage online would take longer to read the passage and would perform more poorly…
The Impact of Multitasking Learning Environments in the Middle Grades

ERIC Educational Resources Information Center

Drinkwine, Timothy

2013-01-01

This research study considers the status of middle school students in the 21st century in terms of their tendency to multitask in their daily lives and the overall influence this multitasking has on teaching and learning environments. Student engagement in the learning environment and students' various learning styles are discussed as primary…
Multitasking and microtasking experience on the NA S Cray-2 and ACF Cray X-MP

NASA Technical Reports Server (NTRS)

Raiszadeh, Farhad

1987-01-01

The fast Fourier transform (FFT) kernel of the NAS benchmark program has been utilized to experiment with the multitasking library on the Cray-2 and Cray X-MP/48, and microtasking directives on the Cray X-MP. Some performance figures are shown, and the state of multitasking software is described.

Rewarding Multitasking: Negative Effects of an Incentive on Problem Solving under Divided Attention

ERIC Educational Resources Information Center

Wieth, Mareike B.; Burns, Bruce D.

2014-01-01

Research has consistently shown negative effects of multitasking on tasks such as problem solving. This study was designed to investigate the impact of an incentive when solving problems in a multitasking situation. Incentives have generally been shown to increase problem solving (e.g., Wieth & Burns, 2006), however, it is unclear whether an…
3D face recognition based on multiple keypoint descriptors and sparse representation.

PubMed

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.
Older Adult Multitasking Performance Using a Gaze-Contingent Useful Field of View.

PubMed

Ward, Nathan; Gaspar, John G; Neider, Mark B; Crowell, James; Carbonari, Ronald; Kaczmarski, Hank; Ringer, Ryan V; Johnson, Aaron P; Loschky, Lester C; Kramer, Arthur F

2018-03-01

Objective We implemented a gaze-contingent useful field of view paradigm to examine older adult multitasking performance in a simulated driving environment. Background Multitasking refers to the ability to manage multiple simultaneous streams of information. Recent work suggests that multitasking declines with age, yet the mechanisms supporting these declines are still debated. One possible framework to better understand this phenomenon is the useful field of view, or the area in the visual field where information can be attended and processed. In particular, the useful field of view allows for the discrimination of two competing theories of real-time multitasking, a general interference account and a tunneling account. Methods Twenty-five older adult subjects completed a useful field of view task that involved discriminating the orientation of lines in gaze-contingent Gabor patches appearing at varying eccentricities (based on distance from the fovea) as they operated a vehicle in a driving simulator. In half of the driving scenarios, subjects also completed an auditory two-back task to manipulate cognitive workload, and during some trials, wind was introduced as a means to alter general driving difficulty. Results Consistent with prior work, indices of driving performance were sensitive to both wind and workload. Interestingly, we also observed a decline in Gabor patch discrimination accuracy under high cognitive workload regardless of eccentricity, which provides support for a general interference account of multitasking. Conclusion The results showed that our gaze-contingent useful field of view paradigm was able to successfully examine older adult multitasking performance in a simulated driving environment. Application This study represents the first attempt to successfully measure dynamic changes in the useful field of view for older adults completing a multitasking scenario involving driving.
Video game training enhances cognitive control in older adults.

PubMed

Anguera, J A; Boccanfuso, J; Rintoul, J L; Al-Hashimi, O; Faraji, F; Janowich, J; Kong, E; Larraburo, Y; Rolle, C; Johnston, E; Gazzaley, A

2013-09-05

Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generating interference as the result of fundamental information processing limitations. It is clear that multitasking behaviour has become ubiquitous in today's technologically dense world, and substantial evidence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population. Here we show that multitasking performance, as assessed with a custom-designed three-dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalography, were remediated by multitasking training (enhanced midline frontal theta power and frontal-posterior theta coherence). Critically, this training resulted in performance benefits that extended to untrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings highlight the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, of how a custom-designed video game can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement.
Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds

PubMed Central

Boik, John C; Newman, Robert A

2008-01-01

Background Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Results Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Conclusion Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans. PMID:18554402
Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds.

PubMed

Boik, John C; Newman, Robert A

2008-06-13

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans.
Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.

PubMed

Zhu, Xiaofeng; Li, Xuelong; Zhang, Shichao; Ju, Chunhua; Wu, Xindong

2017-06-01

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.
Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression.

PubMed

Gijsberts, Arjan; Metta, Giorgio

2013-05-01

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context. Rather than developing a novel algorithm from the ground up, the method is based on the thoroughly studied Gaussian Process Regression algorithm, therefore ensuring a solid theoretical foundation. Non-linearity and a bounded update complexity are achieved simultaneously by means of a finite dimensional random feature mapping that approximates a kernel function. As a result, the computational cost for each update remains constant over time. Finally, algorithmic simplicity and support for automated hyperparameter optimization ensures convenience when employed in practice. Empirical validation on a number of synthetic and real-life learning problems confirms that the performance of Incremental Sparse Spectrum Gaussian Process Regression is superior with respect to the popular Locally Weighted Projection Regression, while computational requirements are found to be significantly lower. The method is therefore particularly suited for learning with real-time constraints or when computational resources are limited. Copyright © 2012 Elsevier Ltd. All rights reserved.
Using an Activity to Simulate the Dangers of Multitasking with Technology while Walking

ERIC Educational Resources Information Center

Lazaros, Edward J.; Xu, Renmei; Londt, Susan

2012-01-01

People are increasingly trying to multitask while walking. Text messaging while walking is a significant area for concern. The number of text messages sent is expected to be more than 8 trillion in 2012. Texting is becoming so commonplace that people use this technology while engaged in other activities. The dangers of multitasking have hit the…
Examining the Impact of Off-Task Multi-Tasking with Technology on Real-Time Classroom Learning

ERIC Educational Resources Information Center

Wood, Eileen; Zivcakova, Lucia; Gentile, Petrice; Archer, Karin; De Pasquale, Domenica; Nosko, Amanda

2012-01-01

The purpose of the present study was to examine the impact of multi-tasking with digital technologies while attempting to learn from real-time classroom lectures in a university setting. Four digitally-based multi-tasking activities (texting using a cell-phone, emailing, MSN messaging and Facebook[TM]) were compared to 3 control groups…
Measurement and Evidence of Computer-Based Task Switching and Multitasking by "Net Generation" Students

ERIC Educational Resources Information Center

Judd, Terry; Kennedy, Gregor

2011-01-01

Logs of on-campus computer and Internet usage were used to conduct a study of computer-based task switching and multitasking by undergraduate medical students. A detailed analysis of over 6000 individual sessions revealed that while a majority of students engaged in both task switching and multitasking behaviours, they did so less frequently than…
Can Teens Really Do It All?: Techno-Multitasking, Learning, and Performance

ERIC Educational Resources Information Center

Bradley, Karen

2011-01-01

Many adults and students today think of themselves as excellent multitaskers--switching from task to task or from task to play in a nanosecond. Yet the pings and tweets their devices emit interrupt them in ways that are more problematic than they think. One of the powerful myths in the culture today is that multitasking is efficient for work or…
Multitasking the code ARC3D. [for computational fluid dynamics

NASA Technical Reports Server (NTRS)

Barton, John T.; Hsiung, Christopher C.

1986-01-01

The CRAY multitasking system was developed in order to utilize all four processors and sharply reduce the wall clock run time. This paper describes the techniques used to modify the computational fluid dynamics code ARC3D for this run and analyzes the achieved speedup. The ARC3D code solves either the Euler or thin-layer N-S equations using an implicit approximate factorization scheme. Results indicate that multitask processing can be used to achieve wall clock speedup factors of over three times, depending on the nature of the program code being used. Multitasking appears to be particularly advantageous for large-memory problems running on multiple CPU computers.
Clustered Multi-Task Learning for Automatic Radar Target Recognition

PubMed Central

Li, Cong; Bao, Weimin; Xu, Luping; Zhang, Hua

2017-01-01

Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. PMID:28953267
Accurate Segmentation of CT Male Pelvic Organs via Regression-based Deformable Models and Multi-task Random Forests

PubMed Central

Gao, Yaozong; Shao, Yeqin; Lian, Jun; Wang, Andrew Z.; Chen, Ronald C.

2016-01-01

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation. PMID:26800531
Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

PubMed

Xu, Yuting; Ma, Junshui; Liaw, Andy; Sheridan, Robert P; Svetnik, Vladimir

2017-10-23

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.
Media multitasking behavior: concurrent television and computer usage.

PubMed

Brasel, S Adam; Gips, James

2011-09-01

Changes in the media landscape have made simultaneous usage of the computer and television increasingly commonplace, but little research has explored how individuals navigate this media multitasking environment. Prior work suggests that self-insight may be limited in media consumption and multitasking environments, reinforcing a rising need for direct observational research. A laboratory experiment recorded both younger and older individuals as they used a computer and television concurrently, multitasking across television and Internet content. Results show that individuals are attending primarily to the computer during media multitasking. Although gazes last longer on the computer when compared to the television, the overall distribution of gazes is strongly skewed toward very short gazes only a few seconds in duration. People switched between media at an extreme rate, averaging more than 4 switches per min and 120 switches over the 27.5-minute study exposure. Participants had little insight into their switching activity and recalled their switching behavior at an average of only 12 percent of their actual switching rate revealed in the objective data. Younger individuals switched more often than older individuals, but other individual differences such as stated multitasking preference and polychronicity had little effect on switching patterns or gaze duration. This overall pattern of results highlights the importance of exploring new media environments, such as the current drive toward media multitasking, and reinforces that self-monitoring, post hoc surveying, and lay theory may offer only limited insight into how individuals interact with media.
Media Multitasking Behavior: Concurrent Television and Computer Usage

PubMed Central

Gips, James

2011-01-01

Abstract Changes in the media landscape have made simultaneous usage of the computer and television increasingly commonplace, but little research has explored how individuals navigate this media multitasking environment. Prior work suggests that self-insight may be limited in media consumption and multitasking environments, reinforcing a rising need for direct observational research. A laboratory experiment recorded both younger and older individuals as they used a computer and television concurrently, multitasking across television and Internet content. Results show that individuals are attending primarily to the computer during media multitasking. Although gazes last longer on the computer when compared to the television, the overall distribution of gazes is strongly skewed toward very short gazes only a few seconds in duration. People switched between media at an extreme rate, averaging more than 4 switches per min and 120 switches over the 27.5-minute study exposure. Participants had little insight into their switching activity and recalled their switching behavior at an average of only 12 percent of their actual switching rate revealed in the objective data. Younger individuals switched more often than older individuals, but other individual differences such as stated multitasking preference and polychronicity had little effect on switching patterns or gaze duration. This overall pattern of results highlights the importance of exploring new media environments, such as the current drive toward media multitasking, and reinforces that self-monitoring, post hoc surveying, and lay theory may offer only limited insight into how individuals interact with media. PMID:21381969
The effect of mild motion sickness and sopite syndrome on multitasking cognitive performance.

PubMed

Matsangas, Panagiotis; McCauley, Michael E; Becker, William

2014-09-01

In this study, we investigated the effects of mild motion sickness and sopite syndrome on multitasking cognitive performance. Despite knowledge on general motion sickness, little is known about the effect of motion sickness and sopite syndrome on multitasking cognitive performance. Specifically, there is a gap in existing knowledge in the gray area of mild motion sickness. Fifty-one healthy individuals performed a multitasking battery. Three independent groups of participants were exposed to two experimental sessions. Two groups received motion only in the first or the second session, whereas the control group did not receive motion. Measurements of motion sickness, sopite syndrome, alertness, and performance were collected during the experiment Only during the second session, motion sickness and sopite syndrome had a significant negative association with cognitive performance. Significant performance differences between symptomatic and asymptomatic participants in the second session were identified in composite (9.43%), memory (31.7%), and arithmetic (14.7%) task scores. The results suggest that performance retention between sessions was not affected by mild motion sickness. Multitasking cognitive performance declined even when motion sickness and soporific symptoms were mild. The results also show an order effect. We postulate that the differential effect of session on the association between symptomatology and multitasking performance may be related to the attentional resources allocated to performing the multiple tasks. Results suggest an inverse relationship between motion sickness effects on performance and the cognitive effort focused on performing a task. Even mild motion sickness has potential implications for multitasking operational performance.
Thermal Analysis for Monitoring Effects of Shock-Induced Physical, Mechanical, and Chemical Changes in Materials

DTIC Science & Technology

2015-01-19

MS WINDOWS platform, which enables multitasking with simultaneous evaluation and operation 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13...measurement and analysis software for data acquisition, storage and evaluation with MS WINDOWS platform, which enables multitasking with simultaneous...Proteus measurement and analysis software for data acquisition, storage and evaluation with MS WINDOWS platform, which enables multitasking with

Investigating Multitasking in High-Functioning Adolescents with Autism Spectrum Disorders Using the Virtual Errands Task

ERIC Educational Resources Information Center

Rajendran, Gnanathusharan; Law, Anna S.; Logie, Robert H.; van der Meulen, Marian; Fraser, Diane; Corley, Martin

2011-01-01

Using a modified version of the Virtual Errands Task (VET; McGeorge et al. in "Presence-Teleop Virtual Environ" 10(4):375-383, 2001), we investigated the executive ability of multitasking in 18 high-functioning adolescents with ASD and 18 typically developing adolescents. The VET requires multitasking (Law et al. in "Acta Psychol" 122(1):27-44,…
Task errors by emergency physicians are associated with interruptions, multitasking, fatigue and working memory capacity: a prospective, direct observation study.

PubMed

Westbrook, Johanna I; Raban, Magdalena Z; Walter, Scott R; Douglas, Heather

2018-01-09

Interruptions and multitasking have been demonstrated in experimental studies to reduce individuals' task performance. These behaviours are frequently used by clinicians in high-workload, dynamic clinical environments, yet their effects have rarely been studied. To assess the relative contributions of interruptions and multitasking by emergency physicians to prescribing errors. 36 emergency physicians were shadowed over 120 hours. All tasks, interruptions and instances of multitasking were recorded. Physicians' working memory capacity (WMC) and preference for multitasking were assessed using the Operation Span Task (OSPAN) and Inventory of Polychronic Values. Following observation, physicians were asked about their sleep in the previous 24 hours. Prescribing errors were used as a measure of task performance. We performed multivariate analysis of prescribing error rates to determine associations with interruptions and multitasking, also considering physician seniority, age, psychometric measures, workload and sleep. Physicians experienced 7.9 interruptions/hour. 28 clinicians were observed prescribing 239 medication orders which contained 208 prescribing errors. While prescribing, clinicians were interrupted 9.4 times/hour. Error rates increased significantly if physicians were interrupted (rate ratio (RR) 2.82; 95% CI 1.23 to 6.49) or multitasked (RR 1.86; 95% CI 1.35 to 2.56) while prescribing. Having below-average sleep showed a >15-fold increase in clinical error rate (RR 16.44; 95% CI 4.84 to 55.81). WMC was protective against errors; for every 10-point increase on the 75-point OSPAN, a 19% decrease in prescribing errors was observed. There was no effect of polychronicity, workload, physician gender or above-average sleep on error rates. Interruptions, multitasking and poor sleep were associated with significantly increased rates of prescribing errors among emergency physicians. WMC mitigated the negative influence of these factors to an extent. These results confirm experimental findings in other fields and raise questions about the acceptability of the high rates of multitasking and interruption in clinical environments. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Online Multitasking Line-Scan Imaging Techniques for Simultaneous Safety and Quality Evaluation of Apples

NASA Astrophysics Data System (ADS)

Kim, Moon Sung; Lee, Kangjin; Chao, Kaunglin; Lefcourt, Alan; Cho, Byung-Kwan; Jun, Won

We developed a push-broom, line-scan imaging system capable of simultaneous measurements of reflectance and fluorescence. The system allows multitasking inspections for quality and safety attributes of apples due to its dynamic capabilities in simultaneously capturing fluorescence and reflectance, and selectivity in multispectral bands. A multitasking image-based inspection system for online applications has been suggested in that a single imaging device that could perform a multitude of both safety and quality inspection needs. The presented multitask inspection approach in online applications may provide an economically viable means for a number of food processing industries being able to adapt to operate and meet the dynamic and specific inspection and sorting needs.
Domestic outsourcing and multitasking: How much do they really contribute?

PubMed

Sullivan, Oriel; Gershuny, Jonathan

2013-09-01

The bulk of responsibility for domestic work and childcare in heterosexual couples falls on women. But the means they find to cope with this load, and how these means relate to the factors underpinning the division of labor are not often studied. Two much-cited ways of reducing overall work time are purchasing domestic assistance (outsourcing) and the multitasking of domestic/caring tasks. Using UK 2000/2001 time-use data (N=4196 couples), we find domestic outsourcing is related to having dependent children and to partners' resources, but has little impact on the total domestic/caring workload of either partner. Nor can outsourcing account for the reduction in women's unpaid labor with increasing economic resources. Wives spend more time multitasking than husbands, but their proportion of multitasked domestic time is similar, and is not affected by resources or dependent children. Domestic multitasking seems to be more related to opportunity (time at home) than to time pressure. Copyright © 2013 Elsevier Inc. All rights reserved.
Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.
Multitasking 3-D forward modeling using high-order finite difference methods on the Cray X-MP/416

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

Terki-Hassaine, O.; Leiss, E.L.

1988-01-01

The CRAY X-MP/416 was used to multitask 3-D forward modeling by the high-order finite difference method. Flowtrace analysis reveals that the most expensive operation in the unitasked program is a matrix vector multiplication. The in-core and out-of-core versions of a reentrant subroutine can perform any fraction of the matrix vector multiplication independently, a pattern compatible with multitasking. The matrix vector multiplication routine can be distributed over two to four processors. The rest of the program utilizes the microtasking feature that lets the system treat independent iterations of DO-loops as subtasks to be performed by any available processor. The availability ofmore » the Solid-State Storage Device (SSD) meant the I/O wait time was virtually zero. A performance study determined a theoretical speedup, taking into account the multitasking overhead. Multitasking programs utilizing both macrotasking and microtasking features obtained actual speedups that were approximately 80% of the ideal speedup.« less
Parallel processing a three-dimensional free-lagrange code

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

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

1989-01-01

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

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

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

1988-01-01

A three-dimensional, time-dependent Free-Lagrange hydrodynamics code has been multitasked and autotasked on a Cray X-MP/416. The multitasking was done by using the Los Alamos Multitasking Control Library, which is a superset of the Cray multitasking library. Autotasking is done by using constructs which are only comment cards if the source code is not run through a preprocessor. The 3-D algorithm has presented a number of problems that simpler algorithms, such as 1-D hydrodynamics, did not exhibit. Problems in converting the serial code, originally written for a Cray 1, to a multitasking code are discussed, Autotasking of a rewritten version ofmore » the code is discussed. Timing results for subroutines and hot spots in the serial code are presented and suggestions for additional tools and debugging aids are given. Theoretical speedup results obtained from Amdahl's law and actual speedup results obtained on a dedicated machine are presented. Suggestions for designing large parallel codes are given. 8 refs., 13 figs.« less
Structured sparse linear graph embedding.

PubMed

Wang, Haixian

2012-03-01

Subspace learning is a core issue in pattern recognition and machine learning. Linear graph embedding (LGE) is a general framework for subspace learning. In this paper, we propose a structured sparse extension to LGE (SSLGE) by introducing a structured sparsity-inducing norm into LGE. Specifically, SSLGE casts the projection bases learning into a regression-type optimization problem, and then the structured sparsity regularization is applied to the regression coefficients. The regularization selects a subset of features and meanwhile encodes high-order information reflecting a priori structure information of the data. The SSLGE technique provides a unified framework for discovering structured sparse subspace. Computationally, by using a variational equality and the Procrustes transformation, SSLGE is efficiently solved with closed-form updates. Experimental results on face image show the effectiveness of the proposed method. Copyright © 2011 Elsevier Ltd. All rights reserved.
An Updated Version of the U.S. Air Force Multi-Attribute Task Battery (AF-MATB)

DTIC Science & Technology

2014-08-01

assessing human performance in a controlled multitask environment. The most recent release of AF-MATB contains numerous improvements and additions...Strategic Behavior, MATB, Multitasking , Task Battery, Simulator, Multi-Attribute Task Battery, Automation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...performance and multitasking strategy. As a result, a specific Information Throughput (IT) Mode was designed to customize the task to fit the Human
Identifying Knowledge, Skill, and Ability Requirements for Contracting Officer Representatives in Deployed Environments

DTIC Science & Technology

2015-06-19

field, able to operate independently (self-tasked) and are able to multitask . 4 CORs comprehend the processes for coordinating, inspecting, and... multitask . 9.) They understand the duties and responsibilities set forth in the COR delegation letter and ensure the COR file is documented...to multitask . CORs comprehend the processes for coordinating, inspecting, and accepting deliveries (and/or services) and the procedures to pay
Multitasking during social interactions in adolescence and early adulthood

PubMed Central

Mills, Kathryn L.; Dumontheil, Iroise; Speekenbrink, Maarten; Blakemore, Sarah-Jayne

2015-01-01

Multitasking is part of the everyday lives of both adolescents and adults. We often multitask during social interactions by simultaneously keeping track of other non-social information. Here, we examined how keeping track of non-social information impacts the ability to navigate social interactions in adolescents and adults. Participants aged 11–17 and 22–30 years old were instructed to carry out two tasks, one social and one non-social, within each trial. The social task involved referential communication, requiring participants to use social cues to guide their decisions, which sometimes required taking a different perspective. The non-social task manipulated cognitive load by requiring participants to remember non-social information in the form of one two-digit number (low load) or three two-digit numbers (high load) presented before each social task stimulus. Participants showed performance deficits when under high cognitive load and when the social task involved taking a different perspective, and individual differences in both trait perspective taking and working memory capacity predicted performance. Overall, adolescents were less adept at multitasking than adults when under high cognitive load. These results suggest that multitasking during social interactions incurs performance deficits, and that adolescents are more sensitive than adults to the effects of cognitive load while multitasking. PMID:26715991
Multitasking during social interactions in adolescence and early adulthood.

PubMed

Mills, Kathryn L; Dumontheil, Iroise; Speekenbrink, Maarten; Blakemore, Sarah-Jayne

2015-11-01

Multitasking is part of the everyday lives of both adolescents and adults. We often multitask during social interactions by simultaneously keeping track of other non-social information. Here, we examined how keeping track of non-social information impacts the ability to navigate social interactions in adolescents and adults. Participants aged 11-17 and 22-30 years old were instructed to carry out two tasks, one social and one non-social, within each trial. The social task involved referential communication, requiring participants to use social cues to guide their decisions, which sometimes required taking a different perspective. The non-social task manipulated cognitive load by requiring participants to remember non-social information in the form of one two-digit number (low load) or three two-digit numbers (high load) presented before each social task stimulus. Participants showed performance deficits when under high cognitive load and when the social task involved taking a different perspective, and individual differences in both trait perspective taking and working memory capacity predicted performance. Overall, adolescents were less adept at multitasking than adults when under high cognitive load. These results suggest that multitasking during social interactions incurs performance deficits, and that adolescents are more sensitive than adults to the effects of cognitive load while multitasking.
Cognitive structure, flexibility, and plasticity in human multitasking-An integrative review of dual-task and task-switching research.

PubMed

Koch, Iring; Poljac, Edita; Müller, Hermann; Kiesel, Andrea

2018-06-01

Numerous studies showed decreased performance in situations that require multiple tasks or actions relative to appropriate control conditions. Because humans often engage in such multitasking activities, it is important to understand how multitasking affects performance. In the present article, we argue that research on dual-task interference and sequential task switching has proceeded largely separately using different experimental paradigms and methodology. In our article we aim at organizing this complex set of research in terms of three complementary research perspectives on human multitasking. One perspective refers to structural accounts in terms of cognitive bottlenecks (i.e., critical processing stages). A second perspective refers to cognitive flexibility in terms of the underlying cognitive control processes. A third perspective emphasizes cognitive plasticity in terms of the influence of practice on human multitasking abilities. With our review article we aimed at highlighting the value of an integrative position that goes beyond isolated consideration of a single theoretical research perspective and that broadens the focus from single experimental paradigms (dual task and task switching) to favor instead a view that emphasizes the fundamental similarity of the underlying cognitive mechanisms across multitasking paradigms. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

PubMed Central

Jie, Biao; Liu, Mingxia; Liu, Jun

2016-01-01

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313
A neuropsychological investigation of multitasking in HIV infection: implications for everyday functioning.

PubMed

Scott, J Cobb; Woods, Steven Paul; Vigil, Ofilio; Heaton, Robert K; Schweinsburg, Brian C; Ellis, Ronald J; Grant, Igor; Marcotte, Thomas D

2011-07-01

A subset of individuals with HIV-associated neurocognitive impairment experience related deficits in "real world" functioning (i.e., independently performing instrumental activities of daily living [IADL]). While performance-based tests of everyday functioning are reasonably sensitive to HIV-associated IADL declines, questions remain regarding the extent to which these tests' highly structured nature fully captures the inherent complexities of daily life. The aim of this study was to assess the predictive and ecological validity of a novel multitasking measure in HIV infection. Participants included 60 individuals with HIV infection (HIV+) and 25 demographically comparable seronegative adults (HIV-). Participants were administered a comprehensive neuropsychological battery, questionnaires assessing mood and everyday functioning, and a novel standardized test of multitasking, which involved balancing the demands of four interconnected performance-based functional tasks (i.e., financial management, cooking, medication management, and telephone communication). HIV+ individuals demonstrated significantly worse overall performance, fewer simultaneous task attempts, and increased errors on the multitasking test as compared to the HIV- group. Within the HIV+ sample, multitasking impairments were modestly associated with deficits on standard neuropsychological measures of executive functions, episodic memory, attention/working memory, and information processing speed, providing preliminary evidence for convergent validity. More importantly, multivariate prediction models revealed that multitasking deficits were uniquely predictive of IADL dependence beyond the effects of depression and global neurocognitive impairment, with excellent sensitivity (86%), but modest specificity (57%). Taken together, these data indicate that multitasking ability may play an important role in successful everyday functioning in HIV+ individuals. PsycINFO Database Record (c) 2011 APA, all rights reserved.
A Neuropsychological Investigation of Multitasking in HIV Infection: Implications for Everyday Functioning

PubMed Central

Scott, J. Cobb; Woods, Steven Paul; Vigil, Ofilio; Heaton, Robert K.; Schweinsburg, Brian C.; Ellis, Ronald J.; Grant, Igor; Marcotte, Thomas D.

2010-01-01

Objective A subset of individuals with HIV-associated neurocognitive impairment experience related deficits in “real world” functioning (i.e., independently performing instrumental activities of daily living [IADL]). While performance-based tests of everyday functioning are reasonably sensitive to HIV-associated IADL declines, questions remain regarding the extent to which these tests’ highly structured nature fully captures the inherent complexities of daily life. The aim of this study was to assess the predictive and ecological validity of a novel multitasking measure in HIV infection. Method Participants included 60 individuals with HIV infection (HIV+) and 25 demographically comparable seronegative adults (HIV−). Participants were administered a comprehensive neuropsychological battery, questionnaires assessing mood and everyday functioning, and a novel standardized test of multitasking, which involved balancing the demands of four interconnected performance-based functional tasks (i.e., financial management, cooking, medication management, and telephone communication). Results HIV+ individuals demonstrated significantly worse overall performance, fewer simultaneous task attempts, and increased errors on the multitasking test as compared to the HIV− sample. Within the HIV+ sample, multitasking impairments were modestly associated with deficits on standard neuropsychological measures of executive functions, episodic memory, attention/working memory, and information processing speed, providing preliminary evidence for convergent validity. More importantly, multivariate prediction models revealed that multitasking deficits were uniquely predictive of IADL dependence beyond the effects of depression and global neurocognitive impairment, with excellent sensitivity (86%), but modest specificity (57%). Conclusions Taken together, these data indicate that multitasking ability may play an important role in successful everyday functioning in HIV+ individuals. PMID:21401259
A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

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

DTIC Science & Technology

2014-10-01

finally get you through some of your most important reading, and make you smarter in the process. Stop Trying to Multitask More and more evidence...is emerging from neuroscience that the brain simply doesn’t multitask well. In fact, trying to mul- titask introduces massive inefficiencies and...actually wastes time. So, how do busy executive types like you avoid multitask - ing as part of the job description? First, recognize when you are trying
Media use and psychosocial adjustment in children and adolescents.

PubMed

Limtrakul, Nicha; Louthrenoo, Orawan; Narkpongphun, Atsawin; Boonchooduang, Nonglak; Chonchaiya, Weerasak

2018-03-01

Currently, television and new forms of media are readily available to children and adolescents in their daily lives. Excessive use of media can lead to negative physical and psychosocial health effects. This study aimed to describe children's media use, including media multitasking, as well as the associations between media use and their psychosocial adjustment. This study recruited 339 participants aged 10-15 years from an international school. The children and their care givers were asked to complete the Strengths and Difficulties Questionnaire independently to evaluate the psychosocial problems of the children. The mean age of the study participants was 12.4 ± 1.5 years, who were recruited from grades 5 to 9. Multitasking media use was reported in 59.3% of participants. The average total media exposure time was 7.0 h/day. The behavioural problem scores from self-reports were greater with increased media use time. After adjusting for confounding variables, the school report and sleep problems were among the factors associated with the total behavioural problem scores from the multiple linear regression analysis (P = 0.001 and <0.001, respectively), whereas age and average total media exposure time were significantly associated with the prosocial behaviour scores reported by the children (P = 0.004 and 0.02, respectively). Multitasking media use was not significantly associated with the total difficulties scores or the prosocial behaviour scores in this study. Increased media use time was significantly associated with decreased prosocial behaviour scores in children in this study. This can provide important information to parents regarding media use in children. © 2017 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

Finding Meaning in Medication Reconciliation Using Electronic Health Records: Qualitative Analysis in Safety Net Primary and Specialty Care

PubMed Central

Matta, George Yaccoub; Khoong, Elaine C; Lyles, Courtney R; Schillinger, Dean

2018-01-01

Background Safety net health systems face barriers to effective ambulatory medication reconciliation for vulnerable populations. Although some electronic health record (EHR) systems offer safety advantages, EHR use may affect the quality of patient-provider communication. Objective This mixed-methods observational study aimed to develop a conceptual framework of how clinicians balance the demands and risks of EHR and communication tasks during medication reconciliation discussions in a safety net system. Methods This study occurred 3 to 16 (median 9) months after new EHR implementation in five academic public hospital clinics. We video recorded visits between English-/Spanish-speaking patients and their primary/specialty care clinicians. We analyzed the proportion of medications addressed and coded time spent on nonverbal tasks during medication reconciliation as “multitasking EHR use,” “silent EHR use,” “non-EHR multitasking,” and “focused patient-clinician talk.” Finally, we analyzed communication patterns to develop a conceptual framework. Results We examined 35 visits (17%, 6/35 Spanish) between 25 patients (mean age 57, SD 11 years; 44%, 11/25 women; 48%, 12/25 Hispanic; and 20%, 5/25 with limited health literacy) and 25 clinicians (48%, 12/25 primary care). Patients had listed a median of 7 (IQR 5-12) relevant medications, and clinicians addressed a median of 3 (interquartile range [IQR] 1-5) medications. The median duration of medication reconciliation was 2.1 (IQR 1.0-4.2) minutes, comprising a median of 10% (IQR 3%-17%) of visit time. Multitasking EHR use occurred in 47% (IQR 26%-70%) of the medication reconciliation time. Silent EHR use and non-EHR multitasking occurred a smaller proportion of medication reconciliation time, with a median of 0% for both. Focused clinician-patient talk occurred a median of 24% (IQR 0-39%) of medication reconciliation time. Five communication patterns with EHR medication reconciliation were observed: (1) typical EHR multitasking for medication reconciliation, (2) dynamic EHR use to negotiate medication discrepancies, (3) focused patient-clinician talk for medication counseling and addressing patient concerns, (4) responding to patient concerns while maintaining EHR use, and (5) using EHRs to engage patients during medication reconciliation. We developed a conceptual diagram representing the dilemma of the multitasking clinician during medication reconciliation. Conclusions Safety net visits involve multitasking EHR use during almost half of medication reconciliation time. The multitasking clinician balances the cognitive and emotional demands posed by incoming information from multiple sources, attempts to synthesize and act on this information through EHR and communication tasks, and adopts strategies of silent EHR use and focused patient-clinician talk that may help mitigate the risks of multitasking. Future studies should explore diverse patient perspectives about clinician EHR multitasking, clinical outcomes related to EHR multitasking, and human factors and systems engineering interventions to improve the safety of EHR use during the complex process of medication reconciliation. PMID:29735477
The Effects of Transcranial Direct Current Stimulation (tDCS) on Multitasking Throughput Capacity

PubMed Central

Nelson, Justin; McKinley, Richard A.; Phillips, Chandler; McIntire, Lindsey; Goodyear, Chuck; Kreiner, Aerial; Monforton, Lanie

2016-01-01

Background: Multitasking has become an integral attribute associated with military operations within the past several decades. As the amount of information that needs to be processed during these high level multitasking environments exceeds the human operators' capabilities, the information throughput capacity reaches an asymptotic limit. At this point, the human operator can no longer effectively process and respond to the incoming information resulting in a plateau or decline in performance. The objective of the study was to evaluate the efficacy of a non-invasive brain stimulation technique known as transcranial direct current stimulation (tDCS) applied to a scalp location over the left dorsolateral prefrontal cortex (lDLPFC) to improve information processing capabilities during a multitasking environment. Methods: The study consisted of 20 participants from Wright-Patterson Air Force Base (16 male and 4 female) with an average age of 31.1 (SD = 4.5). Participants were randomly assigned into two groups, each consisting of eight males and two females. Group one received 2 mA of anodal tDCS and group two received sham tDCS over the lDLPFC on their testing day. Results: The findings indicate that anodal tDCS significantly improves the participants' information processing capability resulting in improved performance compared to sham tDCS. For example, the multitasking throughput capacity for the sham tDCS group plateaued near 1.0 bits/s at the higher baud input (2.0 bits/s) whereas the anodal tDCS group plateaued near 1.3 bits/s. Conclusion: The findings provided new evidence that tDCS has the ability to augment and enhance multitasking capability in a human operator. Future research should be conducted to determine the longevity of the enhancement of transcranial direct current stimulation on multitasking performance, which has yet to be accomplished. PMID:27965553
Everyday Multitasking Abilities in Older HIV+ Adults: Neurobehavioral Correlates and the Mediating Role of Metacognition.

PubMed

Fazeli, P L; Casaletto, K B; Woods, S P; Umlauf, A; Scott, J C; Moore, D J

2017-12-01

The prevalence of older adults living with HIV is rising, as is their risk for everyday functioning problems associated with neurocognitive dysfunction. Multitasking, the ability to maintain and carry out subgoals in support of a larger goal, is a multidimensional skill ubiquitous during most real-life tasks and associated with prefrontal networks that are vulnerable in HIV. Understanding factors associated with multitasking will improve characterization of HIV-associated neurocognitive disorders. Metacognition is also associated with frontal systems, is impaired among individuals with HIV, and may contribute to multitasking. Ninety-nine older (≥50 years) adults with HIV completed: the Everyday Multitasking Test (MT), a performance-based measure during which participants concurrently attempt four everyday tasks (e.g., medication management) within a time limit; a comprehensive neuropsychological battery; measures of metacognition regarding their MT performance (e.g., metacognitive knowledge and online awareness). Better global neuropsychological performance (i.e., average T-score across all domains) was associated with better Everyday MT total scores (rho = 0.34; p < .001), as was global metacognition (rho = 0.37, p < .01). Bootstrapping mediation analysis revealed global metacognition was a significant partial mediator between neurocognition and Everyday MT (b = 0.09, 95% confidence interval [CI] = 0.01, 0.25). Specifically, metacognitive knowledge (but not online awareness) drove this mediation (b = 0.13, 95% CI = 0.03, 0.27). Consistent with findings among younger persons with HIV, neuropsychological performance is strongly associated with a complex, laboratory-based test of everyday multitasking, and metacognition of task performance was a pathway through which successful multitasking occurred. Interventions aimed at modifying metacognition to improve daily functioning may be warranted among older adults with HIV. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
The functional neuroanatomy of multitasking: combining dual tasking with a short term memory task.

PubMed

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan

2013-09-01

Insight into the neural architecture of multitasking is crucial when investigating the pathophysiology of multitasking deficits in clinical populations. Presently, little is known about how the brain combines dual-tasking with a concurrent short-term memory task, despite the relevance of this mental operation in daily life and the frequency of complaints related to this process, in disease. In this study we aimed to examine how the brain responds when a memory task is added to dual-tasking. Thirty-three right-handed healthy volunteers (20 females, mean age 39.9 ± 5.8) were examined with functional brain imaging (fMRI). The paradigm consisted of two cross-modal single tasks (a visual and auditory temporal same-different task with short delay), a dual-task combining both single tasks simultaneously and a multi-task condition, combining the dual-task with an additional short-term memory task (temporal same-different visual task with long delay). Dual-tasking compared to both individual visual and auditory single tasks activated a predominantly right-sided fronto-parietal network and the cerebellum. When adding the additional short-term memory task, a larger and more bilateral frontoparietal network was recruited. We found enhanced activity during multitasking in components of the network that were already involved in dual-tasking, suggesting increased working memory demands, as well as recruitment of multitask-specific components including areas that are likely to be involved in online holding of visual stimuli in short-term memory such as occipito-temporal cortex. These results confirm concurrent neural processing of a visual short-term memory task during dual-tasking and provide evidence for an effective fMRI multitasking paradigm. © 2013 Elsevier Ltd. All rights reserved.
The Effects of Transcranial Direct Current Stimulation (tDCS) on Multitasking Throughput Capacity.

PubMed

Nelson, Justin; McKinley, Richard A; Phillips, Chandler; McIntire, Lindsey; Goodyear, Chuck; Kreiner, Aerial; Monforton, Lanie

2016-01-01

Background: Multitasking has become an integral attribute associated with military operations within the past several decades. As the amount of information that needs to be processed during these high level multitasking environments exceeds the human operators' capabilities, the information throughput capacity reaches an asymptotic limit. At this point, the human operator can no longer effectively process and respond to the incoming information resulting in a plateau or decline in performance. The objective of the study was to evaluate the efficacy of a non-invasive brain stimulation technique known as transcranial direct current stimulation (tDCS) applied to a scalp location over the left dorsolateral prefrontal cortex (lDLPFC) to improve information processing capabilities during a multitasking environment. Methods: The study consisted of 20 participants from Wright-Patterson Air Force Base (16 male and 4 female) with an average age of 31.1 (SD = 4.5). Participants were randomly assigned into two groups, each consisting of eight males and two females. Group one received 2 mA of anodal tDCS and group two received sham tDCS over the lDLPFC on their testing day. Results: The findings indicate that anodal tDCS significantly improves the participants' information processing capability resulting in improved performance compared to sham tDCS. For example, the multitasking throughput capacity for the sham tDCS group plateaued near 1.0 bits/s at the higher baud input (2.0 bits/s) whereas the anodal tDCS group plateaued near 1.3 bits/s. Conclusion: The findings provided new evidence that tDCS has the ability to augment and enhance multitasking capability in a human operator. Future research should be conducted to determine the longevity of the enhancement of transcranial direct current stimulation on multitasking performance, which has yet to be accomplished.
Reading About the Flu Online: How Health-Protective Behavioral Intentions Are Influenced by Media Multitasking, Polychronicity, and Strength of Health-Related Arguments.

PubMed

Kononova, Anastasia; Yuan, Shupei; Joo, Eunsin

2017-06-01

As health organizations increasingly use the Internet to communicate medical information and advice (Shortliffe et al., 2000; World Health Organization, 2013), studying factors that affect health information processing and health-protective behaviors becomes extremely important. The present research applied the elaboration likelihood model of persuasion to explore the effects of media multitasking, polychronicity (preference for multitasking), and strength of health-related arguments on health-protective behavioral intentions. Participants read an online article about influenza that included strong and weak suggestions to engage in flu-preventive behaviors. In one condition, participants read the article and checked Facebook; in another condition, they were exposed only to the article. Participants expressed greater health-protective behavioral intentions in the media multitasking condition than in the control condition. Strong arguments were found to elicit more positive behavioral intentions than weak arguments. Moderate and high polychronics showed greater behavioral intentions than low polychronics when they read the article in the multitasking condition. The difference in intentions to follow strong and weak arguments decreased for moderate and high polychronics. The results of the present study suggest that health communication practitioners should account for not only media use situations in which individuals typically read about health online but also individual differences in information processing, which puts more emphasis on the strength of health-protective suggestions when targeting light multitaskers.
Multitasking capacities in persons diagnosed with schizophrenia: a preliminary examination of their neurocognitive underpinnings and ability to predict real world functioning.

PubMed

Laloyaux, Julien; Van der Linden, Martial; Levaux, Marie-Noëlle; Mourad, Haitham; Pirri, Anthony; Bertrand, Hervé; Domken, Marc-André; Adam, Stéphane; Larøi, Frank

2014-07-30

Difficulties in everyday life activities are core features of persons diagnosed with schizophrenia and in particular during multitasking activities. However, at present, patients׳ multitasking capacities have not been adequately examined in the literature due to the absence of suitable assessment strategies. We thus recently developed a computerized real-life activity task designed to take into account the complex and multitasking nature of certain everyday life activities where participants are required to prepare a room for a meeting. Twenty-one individuals diagnosed with schizophrenia and 20 matched healthy controls completed the computerized task. Patients were also evaluated with a cognitive battery, measures of symptomatology and real world functioning. To examine the ecological validity, 14 other patients were recruited and were given the computerized version and a real version of the meeting preparation task. Results showed that performance on the computerized task was significantly correlated with executive functioning, pointing to the major implication of these cognitive processes in multitasking situations. Performance on the computerized task also significantly predicted up to 50% of real world functioning. Moreover, the computerized task demonstrated good ecological validity. These findings suggest the importance of evaluating multitasking capacities in patients diagnosed with schizophrenia in order to predict real world functioning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Motivation to comply with task rules and multitasking performance: The role of need for cognitive closure and goal importance.

PubMed

Szumowska, Ewa; Kossowska, Małgorzata; Roets, Arne

2018-01-01

In three studies, we examined the role task rules play in multitasking performance. We postulated that rules should be especially important for individuals highly motivated to have structure and clear answers, i.e., those high on need for cognitive closure (NFC). High NFC should thus be related to greater compliance with task rules. Specifically, given high goal importance, NFC should be more strongly related to a multitasking strategy when multitasking is imposed by the rules, and to a mono-tasking strategy when monotasking is imposed by the rules. This should translate into better multitasking or mono-tasking performance, depending on condition. Overall, the results were supportive as NFC was related to a more mono-tasking strategy in the mono-tasking condition (Studies 1 and 2 only) and more dual-tasking strategy in the dual-tasking condition (Studies 1-3). This translated into respective differences in performance. The effects were significant only when goal importance was high (Study 1) and held when cognitive ability was controlled for (Study 2).
3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

PubMed Central

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876
Experiences and results multitasking a hydrodynamics code on global and local memory machines

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

Mandell, D.

1987-01-01

A one-dimensional, time-dependent Lagrangian hydrodynamics code using a Godunov solution method has been multitasked for the Cray X-MP/48, the Intel iPSC hypercube, the Alliant FX series and the IBM RP3 computers. Actual multitasking results have been obtained for the Cray, Intel and Alliant computers and simulated results were obtained for the Cray and RP3 machines. The differences in the methods required to multitask on each of the machines is discussed. Results are presented for a sample problem involving a shock wave moving down a channel. Comparisons are made between theoretical speedups, predicted by Amdahl's law, and the actual speedups obtained.more » The problems of debugging on the different machines are also described.« less
Learning distance function for regression-based 4D pulmonary trunk model reconstruction estimated from sparse MRI data

NASA Astrophysics Data System (ADS)

Vitanovski, Dime; Tsymbal, Alexey; Ionasec, Razvan; Georgescu, Bogdan; Zhou, Shaohua K.; Hornegger, Joachim; Comaniciu, Dorin

2011-03-01

Congenital heart defect (CHD) is the most common birth defect and a frequent cause of death for children. Tetralogy of Fallot (ToF) is the most often occurring CHD which affects in particular the pulmonary valve and trunk. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. While minimal invasive methods become common practice, imaging and non-invasive assessment tools become crucial components in the clinical setting. Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (cMRI) are techniques with complementary properties and ability to acquire multiple non-invasive and accurate scans required for advance evaluation and therapy planning. In contrary to CT which covers the full 4D information over the cardiac cycle, cMRI often acquires partial information, for example only one 3D scan of the whole heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. The data acquired in this way is called sparse cMRI. In this paper, we propose a regression-based approach for the reconstruction of the full 4D pulmonary trunk model from sparse MRI. The reconstruction approach is based on learning a distance function between the sparse MRI which needs to be completed and the 4D CT data with the full information used as the training set. The distance is based on the intrinsic Random Forest similarity which is learnt for the corresponding regression problem of predicting coordinates of unseen mesh points. Extensive experiments performed on 80 cardiac CT and MR sequences demonstrated the average speed of 10 seconds and accuracy of 0.1053mm mean absolute error for the proposed approach. Using the case retrieval workflow and local nearest neighbour regression with the learnt distance function appears to be competitive with respect to "black box" regression with immediate prediction of coordinates, while providing transparency to the predictions made.
A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

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

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less
Full speed ahead for software

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

Wolfe, A.

1986-03-10

Supercomputing software is moving into high gear, spurred by the rapid spread of supercomputers into new applications. The critical challenge is how to develop tools that will make it easier for programmers to write applications that take advantage of vectorizing in the classical supercomputer and the parallelism that is emerging in supercomputers and minisupercomputers. Writing parallel software is a challenge that every programmer must face because parallel architectures are springing up across the range of computing. Cray is developing a host of tools for programmers. Tools to support multitasking (in supercomputer parlance, multitasking means dividing up a single program tomore » run on multiple processors) are high on Cray's agenda. On tap for multitasking is Premult, dubbed a microtasking tool. As a preprocessor for Cray's CFT77 FORTRAN compiler, Premult will provide fine-grain multitasking.« less
Differences in Multitask Resource Reallocation After Change in Task Values.

PubMed

Matton, Nadine; Paubel, Pierre; Cegarra, Julien; Raufaste, Eric

2016-12-01

The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values. When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy. We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices. When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance. In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy. During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment. © 2016, Human Factors and Ergonomics Society.
Regression-based adaptive sparse polynomial dimensional decomposition for sensitivity analysis

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro; Abgrall, Remi

2014-11-01

Polynomial dimensional decomposition (PDD) is employed in this work for global sensitivity analysis and uncertainty quantification of stochastic systems subject to a large number of random input variables. Due to the intimate structure between PDD and Analysis-of-Variance, PDD is able to provide simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to polynomial chaos (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of the standard method unaffordable for real engineering applications. In order to address this problem of curse of dimensionality, this work proposes a variance-based adaptive strategy aiming to build a cheap meta-model by sparse-PDD with PDD coefficients computed by regression. During this adaptive procedure, the model representation by PDD only contains few terms, so that the cost to resolve repeatedly the linear system of the least-square regression problem is negligible. The size of the final sparse-PDD representation is much smaller than the full PDD, since only significant terms are eventually retained. Consequently, a much less number of calls to the deterministic model is required to compute the final PDD coefficients.
Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.
The wired generation: academic and social outcomes of electronic media use among university students.

PubMed

Jacobsen, Wade C; Forste, Renata

2011-05-01

Little is known about the influence of electronic media use on the academic and social lives of university students. Using time-diary and survey data, we explore the use of various types of electronic media among first-year students. Time-diary results suggest that the majority of students use electronic media to multitask. Robust regression results indicate a negative relationship between the use of various types of electronic media and first-semester grades. In addition, we find a positive association between social-networking-site use, cellular-phone communication, and face-to-face social interaction.
The multitasking framework: the effects of increasing workload on acute psychobiological stress reactivity.

PubMed

Wetherell, Mark A; Carter, Kirsty

2014-04-01

A variety of techniques exist for eliciting acute psychological stress in the laboratory; however, they vary in terms of their ease of use, reliability to elicit consistent responses and the extent to which they represent the stressors encountered in everyday life. There is, therefore, a need to develop simple laboratory techniques that reliably elicit psychobiological stress reactivity that are representative of the types of stressors encountered in everyday life. The multitasking framework is a performance-based, cognitively demanding stressor, representative of environments where individuals are required to attend and respond to several different stimuli simultaneously with varying levels of workload. Psychological (mood and perceived workload) and physiological (heart rate and blood pressure) stress reactivity was observed in response to a 15-min period of multitasking at different levels of workload intensity in a sample of 20 healthy participants. Multitasking stress elicited increases in heart rate and blood pressure, and increased workload intensity elicited dose-response increases in levels of perceived workload and mood. As individuals rarely attend to single tasks in real life, the multitasking framework provides an alternative technique for modelling acute stress and workload in the laboratory. Copyright © 2013 John Wiley & Sons, Ltd.
A manipulative instrument with simultaneous gesture and end-effector trajectory planning and controlling

NASA Astrophysics Data System (ADS)

Lin, Hsien-I.; Nguyen, Xuan-Anh

2017-05-01

To operate a redundant manipulator to accomplish the end-effector trajectory planning and simultaneously control its gesture in online programming, incorporating the human motion is a useful and flexible option. This paper focuses on a manipulative instrument that can simultaneously control its arm gesture and end-effector trajectory via human teleoperation. The instrument can be classified by two parts; first, for the human motion capture and data processing, marker systems are proposed to capture human gesture. Second, the manipulator kinematics control is implemented by an augmented multi-tasking method, and forward and backward reaching inverse kinematics, respectively. Especially, the local-solution and divergence problems of a multi-tasking method are resolved by the proposed augmented multi-tasking method. Computer simulations and experiments with a 7-DOF (degree of freedom) redundant manipulator were used to validate the proposed method. Comparison among the single-tasking, original multi-tasking, and augmented multi-tasking algorithms were performed and the result showed that the proposed augmented method had a good end-effector position accuracy and the most similar gesture to the human gesture. Additionally, the experimental results showed that the proposed instrument was realized online.
A personal computer-based, multitasking data acquisition system

NASA Technical Reports Server (NTRS)

Bailey, Steven A.

1990-01-01

A multitasking, data acquisition system was written to simultaneously collect meteorological radar and telemetry data from two sources. This system is based on the personal computer architecture. Data is collected via two asynchronous serial ports and is deposited to disk. The system is written in both the C programming language and assembler. It consists of three parts: a multitasking kernel for data collection, a shell with pull down windows as user interface, and a graphics processor for editing data and creating coded messages. An explanation of both system principles and program structure is presented.

A multitasking general executive for compound continuous tasks.

PubMed

Salvucci, Dario D

2005-05-06

As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the particular domain but do not generalize easily to other domains. This article outlines a general executive for the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture that, given independent models of individual tasks, schedules and interleaves the models' behavior into integrated multitasking behavior. To demonstrate the power of the proposed approach, the article describes an application to the domain of driving, showing how the general executive can interleave component subtasks of the driving task (namely, control and monitoring) and interleave driving with in-vehicle secondary tasks (radio tuning and phone dialing). 2005 Lawrence Erlbaum Associates, Inc.
Training multitasking in a virtual supermarket: a novel intervention after stroke.

PubMed

Rand, Debbie; Weiss, Patrice L Tamar; Katz, Noomi

2009-01-01

To explore the potential of the VMall, a virtual supermarket running on a video-capture virtual reality system, as an intervention tool for people who have multitasking deficits after stroke. Poststroke, 4 participants received ten 60-min sessions over 3 weeks using the VMall. The intervention focused on improving multitasking while the participant was engaged in a virtual shopping task. Instruments included the Multiple Errands Test-Hospital Version (MET-HV) in a real mall and in the VMall. Participants achieved improvements ranging from 20.5% to 51.2% for most of the MET-HV measures performed in a real shopping mall and in the VMall. The data support the VMall's potential as a motivating and effective intervention tool for the rehabilitation of people poststroke who have multitasking deficits during the performance of daily tasks. However, because the sample was small, additional intervention studies with the VMall should be conducted.
One set of pliers for more tasks in installation work: the effects on (dis)comfort and productivity.

PubMed

Groenesteijn, Liesbeth; Eikhout, Sandra M; Vink, Peter

2004-09-01

In installation work, the physical workload is high. Awkward postures, heavy lifting and repetitive movements are often seen. To improve aspects of the work situation, frequently used pliers were redesigned to make them suitable for more cutting tasks. In this study these multitask pliers are evaluated in comparison to the originally used pliers in a field study and a laboratory study. For the field study 26 subjects participated divided into two groups according to their type of work. Ten subjects participated in the laboratory study. The multitask plier appeared to result in more comfort during working, more relaxed working and more satisfaction. No differences in productivity were found. In conclusion, the multitask pliers can replace the originally used pliers and are suitable for more tasks than the original pliers. The installation workers have to carry less pliers by using the multitask pliers.
Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.
Dynamic, continuous multitasking training leads to task-specific improvements but does not transfer across action selection tasks

NASA Astrophysics Data System (ADS)

Bender, Angela D.; Filmer, Hannah L.; Naughtin, Claire K.; Dux, Paul E.

2017-12-01

The ability to perform multiple tasks concurrently is an ever-increasing requirement in our information-rich world. Despite this, multitasking typically compromises performance due to the processing limitations associated with cognitive control and decision-making. While intensive dual-task training is known to improve multitasking performance, only limited evidence suggests that training-related performance benefits can transfer to untrained tasks that share overlapping processes. In the real world, however, coordinating and selecting several responses within close temporal proximity will often occur in high-interference environments. Over the last decade, there have been notable reports that training on video action games that require dynamic multitasking in a demanding environment can lead to transfer effects on aspects of cognition such as attention and working memory. Here, we asked whether continuous and dynamic multitasking training extends benefits to tasks that are theoretically related to the trained tasks. To examine this issue, we asked a group of participants to train on a combined continuous visuomotor tracking task and a perceptual discrimination task for six sessions, while an active control group practiced the component tasks in isolation. A battery of tests measuring response selection, response inhibition, and spatial attention was administered before and immediately after training to investigate transfer. Multitasking training resulted in substantial, task-specific gains in dual-task ability, but there was no evidence that these benefits generalized to other action control tasks. The findings suggest that training on a combined visuomotor tracking and discrimination task results in task-specific benefits but provides no additional value for untrained action selection tasks.
The role of Area 10 (BA10) in human multitasking and in social cognition: a lesion study.

PubMed

Roca, María; Torralva, Teresa; Gleichgerrcht, Ezequiel; Woolgar, Alexandra; Thompson, Russell; Duncan, John; Manes, Facundo

2011-11-01

A role for rostral prefrontal cortex (BA10) has been proposed in multitasking, in particular, the selection and maintenance of higher order internal goals while other sub-goals are being performed. BA10 has also been implicated in the ability to infer someone else's feelings and thoughts, often referred to as theory of mind. While most of the data to support these views come from functional neuroimaging studies, lesion studies are scant. In the present study, we compared the performance of a group of frontal patients whose lesions involved BA10, a group of frontal patients whose lesions did not affect this area (nonBA10), and a group of healthy controls on tests requiring multitasking and complex theory of mind judgments. Only the group with lesions involving BA10 showed deficits on multitasking and theory of mind tasks when compared with control subjects. NonBA10 patients performed more poorly than controls on an executive function screening tool, particularly on measures of response inhibition and abstract reasoning, suggesting that theory of mind and multitasking deficits following lesions to BA10 cannot be explained by a general worsening of executive function. In addition, we searched for correlations between performance and volume of damage within different subregions of BA10. Significant correlations were found between multitasking performance and volume of damage in right lateral BA10, and between theory of mind and total BA10 lesion volume. These findings stress the potential pivotal role of BA10 in higher order cognitive functions. Copyright Â© 2011 Elsevier Ltd. All rights reserved.
Media Multitasking and Cognitive, Psychological, Neural, and Learning Differences.

PubMed

Uncapher, Melina R; Lin, Lin; Rosen, Larry D; Kirkorian, Heather L; Baron, Naomi S; Bailey, Kira; Cantor, Joanne; Strayer, David L; Parsons, Thomas D; Wagner, Anthony D

2017-11-01

American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). This phenomenon is not limited to American youth but is paralleled across the globe. Given that a large number of media multitaskers (MMTs) are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs. It is critical to understand the relation between the relevant cognitive domains and underlying neural structure and function. Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments. The present review surveys the growing body of evidence demonstrating that heavy MMTs show differences in cognition (eg, poorer memory), psychosocial behavior (eg, increased impulsivity), and neural structure (eg, reduced volume in anterior cingulate cortex). Furthermore, research indicates that multitasking with media during learning (in class or at home) can negatively affect academic outcomes. Until the direction of causality is understood (whether media multitasking causes such behavioral and neural differences or whether individuals with such differences tend to multitask with media more often), the data suggest that engagement with concurrent media streams should be thoughtfully considered. Findings from such research promise to inform policy and practice on an increasingly urgent societal issue while significantly advancing our understanding of the intersections between cognitive, psychosocial, neural, and academic factors. Copyright © 2017 by the American Academy of Pediatrics.
Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.
Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .
Investigating the roles of touchscreen and physical control interface characteristics on driver distraction and multitasking performance.

DOT National Transportation Integrated Search

2016-01-01

This study aimed to assess the potential of driver distraction, task performance, orientation of : attention, and perceived workload in a multitasking situation involving interaction with touchscreen : interface, compared to physical interface. Autho...
Human-computer interaction in multitask situations

NASA Technical Reports Server (NTRS)

Rouse, W. B.

1977-01-01

Human-computer interaction in multitask decisionmaking situations is considered, and it is proposed that humans and computers have overlapping responsibilities. Queueing theory is employed to model this dynamic approach to the allocation of responsibility between human and computer. Results of simulation experiments are used to illustrate the effects of several system variables including number of tasks, mean time between arrivals of action-evoking events, human-computer speed mismatch, probability of computer error, probability of human error, and the level of feedback between human and computer. Current experimental efforts are discussed and the practical issues involved in designing human-computer systems for multitask situations are considered.
Multi-task feature learning by using trace norm regularization

NASA Astrophysics Data System (ADS)

Jiangmei, Zhang; Binfeng, Yu; Haibo, Ji; Wang, Kunpeng

2017-11-01

Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.
Choice in multitasking: how delays in the primary task turn a rational into an irrational multitasker.

PubMed

Katidioti, Ioanna; Taatgen, Niels A

2014-06-01

The objective was to establish the nature of choice in cognitive multitasking. Laboratory studies of multitasking suggest people are rational in their switch choices regarding multitasking, whereas observational studies suggest they are not. Threaded cognition theory predicts that switching is opportunistic and depends on availability of cognitive resources. A total of 21 participants answered e-mails by looking up information (similar to customer service employees) while being interrupted by chat messages. They were free to choose when to switch to the chat message. We analyzed the switching behavior and the time they needed to complete the primary mail task. When participants are faced with a delay in the e-mail task, they switch more often to the chat task at high-workload points. Choosing to switch to the secondary task instead of waiting makes them slower. It also makes them forget the information in the e-mail task half of the time, which slows them down even more. When many cognitive resources are available, the probability of switching from one task to another is high. This does not necessarily lead to optimal switching behavior. Potential applications of this research include the minimization of delays in task design and the inability or discouragement of switching in high-workload moments.
Comparing capacity coefficient and dual task assessment of visual multitasking workload

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

Blaha, Leslie M.

Capacity coefficient analysis could offer a theoretically grounded alternative approach to subjective measures and dual task assessment of cognitive workload. Workload capacity or workload efficiency is a human information processing modeling construct defined as the amount of information that can be processed by the visual cognitive system given a specified of amount of time. In this paper, I explore the relationship between capacity coefficient analysis of workload efficiency and dual task response time measures. To capture multitasking performance, I examine how the relatively simple assumptions underlying the capacity construct generalize beyond the single visual decision making tasks. The fundamental toolsmore » for measuring workload efficiency are the integrated hazard and reverse hazard functions of response times, which are defined by log transforms of the response time distribution. These functions are used in the capacity coefficient analysis to provide a functional assessment of the amount of work completed by the cognitive system over the entire range of response times. For the study of visual multitasking, capacity coefficient analysis enables a comparison of visual information throughput as the number of tasks increases from one to two to any number of simultaneous tasks. I illustrate the use of capacity coefficients for visual multitasking on sample data from dynamic multitasking in the modified Multi-attribute Task Battery.« less
Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.
Parametric Human Body Reconstruction Based on Sparse Key Points.

PubMed

Cheng, Ke-Li; Tong, Ruo-Feng; Tang, Min; Qian, Jing-Ye; Sarkis, Michel

2016-11-01

We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
Incorporating biological information in sparse principal component analysis with application to genomic data.

PubMed

Li, Ziyi; Safo, Sandra E; Long, Qi

2017-07-11

Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.
Multitasking Information Seeking and Searching Processes.

ERIC Educational Resources Information Center

Spink, Amanda; Ozmutlu, H. Cenk; Ozmutlu, Seda

2002-01-01

Presents findings from four studies of the prevalence of multitasking information seeking and searching by Web (via the Excite search engine), information retrieval system (mediated online database searching), and academic library users. Highlights include human information coordinating behavior (HICB); and implications for models of information…
Adaptive allocation of decisionmaking responsibility between human and computer in multitask situations

NASA Technical Reports Server (NTRS)

Chu, Y.-Y.; Rouse, W. B.

1979-01-01

As human and computer come to have overlapping decisionmaking abilities, a dynamic or adaptive allocation of responsibilities may be the best mode of human-computer interaction. It is suggested that the computer serve as a backup decisionmaker, accepting responsibility when human workload becomes excessive and relinquishing responsibility when workload becomes acceptable. A queueing theory formulation of multitask decisionmaking is used and a threshold policy for turning the computer on/off is proposed. This policy minimizes event-waiting cost subject to human workload constraints. An experiment was conducted with a balanced design of several subject runs within a computer-aided multitask flight management situation with different task demand levels. It was found that computer aiding enhanced subsystem performance as well as subjective ratings. The queueing model appears to be an adequate representation of the multitask decisionmaking situation, and to be capable of predicting system performance in terms of average waiting time and server occupancy. Server occupancy was further found to correlate highly with the subjective effort ratings.
Multitasking a three-dimensional Navier-Stokes algorithm on the Cray-2

NASA Technical Reports Server (NTRS)

Swisshelm, Julie M.

1989-01-01

A three-dimensional computational aerodynamics algorithm has been multitasked for efficient parallel execution on the Cray-2. It provides a means for examining the multitasking performance of a complete CFD application code. An embedded zonal multigrid scheme is used to solve the Reynolds-averaged Navier-Stokes equations for an internal flow model problem. The explicit nature of each component of the method allows a spatial partitioning of the computational domain to achieve a well-balanced task load for MIMD computers with vector-processing capability. Experiments have been conducted with both two- and three-dimensional multitasked cases. The best speedup attained by an individual task group was 3.54 on four processors of the Cray-2, while the entire solver yielded a speedup of 2.67 on four processors for the three-dimensional case. The multiprocessing efficiency of various types of computational tasks is examined, performance on two Cray-2s with different memory access speeds is compared, and extrapolation to larger problems is discussed.

Naturalistic Assessment of Executive Function and Everyday Multitasking in Healthy Older Adults

PubMed Central

McAlister, Courtney; Schmitter-Edgecombe, Maureen

2013-01-01

Everyday multitasking and its cognitive correlates were investigated in an older adult population using a naturalistic task, the Day Out Task. Fifty older adults and 50 younger adults prioritized, organized, initiated and completed a number of subtasks in a campus apartment to prepare for a day out (e.g., gather ingredients for a recipe, collect change for a bus ride). Participants also completed tests assessing cognitive constructs important in multitasking. Compared to younger adults, the older adults took longer to complete the everyday tasks and more poorly sequenced the subtasks. Although they initiated, completed, and interweaved a similar number of subtasks, the older adults demonstrated poorer task quality and accuracy, completing more subtasks inefficiently. For the older adults, reduced prospective memory abilities were predictive of poorer task sequencing, while executive processes and prospective memory were predictive of inefficiently completed subtasks. The findings suggest that executive dysfunction and prospective memory difficulties may contribute to the age-related decline of everyday multitasking abilities in healthy older adults. PMID:23557096
Algorithm Design of CPCI Backboard's Interrupts Management Based on VxWorks' Multi-Tasks

NASA Astrophysics Data System (ADS)

Cheng, Jingyuan; An, Qi; Yang, Junfeng

2006-09-01

This paper begins with a brief introduction of the embedded real-time operating system VxWorks and CompactPCI standard, then gives the programming interfaces of Peripheral Controller Interface (PCI) configuring, interrupts handling and multi-tasks programming interface under VxWorks, and then emphasis is placed on the software frameworks of CPCI interrupt management based on multi-tasks. This method is sound in design and easy to adapt, ensures that all possible interrupts are handled in time, which makes it suitable for data acquisition systems with multi-channels, a high data rate, and hard real-time high energy physics.
High Assurance Human-Centric Decision Systems

DTIC Science & Technology

2013-05-01

of the human operator who is multitasking in this situation. 38 Crandall, Cummings, and Mitchell [7], [8] have introduced “fan-out” models to estimate...planning in multitasking contexts. In the future, we will study extensions of our cog- nitive model. Currently, the cognitive model is focused solely
Multi-frame linear regressive filter for the measurement of infrared pixel spatial response and MTF from sparse data.

PubMed

Huard, Edouard; Derelle, Sophie; Jaeck, Julien; Nghiem, Jean; Haïdar, Riad; Primot, Jérôme

2018-03-05

A challenging point in the prediction of the image quality of infrared imaging systems is the evaluation of the detector modulation transfer function (MTF). In this paper, we present a linear method to get a 2D continuous MTF from sparse spectral data. Within the method, an object with a predictable sparse spatial spectrum is imaged by the focal plane array. The sparse data is then treated to return the 2D continuous MTF with the hypothesis that all the pixels have an identical spatial response. The linearity of the treatment is a key point to estimate directly the error bars of the resulting detector MTF. The test bench will be presented along with measurement tests on a 25 μm pitch InGaAs detector.
The effects of multitasking on psychological stress reactivity in recreational users of cannabis and MDMA.

PubMed

Wetherell, Mark A; Atherton, Katie; Grainger, Jessica; Brosnan, Robert; Scholey, Andrew B

2012-03-01

Cannabis and 3,4-methylenedioxymethamphetamine (MDMA) use is associated with psychobiological and neurocognitive deficits. Assessments of the latter typically include tests of memory and everyday cognitive functioning. However, to date, little attention has been paid to effects of drug use on psychological stress reactivity. We report three studies examining the effects of recreational use of cannabis and MDMA on mood and psychological responses to multitasking using a cognitively demanding laboratory stressor that provides an analogue for everyday situations involving responses to multiple stimuli. The effects of the multitasking framework on mood and perceived workload were assessed in cannabis (N=25), younger (N=18) and older (N=20) MDMA users and compared with non-target drug controls. Compared with respective control groups, cannabis users became less alert and content, and both MDMA groups became less calm following acute stress. Unexpectedly, the stressor increased ratings of calm in cannabis users. Users also scored higher than their controls with respect to ratings of resources needed to complete the multitasking framework. These findings show, for the first time, that recreational use of cannabis and MDMA, beyond the period of intoxication, can negatively influence psychological responses to a multitasking stressor, and this may have implications for real-life situations which place high demands on cognitive resources. Copyright © 2012 John Wiley & Sons, Ltd.
Effects of two doses of glucose and a caffeine-glucose combination on cognitive performance and mood during multi-tasking.

PubMed

Scholey, Andrew; Savage, Karen; O'Neill, Barry V; Owen, Lauren; Stough, Con; Priestley, Caroline; Wetherell, Mark

2014-09-01

This study assessed the effects of two doses of glucose and a caffeine-glucose combination on mood and performance of an ecologically valid, computerised multi-tasking platform. Following a double-blind, placebo-controlled, randomised, parallel-groups design, 150 healthy adults (mean age 34.78 years) consumed drinks containing placebo, 25 g glucose, 60 g glucose or 60 g glucose with 40 mg caffeine. They completed a multi-tasking framework at baseline and then 30 min following drink consumption with mood assessments immediately before and after the multi-tasking framework. Blood glucose and salivary caffeine were co-monitored. The caffeine-glucose group had significantly better total multi-tasking scores than the placebo or 60 g glucose groups and were significantly faster at mental arithmetic tasks than either glucose drink group. There were no significant treatment effects on mood. Caffeine and glucose levels confirmed compliance with overnight abstinence/fasting, respectively, and followed the predicted post-drink patterns. These data suggest that co-administration of glucose and caffeine allows greater allocation of attentional resources than placebo or glucose alone. At present, we cannot rule out the possibility that the effects are due to caffeine alone Future studies should aim at disentangling caffeine and glucose effects. © 2014 The Authors. Human Psychopharmacology: Clinical and Experimental published by John Wiley & Sons, Ltd.
Effects of two doses of glucose and a caffeine–glucose combination on cognitive performance and mood during multi-tasking

PubMed Central

Scholey, Andrew; Savage, Karen; O'Neill, Barry V; Owen, Lauren; Stough, Con; Priestley, Caroline; Wetherell, Mark

2014-01-01

Background This study assessed the effects of two doses of glucose and a caffeine–glucose combination on mood and performance of an ecologically valid, computerised multi-tasking platform. Materials and methods Following a double-blind, placebo-controlled, randomised, parallel-groups design, 150 healthy adults (mean age 34.78 years) consumed drinks containing placebo, 25 g glucose, 60 g glucose or 60 g glucose with 40 mg caffeine. They completed a multi-tasking framework at baseline and then 30 min following drink consumption with mood assessments immediately before and after the multi-tasking framework. Blood glucose and salivary caffeine were co-monitored. Results The caffeine–glucose group had significantly better total multi-tasking scores than the placebo or 60 g glucose groups and were significantly faster at mental arithmetic tasks than either glucose drink group. There were no significant treatment effects on mood. Caffeine and glucose levels confirmed compliance with overnight abstinence/fasting, respectively, and followed the predicted post-drink patterns. Conclusion These data suggest that co-administration of glucose and caffeine allows greater allocation of attentional resources than placebo or glucose alone. At present, we cannot rule out the possibility that the effects are due to caffeine alone Future studies should aim at disentangling caffeine and glucose effects. PMID:25196040
Should I Text or Call Here? A Situation-Based Analysis of Drivers' Perceived Likelihood of Engaging in Mobile Phone Multitasking.

PubMed

Oviedo-Trespalacios, Oscar; Haque, Md Mazharul; King, Mark; Washington, Simon

2018-05-29

This study investigated how situational characteristics typically encountered in the transport system influence drivers' perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers. © 2018 Society for Risk Analysis.
Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-02-24

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved. This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed. Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers' convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.
Taking on Multitasking

ERIC Educational Resources Information Center

Rekart, Jerome L.

2011-01-01

Multitasking impedes learning and performance in the short-term and may affect long-term memory and retention. The implications of these findings make it critical that educators and parents impress upon students the need to focus and reduce extraneous stimuli while studying or reading. Course-based quizzes and tests can be used for more than…
Examining the Effects of Distractive Multitasking with Peripheral Computing in the Classroom

ERIC Educational Resources Information Center

Puente, Jaime E.

2017-01-01

The growing use of information and communication technologies (ICTs) in college campuses has dramatically increased the potential for multitasking among students who have to juggle classes, school assignments, work, and recreational activities. These students believe that they have become more efficient by performing two or more tasks…
Connected yet Distracted: Multitasking among College Students

ERIC Educational Resources Information Center

Mokhtari, Kouider; Delello, Julie; Reichard, Carla

2015-01-01

In this study, 935 undergraduate college students from a regional four-year university responded to an online time-diary survey asking them to report their multitasking habits and practices while engaged in four main activities: reading voluntarily for fun, reading for academic purposes, watching television (TV), and using the Internet. Results…
Perceived Academic Effects of Instant Messaging Use

ERIC Educational Resources Information Center

Junco, Reynol; Cotten, Shelia R.

2011-01-01

College students use information and communication technologies at much higher levels and in different ways than prior generations. They are also more likely to multitask while using information and communication technologies. However, few studies have examined the impacts of multitasking on educational outcomes among students. This study fills a…
Making Sense of Multitasking: Key Behaviours

ERIC Educational Resources Information Center

Judd, Terry

2013-01-01

Traditionally viewed as a positive characteristic, there is mounting evidence that multitasking using digital devices can have a range of negative impacts on task performance and learning. While the cognitive processes that cause these impacts are starting to be understood and the evidence that they occur in real learning contexts is mounting, the…
Reading Performances between Novices and Experts in Different Media Multitasking Environments

ERIC Educational Resources Information Center

Lin, Lin; Robertson, Tip; Lee, Jennifer

2009-01-01

This experimental study investigated connections between subject expertise and multitasking ability among college students. One hundred thirty college students participated in the study. Participants were assessed on their subject expertise and reading tasks under three conditions: (a) reading only (silence condition), (b) reading with a video…
Genesis Pilot Human Factors Test, Human Factors Evaluation Of Driver Multitasking And Message Formats, Final Report

DOT National Transportation Integrated Search

1996-02-01

THE WORK REPORTED HERE WAS ON TWO SEPARATE TOPICS. THE FIRST OF THESE WAS THE MULTITASKING EFFECTS ON DRIVING PERFORMANCE OF USING PAGERS OR PDAS WHILE DRIVING. THE LITERATURE SHOWED THAT SUCH EFFECTS CAN OCCUR BUT THAT THEY ARE TASK SPECIFIC. FINDIN...
Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.
Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
Multitasking for flows about multiple body configurations using the chimera grid scheme

NASA Technical Reports Server (NTRS)

Dougherty, F. C.; Morgan, R. L.

1987-01-01

The multitasking of a finite-difference scheme using multiple overset meshes is described. In this chimera, or multiple overset mesh approach, a multiple body configuration is mapped using a major grid about the main component of the configuration, with minor overset meshes used to map each additional component. This type of code is well suited to multitasking. Both steady and unsteady two dimensional computations are run on parallel processors on a CRAY-X/MP 48, usually with one mesh per processor. Flow field results are compared with single processor results to demonstrate the feasibility of running multiple mesh codes on parallel processors and to show the increase in efficiency.
Research on rapid agile metrology for manufacturing based on real-time multitask operating system

NASA Astrophysics Data System (ADS)

Chen, Jihong; Song, Zhen; Yang, Daoshan; Zhou, Ji; Buckley, Shawn

1996-10-01

Rapid agile metrology for manufacturing (RAMM) using multiple non-contact sensors is likely to remain a growing trend in manufacturing. High speed inspecting systems for manufacturing is characterized by multitasks implemented in parallel and real-time events which occur simultaneously. In this paper, we introduce a real-time operating system into RAMM research. A general task model of a class-based object- oriented technology is proposed. A general multitask frame of a typical RAMM system using OPNet is discussed. Finally, an application example of a machine which inspects parts held on a carrier strip is described. With RTOS and OPNet, this machine can measure two dimensions of the contacts at 300 parts/second.

Mobile Learning: Can Students Really Multitask?

ERIC Educational Resources Information Center

Coens, Joke; Reynvoet, Bert; Clarebout, Geraldine

2011-01-01

The advent of mobile learning offers opportunities for students to do two things at once in an educational context: learning while performing another activity. The main aim of the reported studies is to address the effect of multitasking on learning with a mobile device. Two experiments were set up to examine the effect of performing a secondary…
Threaded Cognition: An Integrated Theory of Concurrent Multitasking

ERIC Educational Resources Information Center

Salvucci, Dario D.; Taatgen, Niels A.

2008-01-01

The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking--that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual…
No A 4 U: The Relationship between Multitasking and Academic Performance

ERIC Educational Resources Information Center

Junco, Reynol; Cotten, Shelia R.

2012-01-01

The proliferation and ease of access to information and communication technologies (ICTs) such as Facebook, text messaging, and instant messaging has resulted in ICT users being presented with more real-time streaming data than ever before. Unfortunately, this has also resulted in individuals increasingly engaging in multitasking as an information…
High Functioning Children with Autism Spectrum Disorder: A Novel Test of Multitasking

ERIC Educational Resources Information Center

Mackinlay, Rachael; Charman, Tony; Karmiloff-Smith, Annette

2006-01-01

High functioning children with a diagnosis of autism or Asperger's syndrome (HF-ASD) often experience difficulties organising goal-directed actions in their day-to-day lives, requiring support to schedule daily activities. This study aimed to capture these everyday difficulties experimentally using multitasking, a methodology that taps into the…
Collaboration, Multi-Tasking and Problem Solving Performance in Shared Virtual Spaces

ERIC Educational Resources Information Center

Lin, Lin; Mills, Leila A.; Ifenthaler, Dirk

2016-01-01

Collaborative problem-solving is often not a sequential process; instead, it can involve tasking switching or dual tasking (i.e., multitasking) activities in that the collaborators need to shift their attention between the targeted problems and the conversations they carry on with their collaborators. It is not known to what extent the…
Adaptive Allocation of Decision Making Responsibility Between Human and Computer in Multi-Task Situations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chu, Y. Y.

1978-01-01

A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented.
The Problem State: A Cognitive Bottleneck in Multitasking

ERIC Educational Resources Information Center

Borst, Jelmer P.; Taatgen, Niels A.; van Rijn, Hedderik

2010-01-01

The main challenge for theories of multitasking is to predict when and how tasks interfere. Here, we focus on interference related to the problem state, a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgen's (2008) threaded cognition theory, we predict interference if 2 or more…
Bilingualism as a Model for Multitasking

PubMed Central

Poarch, Gregory J.; Bialystok, Ellen

2015-01-01

Because both languages of bilinguals are constantly active, bilinguals need to manage attention to the target language and avoid interference from the non-target language. This process is likely carried out by recruiting the executive function (EF) system, a system that is also the basis for multitasking. In previous research, bilinguals have been shown to outperform monolinguals on tasks requiring EF, suggesting that the practice using EF for language management benefits performance in other tasks as well. The present study examined 203 children, 8-11 years old, who were monolingual, partially bilingual, bilingual, or trilingual performing a flanker task. Two results support the interpretation that bilingualism is related to multitasking. First, bilingual children outperformed monolinguals on the conflict trials in the flanker task, confirming previous results for a bilingual advantage in EF. Second, the inclusion of partial bilinguals and trilinguals set limits on the role of experience: partial bilingual performed similarly to monolinguals and trilinguals performed similarly to bilinguals, suggesting that degrees of experience are not well-calibrated to improvements in EF. Our conclusion is that the involvement of EF in bilingual language processing makes bilingualism a form of linguistic multitasking. PMID:25821336
The developing brain in a multitasking world

PubMed Central

Rothbart, Mary K.; Posner, Michael I.

2015-01-01

To understand the problem of multitasking, it is necessary to examine the brain’s attention networks that underlie the ability to switch attention between stimuli and tasks and to maintain a single focus among distractors. In this paper we discuss the development of brain networks related to the functions of achieving the alert state, orienting to sensory events, and developing self-control. These brain networks are common to everyone, but their efficiency varies among individuals and reflects both genes and experience. Training can alter brain networks. We consider two forms of training: (1) practice in tasks that involve particular networks, and (2) changes in brain state through such practices as meditation that may influence many networks. Playing action video games and multitasking are themselves methods of training the brain that can lead to improved performance but also to overdependence on media activity. We consider both of these outcomes and ideas about how to resist overdependence on media. Overall, our paper seeks to inform the reader about what has been learned about attention that can influence multitasking over the course of development. PMID:25821335
Multitasking During Degraded Speech Recognition in School-Age Children

PubMed Central

Ward, Kristina M.; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children’s multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children’s accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children’s dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children’s proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition. PMID:28105890
Multitasking operating systems for microprocessors

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

Cramer, T.

1981-01-01

Microprocessors, because of their low cost, low power consumption, and small size, have caused an explosion in the number of innovative computer applications. Although there is a great deal of variation in microprocessor applications software, there is relatively little variation in the operating-system-level software from one application to the next. Nonetheless, operating system software, especially when multitasking is involved, can be very time consuming and expensive to develop. The major microprocessor manufacturers have acknowledged the need for operating systems in microprocessor applications and are now supplying real-time multitasking operating system software that is adaptable to a wide variety of usermore » systems. Use of this existing operating system software will decrease the number of redundant operating system development efforts, thus freeing programmers to work on more creative and productive problems. This paper discusses the basic terminology and concepts involved with multitasking operating systems. It is intended to provide a general understanding of the subject, so that the reader will be prepared to evaluate specific operating system software according to his or her needs. 2 references.« less
Automation trust and attention allocation in multitasking workspace.

PubMed

Karpinsky, Nicole D; Chancey, Eric T; Palmer, Dakota B; Yamani, Yusuke

2018-07-01

Previous research suggests that operators with high workload can distrust and then poorly monitor automation, which has been generally inferred from automation dependence behaviors. To test automation monitoring more directly, the current study measured operators' visual attention allocation, workload, and trust toward imperfect automation in a dynamic multitasking environment. Participants concurrently performed a manual tracking task with two levels of difficulty and a system monitoring task assisted by an unreliable signaling system. Eye movement data indicate that operators allocate less visual attention to monitor automation when the tracking task is more difficult. Participants reported reduced levels of trust toward the signaling system when the tracking task demanded more focused visual attention. Analyses revealed that trust mediated the relationship between the load of the tracking task and attention allocation in Experiment 1, an effect that was not replicated in Experiment 2. Results imply a complex process underlying task load, visual attention allocation, and automation trust during multitasking. Automation designers should consider operators' task load in multitasking workspaces to avoid reduced automation monitoring and distrust toward imperfect signaling systems. Copyright © 2018. Published by Elsevier Ltd.
Multitasking During Degraded Speech Recognition in School-Age Children.

PubMed

Grieco-Calub, Tina M; Ward, Kristina M; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children's multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children's accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children's dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children's proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition.
The developing brain in a multitasking world.

PubMed

Rothbart, Mary K; Posner, Michael I

2015-03-01

To understand the problem of multitasking, it is necessary to examine the brain's attention networks that underlie the ability to switch attention between stimuli and tasks and to maintain a single focus among distractors. In this paper we discuss the development of brain networks related to the functions of achieving the alert state, orienting to sensory events, and developing self-control. These brain networks are common to everyone, but their efficiency varies among individuals and reflects both genes and experience. Training can alter brain networks. We consider two forms of training: (1) practice in tasks that involve particular networks, and (2) changes in brain state through such practices as meditation that may influence many networks. Playing action video games and multitasking are themselves methods of training the brain that can lead to improved performance but also to overdependence on media activity. We consider both of these outcomes and ideas about how to resist overdependence on media. Overall, our paper seeks to inform the reader about what has been learned about attention that can influence multitasking over the course of development.
Bilingualism as a Model for Multitasking.

PubMed

Poarch, Gregory J; Bialystok, Ellen

2015-03-01

Because both languages of bilinguals are constantly active, bilinguals need to manage attention to the target language and avoid interference from the non-target language. This process is likely carried out by recruiting the executive function (EF) system, a system that is also the basis for multitasking. In previous research, bilinguals have been shown to outperform monolinguals on tasks requiring EF, suggesting that the practice using EF for language management benefits performance in other tasks as well. The present study examined 203 children, 8-11 years old, who were monolingual, partially bilingual, bilingual, or trilingual performing a flanker task. Two results support the interpretation that bilingualism is related to multitasking. First, bilingual children outperformed monolinguals on the conflict trials in the flanker task, confirming previous results for a bilingual advantage in EF. Second, the inclusion of partial bilinguals and trilinguals set limits on the role of experience: partial bilingual performed similarly to monolinguals and trilinguals performed similarly to bilinguals, suggesting that degrees of experience are not well-calibrated to improvements in EF. Our conclusion is that the involvement of EF in bilingual language processing makes bilingualism a form of linguistic multitasking.
"Surgery interrupted": The effect of multitasking on cognitive and technical tasks in medical students.

PubMed

Evans, C H; Schneider, E; Shostrom, V; Schenarts, P J

2017-02-01

Today's medical learners are Millennials, and reportedly, multitasking pros. We aim to evaluate effect of multitasking on cognitive and technical skills. 16 medical students completed a mock page and laceration closure separately on day 1 and day 13, and in parallel on day 14. Suturing was graded using GRS and mock pages scored. Total time, suturing and loading times, and percent correct on mock page were compared. Percent correct on mock page improved from days 1-13 and 14 (p < 0.01 and 0.04). GRS improved from days 1-13 and 14 (p = 0.04 and <0.01). Total time suturing was similar on all days. However, time suturing during the mock page on day 14 was prolonged compared to before mock page (p = 0.01). Medical students can complete cognitive and technical tasks in parallel, without compromising acceptability. However, multitasking results in longer times to complete the complex component of the technical task. Copyright © 2016 Elsevier Inc. All rights reserved.
Multitasking information behavior, information task switching and anxiety: An exploratory study

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

Alexopoulou, Peggy, E-mail: p.alexopoulou@lboro.ac.uk, E-mail: an-kotsopoulou@yahoo.com; Kotsopoulou, Anastasia, E-mail: p.alexopoulou@lboro.ac.uk, E-mail: an-kotsopoulou@yahoo.com

Multitasking information behavior involves multiple forms of information searching such as library and Web search. Few researchers, however, have explored multitasking information behavior and information task switching in libraries in conjunction with psychological variables. This study explored this behavior in terms of anxiety under time pressure. This was an exploratory case study. Participant searched information for three unrelated everyday life information topics during a library visit, in a timeframe of one hour. The data collection tools used were: diary, observation, interview, and the State-Trait Anxiety Inventory test. Participant took the Trait-anxiety test before the library visit to measure anxiety levelmore » as a personal characteristic. She also took State-anxiety test before, during and after the library visit to measure anxiety levels regarding the information seeking behavior. The results suggested that participant had high levels of anxiety at the beginning of the multitasking information behavior. The reason for that was the concern about the performance as well as the identification of the right resources. During the multitasking information behavior, participant still had anxiety to find the right information. The levels of anxiety, however, were less due to library’s good organized structure. At the end of the information seeking process, the levels of anxiety dropped significant and therefore calm and safety returned. Finally, participant searched information for topics that were more important and for which she had prior knowledge When people, under time pressure, have access to well organized information, the levels of anxiety might decrease.« less
Multitasking information behavior, information task switching and anxiety: An exploratory study

NASA Astrophysics Data System (ADS)

Alexopoulou, Peggy; Kotsopoulou, Anastasia

2015-02-01

Multitasking information behavior involves multiple forms of information searching such as library and Web search. Few researchers, however, have explored multitasking information behavior and information task switching in libraries in conjunction with psychological variables. This study explored this behavior in terms of anxiety under time pressure. This was an exploratory case study. Participant searched information for three unrelated everyday life information topics during a library visit, in a timeframe of one hour. The data collection tools used were: diary, observation, interview, and the State-Trait Anxiety Inventory test. Participant took the Trait-anxiety test before the library visit to measure anxiety level as a personal characteristic. She also took State-anxiety test before, during and after the library visit to measure anxiety levels regarding the information seeking behavior. The results suggested that participant had high levels of anxiety at the beginning of the multitasking information behavior. The reason for that was the concern about the performance as well as the identification of the right resources. During the multitasking information behavior, participant still had anxiety to find the right information. The levels of anxiety, however, were less due to library's good organized structure. At the end of the information seeking process, the levels of anxiety dropped significant and therefore calm and safety returned. Finally, participant searched information for topics that were more important and for which she had prior knowledge When people, under time pressure, have access to well organized information, the levels of anxiety might decrease.
Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.
Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

PubMed Central

2010-01-01

Background Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. Results An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. Conclusions The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism. PMID:20380733

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study.

PubMed

Liu, Qi; Xu, Qian; Zheng, Vincent W; Xue, Hong; Cao, Zhiwei; Yang, Qiang

2010-04-10

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.
Reducing Misses and Near Misses Related to Multitasking on the Electronic Health Record: Observational Study and Qualitative Analysis

PubMed Central

Matta, George Y; Bohsali, Fuad B; Chisolm, Margaret S

2018-01-01

Background Clinicians’ use of electronic health record (EHR) systems while multitasking may increase the risk of making errors, but silent EHR system use may lower patient satisfaction. Delaying EHR system use until after patient visits may increase clinicians’ EHR workload, stress, and burnout. Objective We aimed to describe the perspectives of clinicians, educators, administrators, and researchers about misses and near misses that they felt were related to clinician multitasking while using EHR systems. Methods This observational study was a thematic analysis of perspectives elicited from 63 continuing medical education (CME) participants during 2 workshops and 1 interactive lecture about challenges and strategies for relationship-centered communication during clinician EHR system use. The workshop elicited reflection about memorable times when multitasking EHR use was associated with “misses” (errors that were not caught at the time) or “near misses” (mistakes that were caught before leading to errors). We conducted qualitative analysis using an editing analysis style to identify codes and then select representative themes and quotes. Results All workshop participants shared stories of misses or near misses in EHR system ordering and documentation or patient-clinician communication, wondering about “misses we don’t even know about.” Risk factors included the computer’s position, EHR system usability, note content and style, information overload, problematic workflows, systems issues, and provider and patient communication behaviors and expectations. Strategies to reduce multitasking EHR system misses included clinician transparency when needing silent EHR system use (eg, for prescribing), narrating EHR system use, patient activation during EHR system use, adapting visit organization and workflow, improving EHR system design, and improving team support and systems. Conclusions CME participants shared numerous stories of errors and near misses in EHR tasks and communication that they felt related to EHR multitasking. However, they brainstormed diverse strategies for using EHR systems safely while preserving patient relationships. PMID:29410388
Measuring the relationship between interruptions, multitasking and prescribing errors in an emergency department: a study protocol

PubMed Central

Raban, Magdalena Z; Walter, Scott R; Douglas, Heather E; Strumpman, Dana; Mackenzie, John; Westbrook, Johanna I

2015-01-01

Introduction Interruptions and multitasking are frequent in clinical settings, and have been shown in the cognitive psychology literature to affect performance, increasing the risk of error. However, comparatively less is known about their impact on errors in clinical work. This study will assess the relationship between prescribing errors, interruptions and multitasking in an emergency department (ED) using direct observations and chart review. Methods and analysis The study will be conducted in an ED of a 440-bed teaching hospital in Sydney, Australia. Doctors will be shadowed at proximity by observers for 2 h time intervals while they are working on day shift (between 0800 and 1800). Time stamped data on tasks, interruptions and multitasking will be recorded on a handheld computer using the validated Work Observation Method by Activity Timing (WOMBAT) tool. The prompts leading to interruptions and multitasking will also be recorded. When doctors prescribe medication, type of chart and chart sections written on, along with the patient's medical record number (MRN) will be recorded. A clinical pharmacist will access patient records and assess the medication orders for prescribing errors. The prescribing error rate will be calculated per prescribing task and is defined as the number of errors divided by the number of medication orders written during the prescribing task. The association between prescribing error rates, and rates of prompts, interruptions and multitasking will be assessed using statistical modelling. Ethics and dissemination Ethics approval has been obtained from the hospital research ethics committee. Eligible doctors will be provided with written information sheets and written consent will be obtained if they agree to participate. Doctor details and MRNs will be kept separate from the data on prescribing errors, and will not appear in the final data set for analysis. Study results will be disseminated in publications and feedback to the ED. PMID:26463224
A technical review of flexible endoscopic multitasking platforms.

PubMed

Yeung, Baldwin Po Man; Gourlay, Terence

2012-01-01

Further development of advanced therapeutic endoscopic techniques and natural orifice translumenal endoscopic surgery (NOTES) requires a powerful flexible endoscopic multitasking platform. Medline search was performed to identify literature relating to flexible endoscopic multitasking platform from year 2004-2011 using keywords: Flexible endoscopic multitasking platform, NOTES, Instrumentation, Endoscopic robotic surgery, and specific names of various endoscopic multitasking platforms. Key articles from articles references were reviewed. Flexible multitasking platforms can be classified as either mechanical or robotic. Purely mechanical systems include the dual channel endoscope (DCE) (Olympus), R-Scope (Olympus), the EndoSamurai (Olympus), the ANUBIScope (Karl-Storz), Incisionless Operating Platform (IOP) (USGI), and DDES system (Boston Scientific). Robotic systems include the MASTER system (Nanyang University, Singapore) and the Viacath (Hansen Medical). The DCE, the R-Scope, the EndoSamurai and the ANUBIScope have integrated visual function and instrument manipulation function. The IOP and DDES systems rely on the conventional flexible endoscope for visualization, and instrument manipulation is integrated through the use of a flexible, often lockable, multichannel access device. The advantage of the access device concept is that it allows optics and instrument dissociation. Due to the anatomical constrains of the pharynx, systems are designed to have a diameter of less than 20 mm. All systems are controlled by traction cable system actuated either by hand or by robotic machinery. In a flexible system, this method of actuation inevitably leads to significant hysteresis. This problem will be accentuated with a long endoscope such as that required in performing colonic procedures. Systems often require multiple operators. To date, the DCE, the R-Scope, the IOP, and the Viacath system have data published relating to their application in human. Alternative forms of instrument actuation, camera control and master console ergonomics should be explored to improve instrument precision, sphere of action, size and minimize assistance required. Copyright © 2012 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.
Measuring the relationship between interruptions, multitasking and prescribing errors in an emergency department: a study protocol.

PubMed

Raban, Magdalena Z; Walter, Scott R; Douglas, Heather E; Strumpman, Dana; Mackenzie, John; Westbrook, Johanna I

2015-10-13

Interruptions and multitasking are frequent in clinical settings, and have been shown in the cognitive psychology literature to affect performance, increasing the risk of error. However, comparatively less is known about their impact on errors in clinical work. This study will assess the relationship between prescribing errors, interruptions and multitasking in an emergency department (ED) using direct observations and chart review. The study will be conducted in an ED of a 440-bed teaching hospital in Sydney, Australia. Doctors will be shadowed at proximity by observers for 2 h time intervals while they are working on day shift (between 0800 and 1800). Time stamped data on tasks, interruptions and multitasking will be recorded on a handheld computer using the validated Work Observation Method by Activity Timing (WOMBAT) tool. The prompts leading to interruptions and multitasking will also be recorded. When doctors prescribe medication, type of chart and chart sections written on, along with the patient's medical record number (MRN) will be recorded. A clinical pharmacist will access patient records and assess the medication orders for prescribing errors. The prescribing error rate will be calculated per prescribing task and is defined as the number of errors divided by the number of medication orders written during the prescribing task. The association between prescribing error rates, and rates of prompts, interruptions and multitasking will be assessed using statistical modelling. Ethics approval has been obtained from the hospital research ethics committee. Eligible doctors will be provided with written information sheets and written consent will be obtained if they agree to participate. Doctor details and MRNs will be kept separate from the data on prescribing errors, and will not appear in the final data set for analysis. Study results will be disseminated in publications and feedback to the ED. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Reducing Misses and Near Misses Related to Multitasking on the Electronic Health Record: Observational Study and Qualitative Analysis.

PubMed

Ratanawongsa, Neda; Matta, George Y; Bohsali, Fuad B; Chisolm, Margaret S

2018-02-06

Clinicians' use of electronic health record (EHR) systems while multitasking may increase the risk of making errors, but silent EHR system use may lower patient satisfaction. Delaying EHR system use until after patient visits may increase clinicians' EHR workload, stress, and burnout. We aimed to describe the perspectives of clinicians, educators, administrators, and researchers about misses and near misses that they felt were related to clinician multitasking while using EHR systems. This observational study was a thematic analysis of perspectives elicited from 63 continuing medical education (CME) participants during 2 workshops and 1 interactive lecture about challenges and strategies for relationship-centered communication during clinician EHR system use. The workshop elicited reflection about memorable times when multitasking EHR use was associated with "misses" (errors that were not caught at the time) or "near misses" (mistakes that were caught before leading to errors). We conducted qualitative analysis using an editing analysis style to identify codes and then select representative themes and quotes. All workshop participants shared stories of misses or near misses in EHR system ordering and documentation or patient-clinician communication, wondering about "misses we don't even know about." Risk factors included the computer's position, EHR system usability, note content and style, information overload, problematic workflows, systems issues, and provider and patient communication behaviors and expectations. Strategies to reduce multitasking EHR system misses included clinician transparency when needing silent EHR system use (eg, for prescribing), narrating EHR system use, patient activation during EHR system use, adapting visit organization and workflow, improving EHR system design, and improving team support and systems. CME participants shared numerous stories of errors and near misses in EHR tasks and communication that they felt related to EHR multitasking. However, they brainstormed diverse strategies for using EHR systems safely while preserving patient relationships. ©Neda Ratanawongsa, George Y Matta, Fuad B Bohsali, Margaret S Chisolm. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 06.02.2018.
Work conditions, mental workload and patient care quality: a multisource study in the emergency department.

PubMed

Weigl, Matthias; Müller, Andreas; Holland, Stephan; Wedel, Susanne; Woloshynowych, Maria

2016-07-01

Workflow interruptions, multitasking and workload demands are inherent to emergency departments (ED) work systems. Potential effects of ED providers' work on care quality and patient safety have, however, been rarely addressed. We aimed to investigate the prevalence and associations of ED staff's workflow interruptions, multitasking and workload with patient care quality outcomes. We applied a mixed-methods design in a two-step procedure. First, we conducted a time-motion study to observe the rate of interruptions and multitasking activities. Second, during 20-day shifts we assessed ED staff's reports on workflow interruptions, multitasking activities and mental workload. Additionally, we assessed two care quality indicators with standardised questionnaires: first, ED patients' evaluations of perceived care quality; second, patient intrahospital transfers evaluated by ward staff. The study was conducted in a medium-sized community ED (16 600 annual visits). ED personnel's workflow was disrupted on average 5.63 times per hour. 30% of time was spent on multitasking activities. During 20 observations days, data were gathered from 76 ED professionals, 239 patients and 205 patient transfers. After aggregating daywise data and controlling for staffing levels, prospective associations revealed significant negative associations between ED personnel's mental workload and patients' perceived quality of care. Conversely, workflow interruptions were positively associated with patient-related information on discharge and overall quality of transfer. Our investigation indicated that ED staff's capability to cope with demanding work conditions was associated with patient care quality. Our findings contribute to an improved understanding of the complex effects of interruptions and multitasking in the ED environment for creating safe and efficient ED work and care systems. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

PubMed Central

Martinez, Josue G.; Carroll, Raymond J.; Müller, Samuel; Sampson, Joshua N.; Chatterjee, Nilanjan

2012-01-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso. PMID:22347720
Locomotor Adaptation Improves Balance Control, Multitasking Ability and Reduces the Metabolic Cost of Postural Instability

NASA Technical Reports Server (NTRS)

Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Brady, R. A.; Batson, C. D.; Miller, C. A.; Ploutz-Snyder, R. J.; Guined, J. R.; Buxton, R. E.; Cohen, H. S.

2011-01-01

During exploration-class missions, sensorimotor disturbances may lead to disruption in the ability to ambulate and perform functional tasks during the initial introduction to a novel gravitational environment following a landing on a planetary surface. The overall goal of our current project is to develop a sensorimotor adaptability training program to facilitate rapid adaptation to these environments. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene. It provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. Greater metabolic cost incurred during balance instability means more physical work is required during adaptation to new environments possibly affecting crewmembers? ability to perform mission critical tasks during early surface operations on planetary expeditions. The goal of this study was to characterize adaptation to a discordant sensory challenge across a number of performance modalities including locomotor stability, multi-tasking ability and metabolic cost. METHODS: Subjects (n=15) walked (4.0 km/h) on a treadmill for an 8 -minute baseline walking period followed by 20-minutes of walking (4.0 km/h) with support surface motion (0.3 Hz, sinusoidal lateral motion, peak amplitude 25.4 cm) provided by the treadmill/motion-base system. Stride frequency and auditory reaction time were collected as measures of locomotor stability and multi-tasking ability, respectively. Metabolic data (VO2) were collected via a portable metabolic gas analysis system. RESULTS: At the onset of lateral support surface motion, subj ects walking on our treadmill showed an increase in stride frequency and auditory reaction time indicating initial balance and multi-tasking disturbances. During the 20-minute adaptation period, balance control and multi-tasking performance improved. Similarly, throughout the 20-minute adaptation period, VO2 gradually decreased following an initial increase after the onset of support surface motion. DISCUSSION: Resu lts confirmed that walking in discordant conditions not only compromises locomotor stability and the ability to multi-task, but comes at a quantifiable metabolic cost. Importantly, like locomotor stability and multi-tasking ability, metabolic expenditure while walking in discordant sensory conditions improved during adaptation. This confirms that sensorimotor adaptability training can benefit multiple performance parameters central to the successful completion of critical mission tasks.
Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Goring, Simon

2017-01-01

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.
Effects of cacheing on multitasking efficiency and programming strategy on an ELXSI 6400

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

Montry, G.R.; Benner, R.E.

1985-12-01

The impact of a cache/shared memory architecture, and, in particular, the cache coherency problem, upon concurrent algorithm and program development is discussed. In this context, a simple set of programming strategies are proposed which streamline code development and improve code performance when multitasking in a cache/shared memory or distributed memory environment.
New Research on Multitasking Points to Role of Self-Control

ERIC Educational Resources Information Center

Sparks, Sarah D.

2012-01-01

For a generation of children immersed in technology, emerging research suggests that while the temptation to multitask may be pervasive, the ability to control it could be the real bellwether of academic success. The pervasiveness of technology and social media, coupled with a fear of missing out on something important, has led students to pay…
Multi-task learning with group information for human action recognition

NASA Astrophysics Data System (ADS)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.
Programmable DNA-Mediated Multitasking Processor.

PubMed

Shu, Jian-Jun; Wang, Qi-Wen; Yong, Kian-Yan; Shao, Fangwei; Lee, Kee Jin

2015-04-30

Because of DNA appealing features as perfect material, including minuscule size, defined structural repeat and rigidity, programmable DNA-mediated processing is a promising computing paradigm, which employs DNAs as information storing and processing substrates to tackle the computational problems. The massive parallelism of DNA hybridization exhibits transcendent potential to improve multitasking capabilities and yield a tremendous speed-up over the conventional electronic processors with stepwise signal cascade. As an example of multitasking capability, we present an in vitro programmable DNA-mediated optimal route planning processor as a functional unit embedded in contemporary navigation systems. The novel programmable DNA-mediated processor has several advantages over the existing silicon-mediated methods, such as conducting massive data storage and simultaneous processing via much fewer materials than conventional silicon devices.
Multitask visual learning using genetic programming.

PubMed

Jaśkowski, Wojciech; Krawiec, Krzysztof; Wieloch, Bartosz

2008-01-01

We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.
Mapping soil textural fractions across a large watershed in north-east Florida.

PubMed

Lamsal, S; Mishra, U

2010-08-01

Assessment of regional scale soil spatial variation and mapping their distribution is constrained by sparse data which are collected using field surveys that are labor intensive and cost prohibitive. We explored geostatistical (ordinary kriging-OK), regression (Regression Tree-RT), and hybrid methods (RT plus residual Sequential Gaussian Simulation-SGS) to map soil textural fractions across the Santa Fe River Watershed (3585 km(2)) in north-east Florida. Soil samples collected from four depths (L1: 0-30 cm, L2: 30-60 cm, L3: 60-120 cm, and L4: 120-180 cm) at 141 locations were analyzed for soil textural fractions (sand, silt and clay contents), and combined with textural data (15 profiles) assembled under the Florida Soil Characterization program. Textural fractions in L1 and L2 were autocorrelated, and spatially mapped across the watershed. OK performance was poor, which may be attributed to the sparse sampling. RT model structure varied among textural fractions, and the model explained variations ranged from 25% for L1 silt to 61% for L2 clay content. Regression residuals were simulated using SGS, and the average of simulated residuals were used to approximate regression residual distribution map, which were added to regression trend maps. Independent validation of the prediction maps showed that regression models performed slightly better than OK, and regression combined with average of simulated regression residuals improved predictions beyond the regression model. Sand content >90% in both 0-30 and 30-60 cm covered 80.6% of the watershed area. Copyright 2010 Elsevier Ltd. All rights reserved.
Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.
Further Development of the Assessment of Military Multitasking Performance: Iterative Reliability Testing

PubMed Central

McCulloch, Karen L.; Radomski, Mary V.; Finkelstein, Marsha; Cecchini, Amy S.; Davidson, Leslie F.; Heaton, Kristin J.; Smith, Laurel B.; Scherer, Matthew R.

2017-01-01

The Assessment of Military Multitasking Performance (AMMP) is a battery of functional dual-tasks and multitasks based on military activities that target known sensorimotor, cognitive, and exertional vulnerabilities after concussion/mild traumatic brain injury (mTBI). The AMMP was developed to help address known limitations in post concussive return to duty assessment and decision making. Once validated, the AMMP is intended for use in combination with other metrics to inform duty-readiness decisions in Active Duty Service Members following concussion. This study used an iterative process of repeated interrater reliability testing and feasibility feedback to drive modifications to the 9 tasks of the original AMMP which resulted in a final version of 6 tasks with metrics that demonstrated clinically acceptable ICCs of > 0.92 (range of 0.92–1.0) for the 3 dual tasks and > 0.87 (range 0.87–1.0) for the metrics of the 3 multitasks. Three metrics involved in recording subject errors across 2 tasks did not achieve ICCs above 0.85 set apriori for multitasks (0.64) and above 0.90 set for dual-tasks (0.77 and 0.86) and were not used for further analysis. This iterative process involved 3 phases of testing with between 13 and 26 subjects, ages 18–42 years, tested in each phase from a combined cohort of healthy controls and Service Members with mTBI. Study findings support continued validation of this assessment tool to provide rehabilitation clinicians further return to duty assessment methods robust to ceiling effects with strong face validity to injured Warriors and their leaders. PMID:28056045
Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

PubMed

Belilovsky, Eugene; Gkirtzou, Katerina; Misyrlis, Michail; Konova, Anna B; Honorio, Jean; Alia-Klein, Nelly; Goldstein, Rita Z; Samaras, Dimitris; Blaschko, Matthew B

2015-12-01

We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects. We show that in many cases, use of the k-support norm leads to better predictive performance, solution stability, and interpretability as compared to other standard approaches. We additionally analyze the advantages of using the absolute loss function versus the standard squared loss which leads to significantly better predictive performance for the regularization methods tested in almost all cases. Our results support the use of the k-support norm for fMRI analysis and on the clinical side, the generalizability of the I-RISA model of cocaine addiction. Copyright © 2015 Elsevier Ltd. All rights reserved.
Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.
Multitask Agents and Incentives: The Case of Teaching and Research for University Professors. CEP Discussion Paper No. 1386

ERIC Educational Resources Information Center

De Philippis, Marta

2015-01-01

This paper evaluates the behavioural responses of multitask agents to the provision of incentives skewed towards one task only. In particular it studies the case of strong research incentives for university professors and it analyzes their effects on the way university faculty members allocate effort between teaching and quantity and quality of…
Parallel processing on the Livermore VAX 11/780-4 parallel processor system with compatibility to Cray Research, Inc. (CRI) multitasking. Version 1

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

Werner, N.E.; Van Matre, S.W.

1985-05-01

This manual describes the CRI Subroutine Library and Utility Package. The CRI library provides Cray multitasking functionality on the four-processor shared memory VAX 11/780-4. Additional functionality has been added for more flexibility. A discussion of the library, utilities, error messages, and example programs is provided.
Implementing dense linear algebra algorithms using multitasking on the CRAY X-MP-4 (or approaching the gigaflop)

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

Dongarra, J.J.; Hewitt, T.

1985-08-01

This note describes some experiments on simple, dense linear algebra algorithms. These experiments show that the CRAY X-MP is capable of small-grain multitasking arising from standard implementations of LU and Cholesky decomposition. The implementation described here provides the ''fastest'' execution rate for LU decomposition, 718 MFLOPS for a matrix of order 1000.
Synthetic Synchronisation: From Attention and Multi-Tasking to Negative Capability and Judgment

ERIC Educational Resources Information Center

Stables, Andrew

2013-01-01

Educational literature has tended to focus, explicitly and implicitly, on two kinds of task orientation: the ability either to focus on a single task, or to multi-task. A third form of orientation characterises many highly successful people. This is the ability to combine several tasks into one: to "kill two (or more) birds with one…
An Introduction to Multitasking and Texting: Prevalence and Impact on Grades and GPA in Marketing Classes

ERIC Educational Resources Information Center

Clayson, Dennis E.; Haley, Debra A.

2013-01-01

This exploratory study looks at the phenomena of texting in a marketing education context. It outlines the difficulties of multitasking within two metacognitive models of learning and sets the stage for further research on the effects of texting within class. Students in marketing classes in two different universities were surveyed. They received…
Decision Making in Concurrent Multitasking: Do People Adapt to Task Interference?

PubMed Central

Nijboer, Menno; Taatgen, Niels A.; Brands, Annelies; Borst, Jelmer P.; van Rijn, Hedderik

2013-01-01

While multitasking has received a great deal of attention from researchers, we still know little about how well people adapt their behavior to multitasking demands. In three experiments, participants were presented with a multicolumn subtraction task, which required working memory in half of the trials. This primary task had to be combined with a secondary task requiring either working memory or visual attention, resulting in different types of interference. Before each trial, participants were asked to choose which secondary task they wanted to perform concurrently with the primary task. We predicted that if people seek to maximize performance or minimize effort required to perform the dual task, they choose task combinations that minimize interference. While performance data showed that the predicted optimal task combinations indeed resulted in minimal interference between tasks, the preferential choice data showed that a third of participants did not show any adaptation, and for the remainder it took a considerable number of trials before the optimal task combinations were chosen consistently. On the basis of these results we argue that, while in principle people are able to adapt their behavior according to multitasking demands, selection of the most efficient combination of strategies is not an automatic process. PMID:24244527
Negativity Bias in Media Multitasking: The Effects of Negative Social Media Messages on Attention to Television News Broadcasts.

PubMed

Kätsyri, Jari; Kinnunen, Teemu; Kusumoto, Kenta; Oittinen, Pirkko; Ravaja, Niklas

2016-01-01

Television viewers' attention is increasingly more often divided between television and "second screens", for example when viewing television broadcasts and following their related social media discussion on a tablet computer. The attentional costs of such multitasking may vary depending on the ebb and flow of the social media channel, such as its emotional contents. In the present study, we tested the hypothesis that negative social media messages would draw more attention than similar positive messages. Specifically, news broadcasts were presented in isolation and with simultaneous positive or negative Twitter messages on a tablet to 38 participants in a controlled experiment. Recognition memory, gaze tracking, cardiac responses, and self-reports were used as attentional indices. The presence of any tweets on the tablet decreased attention to the news broadcasts. As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets. Negative tweets did not, however, decrease attention to the news broadcasts. Taken together, the present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects of media multitasking.
Negativity Bias in Media Multitasking: The Effects of Negative Social Media Messages on Attention to Television News Broadcasts

PubMed Central

Kätsyri, Jari; Kinnunen, Teemu; Kusumoto, Kenta; Oittinen, Pirkko; Ravaja, Niklas

2016-01-01

Television viewers’ attention is increasingly more often divided between television and “second screens”, for example when viewing television broadcasts and following their related social media discussion on a tablet computer. The attentional costs of such multitasking may vary depending on the ebb and flow of the social media channel, such as its emotional contents. In the present study, we tested the hypothesis that negative social media messages would draw more attention than similar positive messages. Specifically, news broadcasts were presented in isolation and with simultaneous positive or negative Twitter messages on a tablet to 38 participants in a controlled experiment. Recognition memory, gaze tracking, cardiac responses, and self-reports were used as attentional indices. The presence of any tweets on the tablet decreased attention to the news broadcasts. As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets. Negative tweets did not, however, decrease attention to the news broadcasts. Taken together, the present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects of media multitasking. PMID:27144385
Ecological Relevance Determines Task Priority in Older Adults' Multitasking.

PubMed

Doumas, Michail; Krampe, Ralf Th

2015-05-01

Multitasking is a challenging aspect of human behavior, especially if the concurrently performed tasks are different in nature. Several studies demonstrated pronounced performance decrements (dual-task costs) in older adults for combinations of cognitive and motor tasks. However, patterns of costs among component tasks differed across studies and reasons for participants' resource allocation strategies remained elusive. We investigated young and older adults' multitasking of a working memory task and two sensorimotor tasks, one with low (finger force control) and one with high ecological relevance (postural control). The tasks were performed in single-, dual-, and triple-task contexts. Working memory accuracy was reduced in dual-task contexts with either sensorimotor task and deteriorated further under triple-task conditions. Postural and force performance deteriorated with age and task difficulty in dual-task contexts. However, in the triple-task context with its maximum resource demands, older adults prioritized postural control over both force control and memory. Our results identify ecological relevance as the key factor in older adults' multitasking. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Egocentric daily activity recognition via multitask clustering.

PubMed

Yan, Yan; Ricci, Elisa; Liu, Gaowen; Sebe, Nicu

2015-10-01

Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods.
Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

PubMed

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.
A Systematic Approach for Engagement Analysis Under Multitasking Environments

NASA Technical Reports Server (NTRS)

Zhang, Guangfan; Leddo, John; Xu, Roger; Richey, Carl; Schnell, Tom; McKenzie, Frederick; Li, Jiang

2011-01-01

An overload condition can lead to high stress for an operator and further cause substantial drops in performance. On the other extreme, in automated systems, an operator may become underloaded; in which case, it is difficult for the operator to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, a disengaged operation may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss a systematic approach monitor for extremes of cognitive workload and engagement in multitasking environments. Inferences of cognitive workload ar engagement are based on subjective evaluations, objective performance measures, physiological signals, and task analysis results. The systematic approach developed In this paper aggregates these types of information collected under the multitasking environment and can provide a real-time assessment or engagement.
A multitasking behavioral control system for the Robotic All-Terrain Lunar Exploration Rover (RATLER)

NASA Technical Reports Server (NTRS)

Klarer, Paul

1993-01-01

An approach for a robotic control system which implements so called 'behavioral' control within a realtime multitasking architecture is proposed. The proposed system would attempt to ameliorate some of the problems noted by some researchers when implementing subsumptive or behavioral control systems, particularly with regard to multiple processor systems and realtime operations. The architecture is designed to allow synchronous operations between various behavior modules by taking advantage of a realtime multitasking system's intertask communications channels, and by implementing each behavior module and each interconnection node as a stand-alone task. The potential advantages of this approach over those previously described in the field are discussed. An implementation of the architecture is planned for a prototype Robotic All Terrain Lunar Exploration Rover (RATLER) currently under development and is briefly described.
Natural orifice translumenal endoscopic surgery: Progress in humans since white paper

PubMed Central

Santos, Byron F; Hungness, Eric S

2011-01-01

Since the first description of the concept of natural orifice translumenal endoscopic surgery (NOTES), a substantial number of clinical NOTES reports have appeared in the literature. This editorial reviews the available human data addressing research questions originally proposed by the white paper, including determining the optimal method of access for NOTES, developing safe methods of lumenal closure, suturing and anastomotic devices, advanced multitasking platforms, addressing the risk of infection, managing complications, addressing challenges with visualization, and training for NOTES procedures. An analysis of the literature reveals that so far transvaginal access and closure appear to be the most feasible techniques for NOTES, with a limited, but growing transgastric, transrectal, and transesophageal NOTES experience in humans. The theoretically increased risk of infection as a result of NOTES procedures has not been substantiated in transvaginal and transgastric procedures so far. Development of suturing and anastomotic devices and advanced platforms for NOTES has progressed slowly, with limited clinical data on their use so far. Data on the optimal management and incidence of intraoperative complications remain sparse, although possible factors contributing to complications are discussed. Finally, this editorial discusses the likely direction of future NOTES development and its possible role in clinical practice. PMID:21483624
Real-Time Data Streaming and Storing Structure for the LHD's Fusion Plasma Experiments

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

DTIC Science & Technology

2014-07-23

found that even participants presenting with minor physiological effects of motion experienced a decline in multitasking performance. Further, Yu...literature has investigated the impact of task based interruptions such as being inter- rupted by a phone call or writing an email . In these...Engineers Journal. 102 (2) 65-72. Matsangas, P. (2013). The Effect of Mild Motion Sickness and Sopite Syndrome on Multitasking Cognitive Performance
Brain Imaging and rTMS Studies of Individual Differences in Cognitive Processing

DTIC Science & Technology

2013-08-09

Institution: Address: Phone: Fax: Email : Web Page: Contract: Project Title: Program Officer: Dr. Marcel Adam Just Carnegie Mellon University...and multitasking ) are included in the progress report using well established sets of materials. In the sentence and discourse tasks, participants...compensatory partnerships and the re-emergence of the primary regions. 3. Cortical reorganization induced by rTMS during multitasking (listening to
A queueing model of pilot decision making in a multi-task flight management situation

NASA Technical Reports Server (NTRS)

Walden, R. S.; Rouse, W. B.

1977-01-01

Allocation of decision making responsibility between pilot and computer is considered and a flight management task, designed for the study of pilot-computer interaction, is discussed. A queueing theory model of pilot decision making in this multi-task, control and monitoring situation is presented. An experimental investigation of pilot decision making and the resulting model parameters are discussed.
Feature Clustering for Accelerating Parallel Coordinate Descent

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

Scherrer, Chad; Tewari, Ambuj; Halappanavar, Mahantesh

2012-12-06

We demonstrate an approach for accelerating calculation of the regularization path for L1 sparse logistic regression problems. We show the benefit of feature clustering as a preconditioning step for parallel block-greedy coordinate descent algorithms.

An information theoretic approach of designing sparse kernel adaptive filters.

PubMed

Liu, Weifeng; Park, Il; Principe, José C

2009-12-01

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.
Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

NASA Astrophysics Data System (ADS)

Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

2016-07-01

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.
Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792
Hyperbaric Oxygen Environment Can Enhance Brain Activity and Multitasking Performance

PubMed Central

Vadas, Dor; Kalichman, Leonid; Hadanny, Amir; Efrati, Shai

2017-01-01

Background: The Brain uses 20% of the total oxygen supply consumed by the entire body. Even though, <10% of the brain is active at any given time, it utilizes almost all the oxygen delivered. In order to perform complex tasks or more than one task (multitasking), the oxygen supply is shifted from one brain region to another, via blood perfusion modulation. The aim of the present study was to evaluate whether a hyperbaric oxygen (HBO) environment, with increased oxygen supply to the brain, will enhance the performance of complex and/or multiple activities. Methods: A prospective, double-blind randomized control, crossover trial including 22 healthy volunteers. Participants were asked to perform a cognitive task, a motor task and a simultaneous cognitive-motor task (multitasking). Participants were randomized to perform the tasks in two environments: (a) normobaric air (1 ATA 21% oxygen) (b) HBO (2 ATA 100% oxygen). Two weeks later participants were crossed to the alternative environment. Blinding of the normobaric environment was achieved in the same chamber with masks on while hyperbaric sensation was simulated by increasing pressure in the first minute and gradually decreasing to normobaric environment prior to tasks performance. Results: Compared to the performance at normobaric conditions, both cognitive and motor single tasks scores were significantly enhanced by HBO environment (p < 0.001 for both). Multitasking performance was also significantly enhanced in HBO environment (p = 0.006 for the cognitive part and p = 0.02 for the motor part). Conclusions: The improvement in performance of both single and multi-tasking while in an HBO environment supports the hypothesis which according to, oxygen is indeed a rate limiting factor for brain activity. Hyperbaric oxygenation can serve as an environment for brain performance. Further studies are needed to evaluate the optimal oxygen levels for maximal brain performance. PMID:29021747
Hyperbaric Oxygen Environment Can Enhance Brain Activity and Multitasking Performance.

PubMed

Vadas, Dor; Kalichman, Leonid; Hadanny, Amir; Efrati, Shai

2017-01-01

Background: The Brain uses 20% of the total oxygen supply consumed by the entire body. Even though, <10% of the brain is active at any given time, it utilizes almost all the oxygen delivered. In order to perform complex tasks or more than one task (multitasking), the oxygen supply is shifted from one brain region to another, via blood perfusion modulation. The aim of the present study was to evaluate whether a hyperbaric oxygen (HBO) environment, with increased oxygen supply to the brain, will enhance the performance of complex and/or multiple activities. Methods: A prospective, double-blind randomized control, crossover trial including 22 healthy volunteers. Participants were asked to perform a cognitive task, a motor task and a simultaneous cognitive-motor task (multitasking). Participants were randomized to perform the tasks in two environments: (a) normobaric air (1 ATA 21% oxygen) (b) HBO (2 ATA 100% oxygen). Two weeks later participants were crossed to the alternative environment. Blinding of the normobaric environment was achieved in the same chamber with masks on while hyperbaric sensation was simulated by increasing pressure in the first minute and gradually decreasing to normobaric environment prior to tasks performance. Results: Compared to the performance at normobaric conditions, both cognitive and motor single tasks scores were significantly enhanced by HBO environment ( p < 0.001 for both). Multitasking performance was also significantly enhanced in HBO environment ( p = 0.006 for the cognitive part and p = 0.02 for the motor part). Conclusions: The improvement in performance of both single and multi-tasking while in an HBO environment supports the hypothesis which according to, oxygen is indeed a rate limiting factor for brain activity. Hyperbaric oxygenation can serve as an environment for brain performance. Further studies are needed to evaluate the optimal oxygen levels for maximal brain performance.
Memory and multitasking performance during acute allergic inflammation in seasonal allergic rhinitis.

PubMed

Trikojat, K; Buske-Kirschbaum, A; Plessow, F; Schmitt, J; Fischer, R

2017-04-01

In previous research, patients with seasonal allergic rhinitis (SAR) showed poorer school and work performance during periods of acute allergic inflammation, supporting the idea of an impact of SAR on cognitive functions. However, the specific cognitive domains particularly vulnerable to inflammatory processes are unclear. In this study, the influence of SAR on memory and multitasking performance, as two potentially vulnerable cognitive domains essential in everyday life functioning, was investigated in patients with SAR. Non-medicated patients with SAR (n = 41) and healthy non-allergic controls (n = 42) performed a dual-task paradigm and a verbal learning and memory test during and out of symptomatic allergy periods (pollen vs. non-pollen season). Disease-related factors (e.g. symptom severity, duration of symptoms, duration of disease) and allergy-related quality of life were evaluated as potential influences of cognitive performance. During the symptomatic allergy period, patients showed (1) poorer performance in word list-based learning (P = 0.028) and (2) a general slowing in processing speed (P < 0.001) and a shift in processing strategy (P < 0.001) in multitasking. Yet, typical parameters indicating specific multitasking costs were not affected. A significant negative association was found between learning performance and duration of disease (r = -0.451, P = 0.004), whereas symptom severity (r = 0.326; P = 0.037) and quality of life (r = 0.379; P = 0.015) were positively associated with multitasking strategy. Our findings suggest that SAR has a differentiated and complex impact on cognitive functions, which should be considered in the management of SAR symptoms. They also call attention to the importance of selecting sensitive measures and carefully interpreting cognitive outcomes. © 2017 John Wiley & Sons Ltd.
Prefrontal Cortex Structure Predicts Training-Induced Improvements in Multitasking Performance.

PubMed

Verghese, Ashika; Garner, K G; Mattingley, Jason B; Dux, Paul E

2016-03-02

The ability to perform multiple, concurrent tasks efficiently is a much-desired cognitive skill, but one that remains elusive due to the brain's inherent information-processing limitations. Multitasking performance can, however, be greatly improved through cognitive training (Van Selst et al., 1999, Dux et al., 2009). Previous studies have examined how patterns of brain activity change following training (for review, see Kelly and Garavan, 2005). Here, in a large-scale human behavioral and imaging study of 100 healthy adults, we tested whether multitasking training benefits, assessed using a standard dual-task paradigm, are associated with variability in brain structure. We found that the volume of the rostral part of the left dorsolateral prefrontal cortex (DLPFC) predicted an individual's response to training. Critically, this association was observed exclusively in a task-specific training group, and not in an active-training control group. Our findings reveal a link between DLPFC structure and an individual's propensity to gain from training on a task that taps the limits of cognitive control. Cognitive "brain" training is a rapidly growing, multibillion dollar industry (Hayden, 2012) that has been touted as the panacea for a variety of disorders that result in cognitive decline. A key process targeted by such training is "cognitive control." Here, we combined an established cognitive control measure, multitasking ability, with structural brain imaging in a sample of 100 participants. Our goal was to determine whether individual differences in brain structure predict the extent to which people derive measurable benefits from a cognitive training regime. Ours is the first study to identify a structural brain marker-volume of left hemisphere dorsolateral prefrontal cortex-associated with the magnitude of multitasking performance benefits induced by training at an individual level. Copyright © 2016 the authors 0270-6474/16/362638-08$15.00/0.
Multi-tasking uncovers right spatial neglect and extinction in chronic left-hemisphere stroke patients.

PubMed

Blini, Elvio; Romeo, Zaira; Spironelli, Chiara; Pitteri, Marco; Meneghello, Francesca; Bonato, Mario; Zorzi, Marco

2016-11-01

Unilateral Spatial Neglect, the most dramatic manifestation of contralesional space unawareness, is a highly heterogeneous syndrome. The presence of neglect is related to core spatially lateralized deficits, but its severity is also modulated by several domain-general factors (such as alertness or sustained attention) and by task demands. We previously showed that a computer-based dual-task paradigm exploiting both lateralized and non-lateralized factors (i.e., attentional load/multitasking) better captures this complex scenario and exacerbates deficits for the contralesional space after right hemisphere damage. Here we asked whether multitasking would reveal contralesional spatial disorders in chronic left-hemisphere damaged (LHD) stroke patients, a population in which impaired spatial processing is thought to be uncommon. Ten consecutive LHD patients with no signs of right-sided neglect at standard neuropsychological testing performed a computerized spatial monitoring task with and without concurrent secondary tasks (i.e., multitasking). Severe contralesional (right) space unawareness emerged in most patients under attentional load in both the visual and auditory modalities. Multitasking affected the detection of contralesional stimuli both when presented concurrently with an ipsilesional one (i.e., extinction for bilateral targets) and when presented in isolation (i.e., left neglect for right-sided targets). No spatial bias emerged in a control group of healthy elderly participants, who performed at ceiling, as well as in a second control group composed of patients with Mild Cognitive Impairment. We conclude that the pathological spatial asymmetry in LHD patients cannot be attributed to a global reduction of cognitive resources but it is the consequence of unilateral brain damage. Clinical and theoretical implications of the load-dependent lack of awareness for contralesional hemispace following LHD are discussed. Copyright Â© 2016. Published by Elsevier Ltd.
Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.
Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask

PubMed Central

Scheldrup, Melissa; Greenwood, Pamela M.; McKendrick, Ryan; Strohl, Jon; Bikson, Marom; Alam, Mahtab; McKinley, R. Andy; Parasuraman, Raja

2014-01-01

There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine). Non-invasive brain stimulation—specifically transcranial Direct Current Stimulation (tDCS)—has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks) were seen with a right parietal (C4, reference to left shoulder) montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008). No effects were seen with anodes over sites that stimulated only dorsal (C3) or only ventral (F10) attention networks. The speed subtask (update memory for symbols) benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical. PMID:25249958
Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask.

PubMed

Scheldrup, Melissa; Greenwood, Pamela M; McKendrick, Ryan; Strohl, Jon; Bikson, Marom; Alam, Mahtab; McKinley, R Andy; Parasuraman, Raja

2014-01-01

There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine). Non-invasive brain stimulation-specifically transcranial Direct Current Stimulation (tDCS)-has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks) were seen with a right parietal (C4, reference to left shoulder) montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008). No effects were seen with anodes over sites that stimulated only dorsal (C3) or only ventral (F10) attention networks. The speed subtask (update memory for symbols) benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical.
New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.
Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario

2018-04-01

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.
Experiences with Cray multi-tasking

NASA Technical Reports Server (NTRS)

Miya, E. N.

1985-01-01

The issues involved in modifying an existing code for multitasking is explored. They include Cray extensions to FORTRAN, an examination of the application code under study, designing workable modifications, specific code modifications to the VAX and Cray versions, performance, and efficiency results. The finished product is a faster, fully synchronous, parallel version of the original program. A production program is partitioned by hand to run on two CPUs. Loop splitting multitasks three key subroutines. Simply dividing subroutine data and control structure down the middle of a subroutine is not safe. Simple division produces results that are inconsistent with uniprocessor runs. The safest way to partition the code is to transfer one block of loops at a time and check the results of each on a test case. Other issues include debugging and performance. Task startup and maintenance (e.g., synchronization) are potentially expensive.
NITPICK: peak identification for mass spectrometry data

PubMed Central

Renard, Bernhard Y; Kirchner, Marc; Steen , Hanno; Steen, Judith AJ; Hamprecht , Fred A

2008-01-01

Background The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. Results This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averagine, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra. Conclusion Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from . PMID:18755032
Regression analysis of sparse asynchronous longitudinal data.

PubMed

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.
Soldier Cognitive Processes: Supporting Teleoperated Ground Vehicle Operations

DTIC Science & Technology

2014-12-01

They also examined training materials available to train robotic operators. Materials came from a pilot SUGV Master Trainer Course as well as from the...Allocation of Scarce Mental Resources. It is increasingly difficult to multitask if the similarity of the mental resources used in each task...1984). If separate tasks are not competing as much for the same mental resources, multitasking can be accomplished more effectively. Further, when
The Use of a UNIX-Based Workstation in the Information Systems Laboratory

DTIC Science & Technology

1989-03-01

system. The conclusions of the research and the resulting recommendations are presented in Chapter III. These recommendations include how to manage...required to run the program on a new system, these should not be significant changes. 2. Processing Environment The UNIX processing environment is...interactive with multi-tasking and multi-user capabilities. Multi-tasking refers to the fact that many programs can be run concurrently. This capability
How to Stop and Change a Response: The Role of Goal Activation in Multitasking

ERIC Educational Resources Information Center

Verbruggen, Frederick; Schneider, Darryl W.; Logan, Gordon D.

2008-01-01

Multitasking was studied in the stop-change paradigm, in which the response for a primary GO1 task had to be stopped and replaced by a response for a secondary GO2 task on some trials. In 2 experiments, the delay between the stop signal and the change signal was manipulated to determine which task goals (GO1, GO2, or STOP) were involved in…
The Role of Area 10 (BA10) in Human Multitasking and in Social Cognition: A Lesion Study

ERIC Educational Resources Information Center

Roca, Maria; Torralva, Teresa; Gleichgerrcht, Ezequiel; Woolgar, Alexandra; Thompson, Russell; Duncan, John; Manes, Facundo

2011-01-01

A role for rostral prefrontal cortex (BA10) has been proposed in multitasking, in particular, the selection and maintenance of higher order internal goals while other sub-goals are being performed. BA10 has also been implicated in the ability to infer someone else's feelings and thoughts, often referred to as theory of mind. While most of the data…

Multitask assessment of roads and vehicles network (MARVN)

NASA Astrophysics Data System (ADS)

Yang, Fang; Yi, Meng; Cai, Yiran; Blasch, Erik; Sullivan, Nichole; Sheaff, Carolyn; Chen, Genshe; Ling, Haibin

2018-05-01

Vehicle detection in wide area motion imagery (WAMI) has drawn increasing attention from the computer vision research community in recent decades. In this paper, we present a new architecture for vehicle detection on road using multi-task network, which is able to detect and segment vehicles, estimate their pose, and meanwhile yield road isolation for a given region. The multi-task network consists of three components: 1) vehicle detection, 2) vehicle and road segmentation, and 3) detection screening. Segmentation and detection components share the same backbone network and are trained jointly in an end-to-end way. Unlike background subtraction or frame differencing based methods, the proposed Multitask Assessment of Roads and Vehicles Network (MARVN) method can detect vehicles which are slowing down, stopped, and/or partially occluded in a single image. In addition, the method can eliminate the detections which are located at outside road using yielded road segmentation so as to decrease the false positive rate. As few WAMI datasets have road mask and vehicles bounding box anotations, we extract 512 frames from WPAFB 2009 dataset and carefully refine the original annotations. The resulting dataset is thus named as WAMI512. We extensively compare the proposed method with state-of-the-art methods on WAMI512 dataset, and demonstrate superior performance in terms of efficiency and accuracy.
On the effects of multimodal information integration in multitasking.

PubMed

Stock, Ann-Kathrin; Gohil, Krutika; Huster, René J; Beste, Christian

2017-07-07

There have recently been considerable advances in our understanding of the neuronal mechanisms underlying multitasking, but the role of multimodal integration for this faculty has remained rather unclear. We examined this issue by comparing different modality combinations in a multitasking (stop-change) paradigm. In-depth neurophysiological analyses of event-related potentials (ERPs) were conducted to complement the obtained behavioral data. Specifically, we applied signal decomposition using second order blind identification (SOBI) to the multi-subject ERP data and source localization. We found that both general multimodal information integration and modality-specific aspects (potentially related to task difficulty) modulate behavioral performance and associated neurophysiological correlates. Simultaneous multimodal input generally increased early attentional processing of visual stimuli (i.e. P1 and N1 amplitudes) as well as measures of cognitive effort and conflict (i.e. central P3 amplitudes). Yet, tactile-visual input caused larger impairments in multitasking than audio-visual input. General aspects of multimodal information integration modulated the activity in the premotor cortex (BA 6) as well as different visual association areas concerned with the integration of visual information with input from other modalities (BA 19, BA 21, BA 37). On top of this, differences in the specific combination of modalities also affected performance and measures of conflict/effort originating in prefrontal regions (BA 6).
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

NASA Astrophysics Data System (ADS)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.
Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139
Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions.

PubMed

Liu, Hongcheng; Yao, Tao; Li, Runze; Ye, Yinyu

2017-11-01

This paper concerns the folded concave penalized sparse linear regression (FCPSLR), a class of popular sparse recovery methods. Although FCPSLR yields desirable recovery performance when solved globally, computing a global solution is NP-complete. Despite some existing statistical performance analyses on local minimizers or on specific FCPSLR-based learning algorithms, it still remains open questions whether local solutions that are known to admit fully polynomial-time approximation schemes (FPTAS) may already be sufficient to ensure the statistical performance, and whether that statistical performance can be non-contingent on the specific designs of computing procedures. To address the questions, this paper presents the following threefold results: (i) Any local solution (stationary point) is a sparse estimator, under some conditions on the parameters of the folded concave penalties. (ii) Perhaps more importantly, any local solution satisfying a significant subspace second-order necessary condition (S 3 ONC), which is weaker than the second-order KKT condition, yields a bounded error in approximating the true parameter with high probability. In addition, if the minimal signal strength is sufficient, the S 3 ONC solution likely recovers the oracle solution. This result also explicates that the goal of improving the statistical performance is consistent with the optimization criteria of minimizing the suboptimality gap in solving the non-convex programming formulation of FCPSLR. (iii) We apply (ii) to the special case of FCPSLR with minimax concave penalty (MCP) and show that under the restricted eigenvalue condition, any S 3 ONC solution with a better objective value than the Lasso solution entails the strong oracle property. In addition, such a solution generates a model error (ME) comparable to the optimal but exponential-time sparse estimator given a sufficient sample size, while the worst-case ME is comparable to the Lasso in general. Furthermore, to guarantee the S 3 ONC admits FPTAS.
Sparse learning of stochastic dynamical equations

NASA Astrophysics Data System (ADS)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.
Age-Related Differences in Multiple Task Monitoring

PubMed Central

Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

2014-01-01

Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age. PMID:25215609
Effect of dual task activity on reaction time in males and females.

PubMed

Kaur, Manjinder; Nagpal, Sangeeta; Singh, Harpreet; Suhalka, M L

2014-01-01

The present study was designed to compare the auditory and visual reaction time on an Audiovisual Reaction Time Machine with the concomitant use of mobile phones in 52 women and 30 men in the age group of 18-40 years. Males showed significantly (p < 0.05) shorter reaction times, both auditory and visual, than females both during single task and multi task performance. But the percentage increase from their respective baseline auditory reaction times, was more in men than women during multitasking, in hand held (24.38% & 18.70% respectively) and hands free modes (36.40% & 18.40% respectively) of the use of cell phone. VRT increased non significantly during multitasking in both the groups. However, the multitasking per se has detrimental effect on the reaction times in both the groups studied. Hence, it should best be avoided in crucial and high attention demanding tasks like driving.
Threaded cognition: an integrated theory of concurrent multitasking.

PubMed

Salvucci, Dario D; Taatgen, Niels A

2008-01-01

The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking--that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual and motor resources). The theory specifies a parsimonious mechanism that allows for concurrent execution, resource acquisition, and resolution of resource conflicts, without the need for specialized executive processes. By instantiating this mechanism as a computational model, threaded cognition provides explicit predictions of how multitasking behavior can result in interference, or lack thereof, for a given set of tasks. The authors illustrate the theory in model simulations of several representative domains ranging from simple laboratory tasks such as dual-choice tasks to complex real-world domains such as driving and driver distraction. (c) 2008 APA, all rights reserved
Age-related differences in multiple task monitoring.

PubMed

Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

2014-01-01

Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age.
Do Athletes Excel at Everyday Tasks?

PubMed Central

CHADDOCK, LAURA; NEIDER, MARK B.; VOSS, MICHELLE W.; GASPAR, JOHN G.; KRAMER, ARTHUR F.

2014-01-01

Purpose Cognitive enhancements are associated with sport training. We extended the sport-cognition literature by using a realistic street crossing task to examine the multitasking and processing speed abilities of collegiate athletes and nonathletes. Methods Pedestrians navigated trafficked roads by walking on a treadmill in a virtual world, a challenge that requires the quick and simultaneous processing of multiple streams of information. Results Athletes had higher street crossing success rates than nonathletes, as reflected by fewer collisions with moving vehicles. Athletes also showed faster processing speed on a computer-based test of simple reaction time, and shorter reaction times were associated with higher street crossing success rates. Conclusions The results suggest that participation in athletics relates to superior street crossing multitasking abilities and that athlete and nonathlete differences in processing speed may underlie this difference. We suggest that cognitive skills trained in sport may transfer to performance on everyday fast-paced multitasking abilities. PMID:21407125
The Assessment of Military Multitasking Performance: Validation of a Dual-Task and Multitask Protocol

DTIC Science & Technology

2014-08-01

Revisions based on IRR findings and rater comments Charge of Quarters (CQ) Duty Requires the subject to organize and implement a plan in order to...to evaluate test burden) and malingering are planned . Where feasible, test-retest reliability for several of the tasks is being assessed during...in either a shopping mall or hospital lobby setting (Alderman, Burgess,Knight,&Henman, 2003;Cuberos- Urbano et al., 2013; Dawson et al., 2009
Multiprocessing MCNP on an IBN RS/6000 cluster

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

McKinney, G.W.; West, J.T.

1993-01-01

The advent of high-performance computer systems has brought to maturity programming concepts like vectorization, multiprocessing, and multitasking. While there are many schools of thought as to the most significant factor in obtaining order-of-magnitude increases in performance, such speedup can only be achieved by integrating the computer system and application code. Vectorization leads to faster manipulation of arrays by overlapping instruction CPU cycles. Discrete ordinates codes, which require the solving of large matrices, have proved to be major benefactors of vectorization. Monte Carlo transport, on the other hand, typically contains numerous logic statements and requires extensive redevelopment to benefit from vectorization.more » Multiprocessing and multitasking provide additional CPU cycles via multiple processors. Such systems are generally designed with either common memory access (multitasking) or distributed memory access. In both cases, theoretical speedup, as a function of the number of processors P and the fraction f of task time that multiprocesses, can be formulated using Amdahl's law: S(f, P) =1/(1-f+f/P). However, for most applications, this theoretical limit cannot be achieved because of additional terms (e.g., multitasking overhead, memory overlap, etc.) that are not included in Amdahl's law. Monte Carlo transport is a natural candidate for multiprocessing because the particle tracks are generally independent, and the precision of the result increases as the square Foot of the number of particles tracked.« less
Interruptions and multitasking in surgery: a multicentre observational study of the daily work patterns of doctors and nurses.

PubMed

Bellandi, Tommaso; Cerri, Alessandro; Carreras, Giulia; Walter, Scott; Mengozzi, Cipriana; Albolino, Sara; Mastrominico, Eleonora; Renzetti, Fernando; Tartaglia, Riccardo; Westbrook, Johanna

2018-01-01

The aim of this study was to obtain baseline data on doctors' and nurses' work activities and rates of interruptions and multitasking to improve work organisation and processes. Data were collected in six surgical units with the WOMBAT (Work Observation Method by Activity Timing) tool. Results show that doctors and nurses received approximately 13 interruptions per hour, or one interruption every 4.5 min. Compared to doctors, nurses were more prone to interruptions in most activities, while doctors performed multitasking (33.47% of their time, 95% CI 31.84-35.17%) more than nurses (15.23%, 95% CI 14.24-16.25%). Overall, the time dedicated to patient care is relatively limited for both professions (37.21%, 95% CI 34.95-39.60% for doctors, 27.22%, 95% CI 25.18-29.60% for nurses) compared to the time spent for registration of data and professional communication, that accounts for two-thirds of doctors' time and nearly half of nurses' time. Further investigation is needed on strategies to manage job demands and professional communications. Practitioner Summary: This study offers further findings on the characteristics and frequency of multitasking and interruptions in surgery, with a comparison of how they affect doctors and nurses. Further investigation is needed to improve the management of job demands and communications according to the results.
Getting stuck with pornography? Overuse or neglect of cybersex cues in a multitasking situation is related to symptoms of cybersex addiction.

PubMed

Schiebener, Johannes; Laier, Christian; Brand, Matthias

2015-03-01

Some individuals consume cybersex contents, such as pornographic material, in an addictive manner, which leads to severe negative consequences in private life or work. One mechanism leading to negative consequences may be reduced executive control over cognition and behavior that may be necessary to realize goal-oriented switching between cybersex use and other tasks and obligations of life. To address this aspect,we investigated 104 male participants with an executive multitasking paradigm with two sets: One set consisted of pictures of persons, the other set consisted of pornographic pictures. In both sets the pictures had to be classified according to certain criteria. The explicit goal was to work on all classification tasks to equal amounts, by switching between the sets and classification tasks in a balanced manner. We found that less balanced performance in this multitasking paradigm was associated with a higher tendency towards cybersex addiction. Persons with this tendency often either overused or neglected working on the pornographic pictures. The results indicate that reduced executive control over multitasking performance, when being confronted with pornographic material, may contribute to dysfunctional behaviors and negative consequences resulting from cybersex addiction. However, individuals with tendencies towards cybersex addiction seem to have either an inclination to avoid or to approach the pornographic material, as discussed in motivational models of addiction.
Getting stuck with pornography? Overuse or neglect of cybersex cues in a multitasking situation is related to symptoms of cybersex addiction

PubMed Central

LAIER, CHRISTIAN; BRAND, MATTHIAS

2015-01-01

Background and aims Some individuals consume cybersex contents, such as pornographic material, in an addictive manner, which leads to severe negative consequences in private life or work. One mechanism leading to negative consequences may be reduced executive control over cognition and behavior that may be necessary to realize goal-oriented switching between cybersex use and other tasks and obligations of life. Methods To address this aspect, we investigated 104 male participants with an executive multitasking paradigm with two sets: One set consisted of pictures of persons, the other set consisted of pornographic pictures. In both sets the pictures had to be classified according to certain criteria. The explicit goal was to work on all classification tasks to equal amounts, by switching between the sets and classification tasks in a balanced manner. Results We found that less balanced performance in this multitasking paradigm was associated with a higher tendency towards cybersex addiction. Persons with this tendency often either overused or neglected working on the pornographic pictures. Discussion The results indicate that reduced executive control over multitasking performance, when being confronted with pornographic material, may contribute to dysfunctional behaviors and negative consequences resulting from cybersex addiction. However, individuals with tendencies towards cybersex addiction seem to have either an inclination to avoid or to approach the pornographic material, as discussed in motivational models of addiction. PMID:25786495
Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

NASA Astrophysics Data System (ADS)

Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

2013-10-01

Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.
Improving Cluster Analysis with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-12-01

regression trees Daisy DISsimilAritY PAM partitioning around medoids PMA penalized multivariate analysis SPC sparse principal components UPGMA unweighted...unweighted pair-group average method ( UPGMA ). This method measures dissimilarities between all objects in two clusters and takes the average value
Longitudinal assessment of chemotherapy-induced alterations in brain activation during multitasking and its relation with cognitive complaints.

PubMed

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ronald; Emsell, Louise; Smeets, Ann; Christiaens, Marie-Rose; Amant, Frederic; Sunaert, Stefan

2014-07-01

To examine whether cognitive complaints after treatment for breast cancer are associated with detectable changes in brain activity during multitasking. Eighteen patients who were scheduled to receive chemotherapy performed a functional magnetic resonance imaging multitasking task in the scanner before the start of treatment (t1) and 4 to 6 months after finishing treatment (t2). Sixteen patients who were not scheduled to receive chemotherapy and 17 matched healthy controls performed the same task at matched intervals. Task difficulty level was adjusted individually to match performance across participants. Statistical Parametric Mapping 8 (SPM8) software was used for within-group, between-group, and group-by-time interaction image analyses. Voxel-based paired t tests revealed significantly decreased activation (P < .05) from t1 to t2 at matched performance in the multitasking network of chemotherapy-treated patients, whereas no changes were noted in either of the control groups. At baseline, there were no differences between the groups. Furthermore, in contrast to controls, the chemotherapy-treated patients reported a significant increase in cognitive complaints (P < .05) at t2. Significant (P < .05) correlations were found between these increases and decreases in multitasking-related brain activation. Moreover, a significant group-by-time interaction (P < .05) was found whereby chemotherapy-treated patients showed decreased activation and healthy controls did not. These results suggest that changes in brain activity may underlie chemotherapy-induced cognitive complaints. The observed changes might be related to chemotherapy-induced damage to the brain or reduced connectivity between brain regions rather than to changes in effort or changes in functional strategy. To the best of our knowledge, this is the first longitudinal study providing evidence for a relationship between longitudinal changes in cognitive complaints and changes in brain activation after chemotherapy. © 2014 by American Society of Clinical Oncology.

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PubMed Central

Chen, Yasheng; Juttukonda, Meher; Su, Yi; Benzinger, Tammie; Rubin, Brian G.; Lee, Yueh Z.; Lin, Weili; Shen, Dinggang; Lalush, David

2015-01-01

Purpose To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. © RSNA, 2014 PMID:25521778
The Assessment of Military Multitasking Performance: Validation of a Dual Task and Multitask Protocol

DTIC Science & Technology

2013-09-01

collegiate football players: the NCAA Concussion Study. JAMA 2003; 290(19): 2549-55. 9. McCrea M, Iverson GL, McAllister TW, et al: An integrated review...time fol- lowing concussion in collegiate football players: the NCAA Concussion Study. JAMA. 2003;290:2556–2563. 6 Riemann BL, Guskiewicz KM. Effects of...of Soldiering for use after concussion /mild traumatic brain injury (mTBI). Task evaluation criteria including inter-rater reliability and total test
The Assessment of Military Multitasking Performance: Validation of a Dual-Task and Multitask Protocol

DTIC Science & Technology

2015-11-01

handwriting .  Return all supplies and materials to their original locations and place the footstool on the CQ desk (touch the desk surface) at the end...and seconds between when the examiner says , “Start” and when the participant a) reports to SPC Smith/Guard Shack that he/she is finished with the...abbreviation of the first work area the subject enters after the examiner says , “start”. For 2. To_____ write the abbreviation for the work
A Comparison of Two Methods Used for Ranking Task Exposure Levels Using Simulated Multi-Task Data

DTIC Science & Technology

1999-12-17

OF OKLAHOMA HEALTH SCIENCES CENTER GRADUATE COLLEGE A COMPARISON OF TWO METHODS USED FOR RANKING TASK EXPOSURE LEVELS USING SIMULATED MULTI-TASK...COSTANTINO Oklahoma City, Oklahoma 1999 ^ooo wx °^ A COMPARISON OF TWO METHODS USED FOR RANKING TASK EXPOSURE LEVELS USING SIMULATED MULTI-TASK DATA... METHODS AND MATERIALS 9 TV. RESULTS 14 V. DISCUSSION AND CONCLUSION 28 LIST OF REFERENCES 31 APPENDICES 33 Appendix A JJ -in Appendix B Dl IV
DeepSkeleton: Learning Multi-Task Scale-Associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

NASA Astrophysics Data System (ADS)

Shen, Wei; Zhao, Kai; Jiang, Yuan; Wang, Yan; Bai, Xiang; Yuille, Alan

2017-11-01

Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network. The network is trained by multi-task learning, where one task is skeleton localization to classify whether a pixel is a skeleton pixel or not, and the other is skeleton scale prediction to regress the scale of each skeleton pixel. Supervision is imposed at different stages by guiding the scale-associated side outputs toward the groundtruth skeletons at the appropriate scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to detect skeleton pixels using multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors. Additionally, the usefulness of the obtained skeletons and scales (thickness) are verified on two object detection applications: Foreground object segmentation and object proposal detection.
Unconditional or Conditional Logistic Regression Model for Age-Matched Case-Control Data?

PubMed

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case-control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.
Unconditional or Conditional Logistic Regression Model for Age-Matched Case–Control Data?

PubMed Central

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls. PMID:29552553
Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

NASA Astrophysics Data System (ADS)

Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

2014-03-01

We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.
Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

PubMed

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

2018-02-13

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

NASA Astrophysics Data System (ADS)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.
NITPICK: peak identification for mass spectrometry data.

PubMed

Renard, Bernhard Y; Kirchner, Marc; Steen, Hanno; Steen, Judith A J; Hamprecht, Fred A

2008-08-28

The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra. Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from (http://hci.iwr.uni-heidelberg.de/mip/proteomics/).
Regression analysis of sparse asynchronous longitudinal data

PubMed Central

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P.

2015-01-01

Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus. PMID:26568699
Approximate entropy: a new evaluation approach of mental workload under multitask conditions

NASA Astrophysics Data System (ADS)

Yao, Lei; Li, Xiaoling; Wang, Wei; Dong, Yuanzhe; Jiang, Ying

2014-04-01

There are numerous instruments and an abundance of complex information in the traditional cockpit display-control system, and pilots require a long time to familiarize themselves with the cockpit interface. This can cause accidents when they cope with emergency events, suggesting that it is necessary to evaluate pilot cognitive workload. In order to establish a simplified method to evaluate cognitive workload under a multitask condition. We designed a series of experiments involving different instrument panels and collected electroencephalograms (EEG) from 10 healthy volunteers. The data were classified and analyzed with an approximate entropy (ApEn) signal processing. ApEn increased with increasing experiment difficulty, suggesting that it can be used to evaluate cognitive workload. Our results demonstrate that ApEn can be used as an evaluation criteria of cognitive workload and has good specificity and sensitivity. Moreover, we determined an empirical formula to assess the cognitive workload interval, which can simplify cognitive workload evaluation under multitask conditions.
Proactive Motor Control Reduces Monetary Risk Taking in Gambling

PubMed Central

Adams, Rachel; Chambers, Christopher D.

2012-01-01

Less supervision by the executive system after disruption of the right prefrontal cortex leads to increased risk taking in gambling because superficially attractive—but risky—choices are not suppressed. Similarly, people might gamble more in multitask situations than in single-task situations because concurrent executive processes usually interfere with each other. In the study reported here, we used a novel monetary decision-making paradigm to investigate whether multitasking could reduce rather than increase risk taking in gambling. We found that performing a task that induced cautious motor responding reduced gambling in a multitask situation (Experiment 1). We then found that a short period of inhibitory training lessened risk taking in gambling at least 2 hr later (Experiments 2 and 3). Our findings indicate that proactive motor control strongly affects monetary risk taking in gambling. The link between control systems at different cognitive levels might be exploited to develop new methods for rehabilitation of addiction and impulse-control disorders. PMID:22692336
The epidemic of distraction.

PubMed

Weksler, Marc E; Weksler, Babette B

2012-01-01

Multitasking is a rapidly growing phenomenon affecting all segments of the population but is rarely as successful as its proponents believe. The use of mobile electronic devices contributes importantly to multitasking and cognitive overload. Although personal electronic devices provide many benefits, their adverse effects are frequently overlooked. Personal observation and a review of the scientific literature supports the view that overuse or misuse of personal electronic devices promotes cognitive overload, impairs multitasking and lowers performance at all ages but particularly in the elderly. This phenomenon appears to be rapidly increasing and threatens to become a tsunami as spreading electronic waves cause an 'epidemic of distraction'. Mobile electronic devices often bring benefits to their users in terms of rapid access to information. However, there is a dark side to the increasing addiction to these devices that challenges the health and well-being of the entire population, targeting, in particular, the aged and infirm. New approaches to information gathering can foster creativity if cognitive overload is avoided. Copyright © 2012 S. Karger AG, Basel.
Metacognition of Multi-Tasking: How Well Do We Predict the Costs of Divided Attention?

PubMed Central

Finley, Jason R.; Benjamin, Aaron S.; McCarley, Jason S.

2014-01-01

Risky multi-tasking, such as texting while driving, may occur because people misestimate the costs of divided attention. In two experiments, participants performed a computerized visual-manual tracking task in which they attempted to keep a mouse cursor within a small target that moved erratically around a circular track. They then separately performed an auditory n-back task. After practicing both tasks separately, participants received feedback on their single-task tracking performance and predicted their dual-task tracking performance before finally performing the two tasks simultaneously. Most participants correctly predicted reductions in tracking performance under dual-task conditions, with a majority overestimating the costs of dual-tasking. However, the between-subjects correlation between predicted and actual performance decrements was near zero. This combination of results suggests that people do anticipate costs of multi-tasking, but have little metacognitive insight on the extent to which they are personally vulnerable to the risks of divided attention, relative to other people. PMID:24490818
Examining the impact of age and multitasking on motorcycle conspicuity.

PubMed

Ledbetter, Jonathan L; Boyce, Michael W; Fekety, Drea K; Sawyer, Ben; Smither, Janan A

2012-01-01

This poster presents a study to assess one's ability to detect motorcycles under different conditions of conspicuity while performing a secondary visual load task. Previous research in which participants were required to detect motorcycles revealed differences in age (young adults/older adult) as well as differences associated with motorcycle conspicuity conditions. Past research has specifically found motorcycles with headlights ON and modulating headlights (flashing) to be more conspicuous than motorcycles with headlights OFF within traffic conditions. The present study seeks to provide more information on the effects of multitasking on motorcycle conspicuity and safety. The current study seeks to determine the degree to which multitasking limits the conspicuity of a motorcycle within traffic. We expect our results will indicate main effects for distraction task, age, gender, motorcycle lighting conditions, and vehicular DRLs on one's ability to effectively detect a motorcycle. The results have implications for motorcycle safety in general and through this research, a better understanding of motorcycle conspicuity can be established so as to minimize the risk involved with motorcycle operation.
Metacognition of multitasking: How well do we predict the costs of divided attention?

PubMed

Finley, Jason R; Benjamin, Aaron S; McCarley, Jason S

2014-06-01

Risky multitasking, such as texting while driving, may occur because people misestimate the costs of divided attention. In two experiments, participants performed a computerized visual-manual tracking task in which they attempted to keep a mouse cursor within a small target that moved erratically around a circular track. They then separately performed an auditory n-back task. After practicing both tasks separately, participants received feedback on their single-task tracking performance and predicted their dual-task tracking performance before finally performing the 2 tasks simultaneously. Most participants correctly predicted reductions in tracking performance under dual-task conditions, with a majority overestimating the costs of dual-tasking. However, the between-subjects correlation between predicted and actual performance decrements was near 0. This combination of results suggests that people do anticipate costs of multitasking, but have little metacognitive insight on the extent to which they are personally vulnerable to the risks of divided attention, relative to other people. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking

PubMed Central

Kievit, Rogier A.; Davis, Simon W.; Mitchell, Daniel J.; Taylor, Jason R.; Duncan, John; Tyler, Lorraine K.; Brayne, Carol; Bullmore, Ed; Calder, Andrew; Cusack, Rhodri; Dalgleish, Tim; Matthews, Fiona; Marslen-Wilson, William; Rowe, James; Shafto, Meredith; Campbell, Karen; Cheung, Teresa; Geerligs, Linda; McCarrey, Anna; Tsvetanov, Kamen; Williams, Nitin; Bates, Lauren; Emery, Tina; Erzinçlioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances; Johnston, Patricia; Kavanagh-Williamson, Thea; Kwasniewska, Magdalena; McMinn, Alison; Norman, Kim; Penrose, Jessica; Roby, Fiona; Rowland, Diane; Sargeant, John; Squire, Maggie; Stevens, Beth; Stoddart, Aldabra; Stone, Cheryl; Thompson, Tracy; Yazlik, Ozlem; Barnes, Dan; Dixon, Marie; Hillman, Jaya; Mitchell, Joanne; Villis, Laura; Henson, Richard N.A.

2014-01-01

Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N=567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate fluid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and fluid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing. PMID:25519467
Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

NASA Astrophysics Data System (ADS)

Liu, Chunhui; Zhang, Duona; Zhao, Xintao

2018-03-01

Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.

Men's Alcohol Expectancies at Selected Community Colleges

ERIC Educational Resources Information Center

Derby, Dustin C.

2011-01-01

Men's alcohol expectancies are an important cognitive-behavioral component of their consumption; yet, sparse research details such behaviors for men in two-year colleges. Selected for inclusion with the current study were 563 men from seven Illinois community colleges. Logistic regression analysis indicated four significant, positive relationships…
Prevalence of and attitudes about distracted driving in college students.

PubMed

Hill, Linda; Rybar, Jill; Styer, Tara; Fram, Ethan; Merchant, Gina; Eastman, Amelia

2015-01-01

To identify current distracted driving (DD) behaviors among college students, primarily those involving cell phone use, and elucidate the opinions of the students on the most effective deterrent or intervention for reducing cell phone use. Students enrolled at 12 colleges and universities were recruited to participate in an online, anonymous survey. Recruitment was done via school-based list-serves and posters. School sizes ranged from 476 to over 30,000. The validated survey included 38 questions; 17 were specifically related to distracted driving. Four thousand nine hundred sixty-four participants completed the surveys; the average age was 21.8, 66% were female, 82.7% were undergraduates, and 47% were white/non-Hispanic. Additionally, 4,517 (91%) reported phoning and/or texting while driving; 4,467 (90%) of drivers said they talk on the phone while driving; 1,241 (25%) reported using a hands-free device "most of the time"; 4,467 (90%) of drivers reported texting while driving; 2,488 (50%) reported sending texts while driving on the freeway; 2,978 (60%) while in stop-and-go traffic or on city streets; and 4,319 (87%) at traffic lights. Those who drove more often were more likely to drive distracted. When asked about their capability to drive distracted, 46% said they were capable or very capable of talking on a cell phone and driving, but they felt that only 8.5% of other drivers were capable. In a multivariate model, 9 predictors explained 44% of the variance in DD, which was statistically significant, F (17, 4945) = 224.31; P <.0001; R(2) = 0.44. The four strongest predictors (excluding driving frequency) were self-efficacy (i.e., confidence) in driving while multitasking (β = 0.37), perception of safety of multitasking while driving (β = 0.19), social norms (i.e., observing others multitasking while driving; β = 0.29), and having a history of crashing due to multitasking while driving (β = 0.11). Distracted driving is a highly prevalent behavior among college students who have higher confidence in their own driving skills and ability to multitask than they have in other drivers' abilities. Drivers' self-efficacy for driving and multitasking in the car, coupled with a greater likelihood of having witnessed DD behaviors in others, greatly increased the probability that a student would engage in DD. Most students felt that policies, such as laws impacting driving privilege and insurance rate increases, would influence their behavior.
Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.
TH-AB-202-08: A Robust Real-Time Surface Reconstruction Method On Point Clouds Captured From a 3D Surface Photogrammetry System

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

Liu, W; Sawant, A; Ruan, D

2016-06-15

Purpose: Surface photogrammetry (e.g. VisionRT, C-Rad) provides a noninvasive way to obtain high-frequency measurement for patient motion monitoring in radiotherapy. This work aims to develop a real-time surface reconstruction method on the acquired point clouds, whose acquisitions are subject to noise and missing measurements. In contrast to existing surface reconstruction methods that are usually computationally expensive, the proposed method reconstructs continuous surfaces with comparable accuracy in real-time. Methods: The key idea in our method is to solve and propagate a sparse linear relationship from the point cloud (measurement) manifold to the surface (reconstruction) manifold, taking advantage of the similarity inmore » local geometric topology in both manifolds. With consistent point cloud acquisition, we propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, building the point correspondences by the iterative closest point (ICP) method. To accommodate changing noise levels and/or presence of inconsistent occlusions, we further propose a modified sparse regression (MSR) model to account for the large and sparse error built by ICP, with a Laplacian prior. We evaluated our method on both clinical acquired point clouds under consistent conditions and simulated point clouds with inconsistent occlusions. The reconstruction accuracy was evaluated w.r.t. root-mean-squared-error, by comparing the reconstructed surfaces against those from the variational reconstruction method. Results: On clinical point clouds, both the SR and MSR models achieved sub-millimeter accuracy, with mean reconstruction time reduced from 82.23 seconds to 0.52 seconds and 0.94 seconds, respectively. On simulated point cloud with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent performance despite the introduced occlusions. Conclusion: We have developed a real-time and robust surface reconstruction method on point clouds acquired by photogrammetry systems. It serves an important enabling step for real-time motion tracking in radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.« less
A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system.

PubMed

Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

2016-05-01

To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.
A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

PubMed Central

Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

2016-01-01

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications. PMID:27147347
Scalability of Robotic Controllers: Effects of Progressive Autonomy on Intelligence, Surveillance, and Reconnaissance Robotic Tasks

DTIC Science & Technology

2012-09-01

away from the MOCU. The semi-autonomous mode was preferred over the teleoperated mode for multitasking , maintaining SA, avoiding obstacles, and...0 23 Software with icons 0 0 0 0 2 25 Pull-down menu * 0 0 0 0 3 24 Graphics/drawing features in software packages* 3 8 1 4 3 8 Email 1 0 0 0 1...r. Navigate to the next waypoint or set of hash lines 5.27 5.08 6.25 s. Ability to multitask (operate/monitor robot and communicate on the radio
Multitasking-Pascal extensions solve concurrency problems

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

Mackie, P.H.

1982-09-29

To avoid deadlock (one process waiting for a resource than another process can't release) and indefinite postponement (one process being continually denied a resource request) in a multitasking-system application, it is possible to use a high-level development language with built-in concurrency handlers. Parallel Pascal is one such language; it extends standard Pascal via special task synchronizers: a new data type called signal, new system procedures called wait and send and a Boolean function termed awaited. To understand the language's use the author examines the problems it helps solve.
Multitasking OS manages a team of processors

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

Ripps, D.L.

1983-07-21

MTOS-68k is a real-time multitasking operating system designed for the popular MC68000 microprocessors. It aproaches task coordination and synchronization in a fashion that matches uniquely the structural simplicity and regularity of the 68000 instruction set. Since in many 68000 applications the speed and power of one CPU are not enough, MTOS-68k has been designed to support multiple processors, as well as multiple tasks. Typically, the devices are tightly coupled single-board computers, that is they share a backplane and parts of global memory.
Multitasking the INS3D-LU code on the Cray Y-MP

NASA Technical Reports Server (NTRS)

Fatoohi, Rod; Yoon, Seokkwan

1991-01-01

This paper presents the results of multitasking the INS3D-LU code on eight processors. The code is a full Navier-Stokes solver for incompressible fluid in three dimensional generalized coordinates using a lower-upper symmetric-Gauss-Seidel implicit scheme. This code has been fully vectorized on oblique planes of sweep and parallelized using autotasking with some directives and minor modifications. The timing results for five grid sizes are presented and analyzed. The code has achieved a processing rate of over one Gflops.
A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Boucher, Thomas F.; Ozanne, Marie V.; Carmosino, Marco L.; Dyar, M. Darby; Mahadevan, Sridhar; Breves, Elly A.; Lepore, Kate H.; Clegg, Samuel M.

2015-05-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO2, Fe2O3, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na2O, K2O, TiO2, and P2O5, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels) relative to the small number of samples studied. The best-performing models were SVR-Lin for SiO2, MgO, Fe2O3, and Na2O, lasso for Al2O3, elastic net for MnO, and PLS-1 for CaO, TiO2, and K2O. Although these differences in model performance between methods were identified, most of the models produce comparable results when p ≤ 0.05 and all techniques except kNN produced statistically-indistinguishable results. It is likely that a combination of models could be used together to yield a lower total error of prediction, depending on the requirements of the user.
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.
Distributed multitasking ITS with PVM

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

Fan, W.C.; Halbleib, J.A. Sr.

1995-12-31

Advances in computer hardware and communication software have made it possible to perform parallel-processing computing on a collection of desktop workstations. For many applications, multitasking on a cluster of high-performance workstations has achieved performance comparable to or better than that on a traditional supercomputer. From the point of view of cost-effectiveness, it also allows users to exploit available but unused computational resources and thus achieve a higher performance-to-cost ratio. Monte Carlo calculations are inherently parallelizable because the individual particle trajectories can be generated independently with minimum need for interprocessor communication. Furthermore, the number of particle histories that can be generatedmore » in a given amount of wall-clock time is nearly proportional to the number of processors in the cluster. This is an important fact because the inherent statistical uncertainty in any Monte Carlo result decreases as the number of histories increases. For these reasons, researchers have expended considerable effort to take advantage of different parallel architectures for a variety of Monte Carlo radiation transport codes, often with excellent results. The initial interest in this work was sparked by the multitasking capability of the MCNP code on a cluster of workstations using the Parallel Virtual Machine (PVM) software. On a 16-machine IBM RS/6000 cluster, it has been demonstrated that MCNP runs ten times as fast as on a single-processor CRAY YMP. In this paper, we summarize the implementation of a similar multitasking capability for the coupled electronphoton transport code system, the Integrated TIGER Series (ITS), and the evaluation of two load-balancing schemes for homogeneous and heterogeneous networks.« less
Distributed multitasking ITS with PVM

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

Fan, W.C.; Halbleib, J.A. Sr.

1995-02-01

Advances of computer hardware and communication software have made it possible to perform parallel-processing computing on a collection of desktop workstations. For many applications, multitasking on a cluster of high-performance workstations has achieved performance comparable or better than that on a traditional supercomputer. From the point of view of cost-effectiveness, it also allows users to exploit available but unused computational resources, and thus achieve a higher performance-to-cost ratio. Monte Carlo calculations are inherently parallelizable because the individual particle trajectories can be generated independently with minimum need for interprocessor communication. Furthermore, the number of particle histories that can be generated inmore » a given amount of wall-clock time is nearly proportional to the number of processors in the cluster. This is an important fact because the inherent statistical uncertainty in any Monte Carlo result decreases as the number of histories increases. For these reasons, researchers have expended considerable effort to take advantage of different parallel architectures for a variety of Monte Carlo radiation transport codes, often with excellent results. The initial interest in this work was sparked by the multitasking capability of MCNP on a cluster of workstations using the Parallel Virtual Machine (PVM) software. On a 16-machine IBM RS/6000 cluster, it has been demonstrated that MCNP runs ten times as fast as on a single-processor CRAY YMP. In this paper, we summarize the implementation of a similar multitasking capability for the coupled electron/photon transport code system, the Integrated TIGER Series (ITS), and the evaluation of two load balancing schemes for homogeneous and heterogeneous networks.« less
Subjective scaling of mental workload in a multi-task environment

NASA Technical Reports Server (NTRS)

Daryanian, B.

1982-01-01

Those factors in a multi-task environment that contribute to the operators' "sense" of mental workload were identified. The subjective judgment as conscious experience of mental effort was decided to be the appropriate method of measurement. Thurstone's law of comparative judgment was employed in order to construct interval scales of subjective mental workload from paired comparisons data. An experimental paradigm (Simulated Multi-Task Decision-Making Environment) was employed to represent the ideal experimentally controlled environment in which human operators were asked to "attend" to different cases of Tulga's decision making tasks. Through various statistical analyses it was found that, in general, a lower number of tasks-to-be-processed per unit time (a condition associated with longer interarrival times), results in a lower mental workload, a higher consistency of judgments within a subject, a higher degree of agreement among the subjects, and larger distances between the cases on the Thurstone scale of subjective mental workload. The effects of various control variables and their interactions, and the different characteristics of the subjects on the variation of subjective mental workload are demonstrated.
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

PubMed

Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling

2015-11-01

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
Young and Older Adults' Gender Stereotype in Multitasking

PubMed Central

Strobach, Tilo; Woszidlo, Alesia

2015-01-01

In the present study, we investigated discrepancies between two components of stereotyping by means of the popular notion that women are better at multitasking behaviors: the cognitive structure in individuals (personal belief) and the perceived consensus regarding certain beliefs (perceived belief of groups). With focus on this notion, we examined whether there was empirical evidence for the stereotype's existence and whether and how it was shared among different age groups. Data were collected from 241 young (n = 129) and older (n = 112) German individuals. The reported perceptions of gender effects at multitasking were substantial and thus demonstrated the existence of its stereotype. Importantly, in young and older adults, this stereotype existed in the perception of attributed characteristics by members of a collective (perceived belief of groups). When contrasting this perceived belief of groups and the personal belief, older adults showed a similar level of conformation of the gender stereotype while young adults were able to differentiate between these perspectives. Thus, young adults showed a discrepancy between the stereotype's components cognitive structure in individuals and perceived consensus regarding certain beliefs. PMID:26733913
Young and Older Adults' Gender Stereotype in Multitasking.

PubMed

Strobach, Tilo; Woszidlo, Alesia

2015-01-01

In the present study, we investigated discrepancies between two components of stereotyping by means of the popular notion that women are better at multitasking behaviors: the cognitive structure in individuals (personal belief) and the perceived consensus regarding certain beliefs (perceived belief of groups). With focus on this notion, we examined whether there was empirical evidence for the stereotype's existence and whether and how it was shared among different age groups. Data were collected from 241 young (n = 129) and older (n = 112) German individuals. The reported perceptions of gender effects at multitasking were substantial and thus demonstrated the existence of its stereotype. Importantly, in young and older adults, this stereotype existed in the perception of attributed characteristics by members of a collective (perceived belief of groups). When contrasting this perceived belief of groups and the personal belief, older adults showed a similar level of conformation of the gender stereotype while young adults were able to differentiate between these perspectives. Thus, young adults showed a discrepancy between the stereotype's components cognitive structure in individuals and perceived consensus regarding certain beliefs.
Multitask SVM learning for remote sensing data classification

NASA Astrophysics Data System (ADS)

Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

2010-10-01

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.
Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

Predictors of Asian American Adolescents' Suicide Attempts: A Latent Class Regression Analysis

ERIC Educational Resources Information Center

Wong, Y. Joel; Maffini, Cara S.

2011-01-01

Although suicide-related outcomes among Asian American adolescents are a serious public health problem in the United States, research in this area has been relatively sparse. To address this gap in the empirical literature, this study examined subgroups of Asian American adolescents for whom family, school, and peer relationships exerted…
Optimal Sparse Upstream Sensor Placement for Hydrokinetic Turbines

NASA Astrophysics Data System (ADS)

Cavagnaro, Robert; Strom, Benjamin; Ross, Hannah; Hill, Craig; Polagye, Brian

2016-11-01

Accurate measurement of the flow field incident upon a hydrokinetic turbine is critical for performance evaluation during testing and setting boundary conditions in simulation. Additionally, turbine controllers may leverage real-time flow measurements. Particle image velocimetry (PIV) is capable of rendering a flow field over a wide spatial domain in a controlled, laboratory environment. However, PIV's lack of suitability for natural marine environments, high cost, and intensive post-processing diminish its potential for control applications. Conversely, sensors such as acoustic Doppler velocimeters (ADVs), are designed for field deployment and real-time measurement, but over a small spatial domain. Sparsity-promoting regression analysis such as LASSO is utilized to improve the efficacy of point measurements for real-time applications by determining optimal spatial placement for a small number of ADVs using a training set of PIV velocity fields and turbine data. The study is conducted in a flume (0.8 m2 cross-sectional area, 1 m/s flow) with laboratory-scale axial and cross-flow turbines. Predicted turbine performance utilizing the optimal sparse sensor network and associated regression model is compared to actual performance with corresponding PIV measurements.
Fault recovery for real-time, multi-tasking computer system

NASA Technical Reports Server (NTRS)

Hess, Richard (Inventor); Kelly, Gerald B. (Inventor); Rogers, Randy (Inventor); Stange, Kent A. (Inventor)

2011-01-01

System and methods for providing a recoverable real time multi-tasking computer system are disclosed. In one embodiment, a system comprises a real time computing environment, wherein the real time computing environment is adapted to execute one or more applications and wherein each application is time and space partitioned. The system further comprises a fault detection system adapted to detect one or more faults affecting the real time computing environment and a fault recovery system, wherein upon the detection of a fault the fault recovery system is adapted to restore a backup set of state variables.
Computing group cardinality constraint solutions for logistic regression problems.

PubMed

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.
NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles.

PubMed

Tariq, Amara; Karim, Asim; Foroosh, Hassan

2017-10-01

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities' network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time.
Long-Term Exercise in Older Adults: 4-Year Outcomes of Music-Based Multitask Training

PubMed Central

Herrmann, François R.; Fielding, Roger A.; Reid, Kieran F.; Rizzoli, René; Trombetti, Andrea

2016-01-01

Prospective controlled evidence supporting the efficacy of long-term exercise to prevent physical decline and reduce falls in old age is lacking. The present study aimed to assess the effects of long-term music-based multitask exercise (i.e., Jaques-Dalcroze eurhythmics) on physical function and fall risk in older adults. A 3-year follow-up extension of a 1-year randomized controlled trial (NCT01107288) was conducted in Geneva (Switzerland), in which 134 community-dwellers aged ≥65 years at increased risk of falls received a 6-month music-based multitask exercise program. Four years following original trial enrolment, 52 subjects (baseline mean ± SD age, 75 ± 8 years) who (i) have maintained exercise program participation through the 4-year follow-up visit (“long-term intervention group”, n = 23) or (ii) have discontinued participation following original trial completion (“control group”, n = 29) were studied. They were reassessed in a blind fashion, using the same procedures as at baseline. At 4 years, linear mixed-effects models showed significant gait (gait speed, P = 0.006) and balance (one-legged stance time, P = 0.015) improvements in the long-term intervention group, compared with the control group. Also, long-term intervention subjects did better on Timed Up & Go, Five-Times-Sit-to-Stand and handgrip strength tests, than controls (P < 0.05, for all comparisons). Furthermore, the exercise program reduced the risk of falling (relative risk, 0.69; 95 % confidence interval, 0.5–0.9; P = 0.008). These findings suggest that long-term maintenance of a music-based multitask exercise program is a promising strategy to prevent age-related physical decline in older adults. They also highlight the efficacy of sustained long-term adherence to exercise for falls prevention. PMID:25148876
Person re-identification over camera networks using multi-task distance metric learning.

PubMed

Ma, Lianyang; Yang, Xiaokang; Tao, Dacheng

2014-08-01

Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.
Automation bias and verification complexity: a systematic review.

PubMed

Lyell, David; Coiera, Enrico

2017-03-01

While potentially reducing decision errors, decision support systems can introduce new types of errors. Automation bias (AB) happens when users become overreliant on decision support, which reduces vigilance in information seeking and processing. Most research originates from the human factors literature, where the prevailing view is that AB occurs only in multitasking environments. This review seeks to compare the human factors and health care literature, focusing on the apparent association of AB with multitasking and task complexity. EMBASE, Medline, Compendex, Inspec, IEEE Xplore, Scopus, Web of Science, PsycINFO, and Business Source Premiere from 1983 to 2015. Evaluation studies where task execution was assisted by automation and resulted in errors were included. Participants needed to be able to verify automation correctness and perform the task manually. Tasks were identified and grouped. Task and automation type and presence of multitasking were noted. Each task was rated for its verification complexity. Of 890 papers identified, 40 met the inclusion criteria; 6 were in health care. Contrary to the prevailing human factors view, AB was found in single tasks, typically involving diagnosis rather than monitoring, and with high verification complexity. The literature is fragmented, with large discrepancies in how AB is reported. Few studies reported the statistical significance of AB compared to a control condition. AB appears to be associated with the degree of cognitive load experienced in decision tasks, and appears to not be uniquely associated with multitasking. Strategies to minimize AB might focus on cognitive load reduction. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Effect of music-based multitask training on cognition and mood in older adults.

PubMed

Hars, Mélany; Herrmann, Francois R; Gold, Gabriel; Rizzoli, René; Trombetti, Andrea

2014-03-01

in a secondary analysis of a randomised controlled trial, we investigated whether 6 months of music-based multitask training had beneficial effects on cognitive functioning and mood in older adults. 134 community-dwellers aged ≥65 years at increased risk for falling were randomly assigned to either an intervention group (n = 66) who attended once weekly 1-h supervised group classes of multitask exercises, executed to the rhythm of piano music, or a control group with delayed intervention (n = 68) who maintained usual lifestyle habits, for 6 months. A short neuropsychological test battery was administered by an intervention-blinded neuropsychologist at baseline and Month 6, including the mini-mental state examination (MMSE), the clock-drawing test, the frontal assessment battery (FAB) and the hospital anxiety (HADS-A) and depression scale. intention-to-treat analysis showed an improvement in the sensitivity to interference subtest of the FAB (adjusted between-group mean difference (AMD), 0.12; 95% CI, 0.00 to 0.25; P = 0.047) and a reduction in anxiety level (HADS-A; AMD, -0.88; 95% CI, -1.73 to -0.05; P = 0.039) in intervention participants, as compared with the controls. Within-group analysis revealed an increase in MMSE score (P = 0.004) and a reduction in the number of participants with impaired global cognitive performance (i.e., MMSE score ≤23; P = 0.003) with intervention. six months of once weekly music-based multitask training was associated with improved cognitive function and decreased anxiety in community-dwelling older adults, compared with non-exercising controls. Studies designed to further delineate whether training-induced changes in cognitive function could contribute to dual-task gait improvements and falls reduction, remain to be conducted.
Gaze training enhances laparoscopic technical skill acquisition and multi-tasking performance: a randomized, controlled study.

PubMed

Wilson, Mark R; Vine, Samuel J; Bright, Elizabeth; Masters, Rich S W; Defriend, David; McGrath, John S

2011-12-01

The operating room environment is replete with stressors and distractions that increase the attention demands of what are already complex psychomotor procedures. Contemporary research in other fields (e.g., sport) has revealed that gaze training interventions may support the development of robust movement skills. This current study was designed to examine the utility of gaze training for technical laparoscopic skills and to test performance under multitasking conditions. Thirty medical trainees with no laparoscopic experience were divided randomly into one of three treatment groups: gaze trained (GAZE), movement trained (MOVE), and discovery learning/control (DISCOVERY). Participants were fitted with a Mobile Eye gaze registration system, which measures eye-line of gaze at 25 Hz. Training consisted of ten repetitions of the "eye-hand coordination" task from the LAP Mentor VR laparoscopic surgical simulator while receiving instruction and video feedback (specific to each treatment condition). After training, all participants completed a control test (designed to assess learning) and a multitasking transfer test, in which they completed the procedure while performing a concurrent tone counting task. Not only did the GAZE group learn more quickly than the MOVE and DISCOVERY groups (faster completion times in the control test), but the performance difference was even more pronounced when multitasking. Differences in gaze control (target locking fixations), rather than tool movement measures (tool path length), underpinned this performance advantage for GAZE training. These results suggest that although the GAZE intervention focused on training gaze behavior only, there were indirect benefits for movement behaviors and performance efficiency. Additionally, focusing on a single external target when learning, rather than on complex movement patterns, may have freed-up attentional resources that could be applied to concurrent cognitive tasks.
Vectorized and multitasked solution of the few-group neutron diffusion equations

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

Zee, S.K.; Turinsky, P.J.; Shayer, Z.

1989-03-01

A numerical algorithm with parallelism was used to solve the two-group, multidimensional neutron diffusion equations on computers characterized by shared memory, vector pipeline, and multi-CPU architecture features. Specifically, solutions were obtained on the Cray X/MP-48, the IBM-3090 with vector facilities, and the FPS-164. The material-centered mesh finite difference method approximation and outer-inner iteration method were employed. Parallelism was introduced in the inner iterations using the cyclic line successive overrelaxation iterative method and solving in parallel across lines. The outer iterations were completed using the Chebyshev semi-iterative method that allows parallelism to be introduced in both space and energy groups. Formore » the three-dimensional model, power, soluble boron, and transient fission product feedbacks were included. Concentrating on the pressurized water reactor (PWR), the thermal-hydraulic calculation of moderator density assumed single-phase flow and a closed flow channel, allowing parallelism to be introduced in the solution across the radial plane. Using a pinwise detail, quarter-core model of a typical PWR in cycle 1, for the two-dimensional model without feedback the measured million floating point operations per second (MFLOPS)/vector speedups were 83/11.7. 18/2.2, and 2.4/5.6 on the Cray, IBM, and FPS without multitasking, respectively. Lower performance was observed with a coarser mesh, i.e., shorter vector length, due to vector pipeline start-up. For an 18 x 18 x 30 (x-y-z) three-dimensional model with feedback of the same core, MFLOPS/vector speedups of --61/6.7 and an execution time of 0.8 CPU seconds on the Cray without multitasking were measured. Finally, using two CPUs and the vector pipelines of the Cray, a multitasking efficiency of 81% was noted for the three-dimensional model.« less
Effect of music-based multitask training on gait, balance, and fall risk in elderly people: a randomized controlled trial.

PubMed

Trombetti, Andrea; Hars, Mélany; Herrmann, François R; Kressig, Reto W; Ferrari, Serge; Rizzoli, René

2011-03-28

Falls occur mainly while walking or performing concurrent tasks. We determined whether a music-based multitask exercise program improves gait and balance and reduces fall risk in elderly individuals. We conducted a 12-month randomized controlled trial involving 134 community-dwelling individuals older than 65 years, who are at increased risk of falling. They were randomly assigned to an intervention group (n = 66) or a delayed intervention control group scheduled to start the program 6 months later (n = 68). The intervention was a 6-month multitask exercise program performed to the rhythm of piano music. Change in gait variability under dual-task condition from baseline to 6 months was the primary end point. Secondary outcomes included changes in balance, functional performances, and fall risk. At 6 months, there was a reduction in stride length variability (adjusted mean difference, -1.4%; P < .002) under dual-task condition in the intervention group, compared with the delayed intervention control group. Balance and functional tests improved compared with the control group. There were fewer falls in the intervention group (incidence rate ratio, 0.46; 95% confidence interval, 0.27-0.79) and a lower risk of falling (relative risk, 0.61; 95% confidence interval, 0.39-0.96). Similar changes occurred in the delayed intervention control group during the second 6-month period with intervention. The benefit of the intervention on gait variability persisted 6 months later. In community-dwelling older people at increased risk of falling, a 6-month music-based multitask exercise program improved gait under dual-task condition, improved balance, and reduced both the rate of falls and the risk of falling. Trial Registration clinicaltrials.gov Identifier: NCT01107288.
Balance training with multi-task exercises improves fall-related self-efficacy, gait, balance performance and physical function in older adults with osteoporosis: a randomized controlled trial.

PubMed

Halvarsson, Alexandra; Franzén, Erika; Ståhle, Agneta

2015-04-01

To evaluate the effects of a balance training program including dual- and multi-task exercises on fall-related self-efficacy, fear of falling, gait and balance performance, and physical function in older adults with osteoporosis with an increased risk of falling and to evaluate whether additional physical activity would further improve the effects. Randomized controlled trial, including three groups: two intervention groups (Training, or Training+Physical activity) and one Control group, with a 12-week follow-up. Stockholm County, Sweden. Ninety-six older adults, aged 66-87, with verified osteoporosis. A specific and progressive balance training program including dual- and multi-task three times/week for 12 weeks, and physical activity for 30 minutes, three times/week. Fall-related self-efficacy (Falls Efficacy Scale-International), fear of falling (single-item question - 'In general, are you afraid of falling?'), gait speed with and without a cognitive dual-task at preferred pace and fast walking (GAITRite®), balance performance tests (one-leg stance, and modified figure of eight), and physical function (Late-Life Function and Disability Instrument). Both intervention groups significantly improved their fall-related self-efficacy as compared to the controls (p ≤ 0.034, 4 points) and improved their balance performance. Significant differences over time and between groups in favour of the intervention groups were found for walking speed with a dual-task (p=0.003), at fast walking speed (p=0.008), and for advanced lower extremity physical function (p=0.034). This balance training program, including dual- and multi-task, improves fall-related self-efficacy, gait speed, balance performance, and physical function in older adults with osteoporosis. © The Author(s) 2014.
Modality-specific, multitask locomotor deficits persist despite good recovery after a traumatic brain injury.

PubMed

McFadyen, Bradford J; Cantin, Jean-François; Swaine, Bonnie; Duchesneau, Guylaine; Doyon, Julien; Dumas, Denyse; Fait, Philippe

2009-09-01

To study the effects of sensory modality of simultaneous tasks during walking with and without obstacles after moderate to severe traumatic brain injury (TBI). Group comparison study. Gait analysis laboratory within a postacute rehabilitation facility. Volunteer sample (N=18). Persons with moderate to severe TBI (n=11) (9 men, 3 women; age, 37.56+/-13.79 y) and a comparison group (n=7) of subjects without neurologic problems matched on average for body mass index and age (4 men, 3 women; age, 39.19+/-17.35 y). Not applicable. Magnitudes and variability for walking speeds, foot clearance margins (ratio of foot clearance distance to obstacle height), and response reaction times (both direct and as a relative cost because of obstacle avoidance). The TBI group had well-recovered walking speeds and a general ability to avoid obstacles. However, these subjects did show lower trail limb toe clearances (P=.003) across all conditions. Response reaction times to the Stroop tasks were longer in general for the TBI group (P=.017), and this group showed significant increases in response reaction times for the visual modality within the more challenging obstacle avoidance task that was not observed for control subjects. A measure of multitask costs related to differences in response reaction times between obstructed and unobstructed trials also only showed increased attention costs for the visual over the auditory stimuli for the TBI group (P=.002). Mobility is a complex construct, and the present results provide preliminary findings that, even after good locomotor recovery, subjects with moderate to severe TBI show residual locomotor deficits in multitasking. Furthermore, our results suggest that sensory modality is important, and greater multitask costs occur during sensory competition (ie, visual interference).
Differences in prefrontal blood oxygenation during an acute multitasking stressor in ecstasy polydrug users.

PubMed

Roberts, C A; Wetherell, M A; Fisk, J E; Montgomery, C

2015-01-01

Cognitive deficits are well documented in ecstasy (3,4-methylenedioxymethamphetamine; MDMA) users, with such deficits being taken as evidence of dysregulation of the serotonin (5-hydroxytryptamine; 5-HT) system. More recently neuroimaging has been used to corroborate these deficits. The present study aimed to assess multitasking performance in ecstasy polydrug users, polydrug users and drug-naive individuals. It was predicted that ecstasy polydrug users would perform worse than non-users on the behavioural measure and this would be supported by differences in cortical blood oxygenation. In the study, 20 ecstasy-polydrug users, 17 polydrug users and 19 drug-naive individuals took part. On day 1, drug use history was taken and questionnaire measures were completed. On day 2, participants completed a 20-min multitasking stressor while brain blood oxygenation was measured using functional near infrared spectroscopy (fNIRS). There were no significant differences between the three groups on the subscales of the multitasking stressor. In addition, there were no significant differences on self-report measures of perceived workload (NASA Task Load Index). In terms of mood, ecstasy users were significantly less calm and less relaxed compared with drug-naive controls. There were also significant differences at three voxels on the fNIRS, indicating decreased blood oxygenation in ecstasy users compared with drug-naive controls at voxel 2 (left dorsolateral prefrontal cortex), voxel 14 and voxel 16 (right dorsolateral prefrontal cortex), and compared with polydrug controls at V14. The results of the present study provide support for changes in brain activation during performance of demanding tasks in ecstasy polydrug users, which could be related to cerebral vasoconstriction.
Situational characteristics of young adults' eating occasions: a real-time data collection using Personal Digital Assistants.

PubMed

Laska, Melissa Nelson; Graham, Dan; Moe, Stacey G; Lytle, Leslie; Fulkerson, Jayne

2011-03-01

To examine (i) situational characteristics of young adults' eating occasions, including away-from-home eating, social influences and multi-tasking, and (ii) how these characteristics are associated with specific foods/beverages consumed. Participants logged numerous characteristics of eating occasions (n 1237) in real time over 7 d. Minneapolis/St. Paul metropolitan area (Minnesota, USA). Forty-eight participants, aged 18-23 years. Half of all eating occasions (46 %) occurred alone, 26 % occurred while watching television and 36 % involved other multi-tasking. Most participants (63 %) did not think about their food choices in advance of eating occasions. Eating that occurred in the absence of television viewing and/or other multi-tasking was less likely to include sweetened beverages and more likely to include items like water, fruit, vegetables, cereal, grains and entrées. Eating occasions occurring alone, and/or those occurring at home, were more likely to include snack foods that required little preparation (e.g. cookies, baked goods) and less likely to include more traditional meal items (e.g. fruits, vegetables, entrée items). Overall, a large proportion of young adults' eating occasions occurred alone, while engaging in other activities and with little advanced planning. Although many young adults' eating occasions consist of a wide range of highly processed, energy-dense, convenience products, more traditional meal settings (i.e. eating at home with others in the absence of multi-tasking) may result in more structured mealtimes and better food choices, such as more fruits and vegetables. Effective behavioural strategies promoting positive eating patterns, including home meal preparation, are urgently needed among young adults.
Situational characteristics of young adults' eating occasions: a real-time data collection using Personal Digital Assistants

PubMed Central

Laska, Melissa Nelson; Graham, Dan; Moe, Stacey G.; Lytle, Leslie; Fulkerson, Jayne

2012-01-01

Objective To examine: (a) situational characteristics of young adult eating occasions, including away-from-home eating, social influences, and multitasking, and (b) how these characteristics are associated with specific foods/beverages consumed. Design Participants logged numerous characteristics of eating occasions (n=1237) in real-time over 7 days. Setting Minneapolis/St. Paul metropolitan area (Minnesota, USA) Subjects Forty-eight participants, ages 18–3 years Results Half of all eating occasions (46%) occurred alone, 26% occurred while watching television and 36% involved other multitasking. Most participants (63%) did not think about their food choices in advance of eating occasions. Eating that occurred in the absence of television viewing and/or other multi-tasking was less likely to include sweetened beverages, and more likely to include items like water, fruit, vegetables, cereal, grains and entrées. Eating occasions occurring and/or those occurring at home, were more likely to include snack foods that required little preparation (e.g., cookies, baked goods), and less likely to include more traditional meal items (e.g., fruits, vegetables, entrée items). Conclusion Overall, a large proportion of young adult eating occasions occurred alone, while engaging in other activities and with little advanced planning. Although many young adult eating occasions consist of a wide range of highly processed, energy-dense, convenience products, more traditional meal settings (i.e., eating at home with others in the absence of multi-tasking) may result in more structured mealtimes and better food choices, such as more fruits and vegetables. Effective behavioral strategies promoting positive eating patterns, including home meal preparation, among young adults are urgently needed. PMID:21138611
Cognitive-Motor Interference in an Ecologically Valid Street Crossing Scenario.

PubMed

Janouch, Christin; Drescher, Uwe; Wechsler, Konstantin; Haeger, Mathias; Bock, Otmar; Voelcker-Rehage, Claudia

2018-01-01

Laboratory-based research revealed that gait involves higher cognitive processes, leading to performance impairments when executed with a concurrent loading task. Deficits are especially pronounced in older adults. Theoretical approaches like the multiple resource model highlight the role of task similarity and associated attention distribution problems. It has been shown that in cases where these distribution problems are perceived relevant to participant's risk of falls, older adults prioritize gait and posture over the concurrent loading task. Here we investigate whether findings on task similarity and task prioritization can be transferred to an ecologically valid scenario. Sixty-three younger adults (20-30 years of age) and 61 older adults (65-75 years of age) participated in a virtual street crossing simulation. The participants' task was to identify suitable gaps that would allow them to cross a simulated two way street safely. Therefore, participants walked on a manual treadmill that transferred their forward motion to forward displacements in a virtual city. The task was presented as a single task (crossing only) and as a multitask. In the multitask condition participants were asked, among others, to type in three digit numbers that were presented either visually or auditorily. We found that for both age groups, street crossing as well as typing performance suffered under multitasking conditions. Impairments were especially pronounced for older adults (e.g., longer crossing initiation phase, more missed opportunities). However, younger and older adults did not differ in the speed and success rate of crossing. Further, deficits were stronger in the visual compared to the auditory task modality for most parameters. Our findings conform to earlier studies that found an age-related decline in multitasking performance in less realistic scenarios. However, task similarity effects were inconsistent and question the validity of the multiple resource model within ecologically valid scenarios.
Position-aware deep multi-task learning for drug-drug interaction extraction.

PubMed

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.
Use of anomolous thermal imaging effects for multi-mode systems control during crystal growth

NASA Technical Reports Server (NTRS)

Wargo, Michael J.

1989-01-01

Real time image processing techniques, combined with multitasking computational capabilities are used to establish thermal imaging as a multimode sensor for systems control during crystal growth. Whereas certain regions of the high temperature scene are presently unusable for quantitative determination of temperature, the anomalous information thus obtained is found to serve as a potentially low noise source of other important systems control output. Using this approach, the light emission/reflection characteristics of the crystal, meniscus and melt system are used to infer the crystal diameter and a linear regression algorithm is employed to determine the local diameter trend. This data is utilized as input for closed loop control of crystal shape. No performance penalty in thermal imaging speed is paid for this added functionality. Approach to secondary (diameter) sensor design and systems control structure is discussed. Preliminary experimental results are presented.

[Quality of life in Latin American immigrant caregivers in Spain].

PubMed

Bover, Andreu; Taltavull, Joana Maria; Gastaldo, Denise; Luengo, Raquel; Izquierdo, María Dolores; Juando-Prats, Clara; Sáenz de Ormijana, Amaia; Robledo, Juana

2015-01-01

To describe perceived quality of life in Latin American caregivers working in Spain and how it varies in relation to certain variables shared by this group. We used the SF-36 to measure perceived quality of life in 517 women residing in five Spanish regions: the Balearic Islands, Catalonia, the Basque Country, the Canary Islands, and Madrid. Several variables related to the socio-demographic profile and migration process were studied using Student's t test, ANOVA and linear regression models. The participants scored very low on the dimensions of physical and emotional roles. The factors associated with lower quality of life scores within the group were working as a live-in caregiver, lack of contract, multitasking, irregular status, and younger age. The vulnerability of these women can be explained by poor working conditions and other factors related to the migratory process. Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.
Classification of mislabelled microarrays using robust sparse logistic regression.

PubMed

Bootkrajang, Jakramate; Kabán, Ata

2013-04-01

Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Kunkun, E-mail: ktg@illinois.edu; Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence; Congedo, Pietro M.

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable formore » real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.« less
Forest/non-forest mapping using inventory data and satellite imagery

Treesearch

Ronald E. McRoberts

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and two prediction techniques, logistic regression and a k-Nearest Neighbours technique. The maps were used to increase the precision of forest area estimates by...
The effects of forest fragmentation on forest stand attributes

Treesearch

Ronald E. McRoberts; Greg C. Liknes

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and a logistic regression model. The maps were used to estimate quantitative indices of forest fragmentation. Correlations between the values of the indices and...
A wavelet approach to binary blackholes with asynchronous multitasking

NASA Astrophysics Data System (ADS)

Lim, Hyun; Hirschmann, Eric; Neilsen, David; Anderson, Matthew; Debuhr, Jackson; Zhang, Bo

2016-03-01

Highly accurate simulations of binary black holes and neutron stars are needed to address a variety of interesting problems in relativistic astrophysics. We present a new method for the solving the Einstein equations (BSSN formulation) using iterated interpolating wavelets. Wavelet coefficients provide a direct measure of the local approximation error for the solution and place collocation points that naturally adapt to features of the solution. Further, they exhibit exponential convergence on unevenly spaced collection points. The parallel implementation of the wavelet simulation framework presented here deviates from conventional practice in combining multi-threading with a form of message-driven computation sometimes referred to as asynchronous multitasking.
Parallel performance of TORT on the CRAY J90: Model and measurement

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

Barnett, A.; Azmy, Y.Y.

1997-10-01

A limitation on the parallel performance of TORT on the CRAY J90 is the amount of extra work introduced by the multitasking algorithm itself. The extra work beyond that of the serial version of the code, called overhead, arises from the synchronization of the parallel tasks and the accumulation of results by the master task. The goal of recent updates to TORT was to reduce the time consumed by these activities. To help understand which components of the multitasking algorithm contribute significantly to the overhead, a parallel performance model was constructed and compared to measurements of actual timings of themore » code.« less
User-friendly program for multitask analysis

NASA Astrophysics Data System (ADS)

Caporali, Sergio A.; Akladios, Magdy; Becker, Paul E.

2000-10-01

Research on lifting activities has led to the design of several useful tools for evaluating tasks that involve lifting and material handling. The National Institute for Occupational Safety and Health (NIOSH) has developed a single task lifting equation. This formula has been frequently used as a guide in the field of ergonomics and material handling. While being much more complicated, the multi-task formula will provide a more realistic analysis for the evaluation of lifting and material handling jobs. A user friendly tool has been developed to assist professionals in the field of ergonomics in analyzing multitask types of material handling jobs. The program allows for up to 10 different tasks to be evaluated. The program requires a basic understanding of the NIOSH lifting guidelines and the six multipliers that are involved in the analysis of each single task. These multipliers are: Horizontal Distance Multiplier (HM), Vertical Distance Multiplier (VM), Vertical Displacement Multiplier (DM), Frequency of lifting Multiplier (FM), Coupling Multiplier (CM), and the Asymmetry Multiplier (AM). Once a given job is analyzed, a researched list of recommendations is provided to the user in an attempt to reduce the potential risk factors that are associated with each task.
Task Integration Facilitates Multitasking.

PubMed

de Oliveira, Rita F; Raab, Markus; Hegele, Mathias; Schorer, Jörg

2017-01-01

The aim of this study was to investigate multi-task integration in a continuous tracking task. We were particularly interested in how manipulating task structure in a dual-task situation affects learning of a constant segment embedded in a pursuit-tracking task. Importantly, we examined if dual-task effects could be attributed to task integration by varying the structural similarity and difficulty of the primary and secondary tasks. In Experiment 1 participants performed a pursuit tracking task while counting high-pitched tones and ignoring low-pitched tones. The tones were either presented randomly or structurally 250 ms before each tracking turn. Experiment 2 increased the motor load of the secondary tasks by asking participants to tap their feet to the tones. Experiment 3 further increased motor load of the primary task by increasing its speed and having participants tracking with their non-dominant hand. The results show that dual-task interference can be moderated by secondary task conditions that match the structure of the primary task. Therefore our results support proposals of task integration in continuous tracking paradigms. We conclude that multi-tasking is not always detrimental for motor learning but can be facilitated through task-integration.
Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors.

PubMed

Feng, S F; Schwemmer, M; Gershman, S J; Cohen, J D

2014-03-01

Why is it that behaviors that rely on control, so striking in their diversity and flexibility, are also subject to such striking limitations? Typically, people cannot engage in more than a few-and usually only a single-control-demanding task at a time. This limitation was a defining element in the earliest conceptualizations of controlled processing; it remains one of the most widely accepted axioms of cognitive psychology, and is even the basis for some laws (e.g., against the use of mobile devices while driving). Remarkably, however, the source of this limitation is still not understood. Here, we examine one potential source of this limitation, in terms of a trade-off between the flexibility and efficiency of representation ("multiplexing") and the simultaneous engagement of different processing pathways ("multitasking"). We show that even a modest amount of multiplexing rapidly introduces cross-talk among processing pathways, thereby constraining the number that can be productively engaged at once. We propose that, given the large number of advantages of efficient coding, the human brain has favored this over the capacity for multitasking of control-demanding processes.
Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.
Serial or overlapping processing in multitasking as individual preference: Effects of stimulus preview on task switching and concurrent dual-task performance.

PubMed

Reissland, Jessika; Manzey, Dietrich

2016-07-01

Understanding the mechanisms and performance consequences of multitasking has long been in focus of scientific interest, but has been investigated by three research lines more or less isolated from each other. Studies in the fields of the psychological refractory period, task switching, and interruptions have scored with a high experimental control, but usually do not give participants many degrees of freedom to self-organize the processing of two concurrent tasks. Individual strategies as well as their impact on efficiency have mainly been neglected. Self-organized multitasking has been investigated in the field of human factors, but primarily with respect to overall performance without detailed investigation of how the tasks are processed. The current work attempts to link aspects of these research lines. All of them, explicitly or implicitly, provide hints about an individually preferred type of task organization, either more cautious trying to work strictly serially on only one task at a time or more daring with a focus on task interleaving and, if possible, also partially overlapping (parallel) processing. In two experiments we investigated different strategies of task organization and their impact on efficiency using a new measure of overall multitasking efficiency. Experiment 1 was based on a classical task switching paradigm with two classification tasks, but provided one group of participants with a stimulus preview of the task to switch to next, enabling at least partial overlapping processing. Indeed, this preview led to a reduction of switch costs and to an increase of dual-task efficiency, but only for a subgroup of participants. They obviously exploited the possibility of overlapping processing, while the others worked mainly serially. While task-sequence was externally guided in the first experiment, Experiment 2 extended the approach by giving the participants full freedom of task organization in concurrent performance of the same tasks. Fine-grained analyses of response scheduling again revealed individual differences regarding the preference for strictly serial processing vs. some sort of task interleaving and overlapping processing. However, neither group showed a striking benefit in dual-task efficiency, although the results show that the costs of multitasking can partly be compensated by overlapping processing. Copyright © 2016 Elsevier B.V. All rights reserved.
Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox.

PubMed

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

2008-01-01

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

DOE PAGES

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

2016-11-22

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less
SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).
Influence plots for LASSO

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

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less
Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

PubMed

Annunziata, Roberto; Trucco, Emanuele

2016-11-01

Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time-consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm-start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.
Wavelet-promoted sparsity for non-invasive reconstruction of electrical activity of the heart.

PubMed

Cluitmans, Matthijs; Karel, Joël; Bonizzi, Pietro; Volders, Paul; Westra, Ronald; Peeters, Ralf

2018-05-12

We investigated a novel sparsity-based regularization method in the wavelet domain of the inverse problem of electrocardiography that aims at preserving the spatiotemporal characteristics of heart-surface potentials. In three normal, anesthetized dogs, electrodes were implanted around the epicardium and body-surface electrodes were attached to the torso. Potential recordings were obtained simultaneously on the body surface and on the epicardium. A CT scan was used to digitize a homogeneous geometry which consisted of the body-surface electrodes and the epicardial surface. A novel multitask elastic-net-based method was introduced to regularize the ill-posed inverse problem. The method simultaneously pursues a sparse wavelet representation in time-frequency and exploits correlations in space. Performance was assessed in terms of quality of reconstructed epicardial potentials, estimated activation and recovery time, and estimated locations of pacing, and compared with performance of Tikhonov zeroth-order regularization. Results in the wavelet domain obtained higher sparsity than those in the time domain. Epicardial potentials were non-invasively reconstructed with higher accuracy than with Tikhonov zeroth-order regularization (p < 0.05), and recovery times were improved (p < 0.05). No significant improvement was found in terms of activation times and localization of origin of pacing. Next to improved estimation of recovery isochrones, which is important when assessing substrate for cardiac arrhythmias, this novel technique opens potentially powerful opportunities for clinical application, by allowing to choose wavelet bases that are optimized for specific clinical questions. Graphical Abstract The inverse problem of electrocardiography is to reconstruct heart-surface potentials from recorded bodysurface electrocardiograms (ECGs) and a torso-heart geometry. However, it is ill-posed and solving it requires additional constraints for regularization. We introduce a regularization method that simultaneously pursues a sparse wavelet representation in time-frequency and exploits correlations in space. Our approach reconstructs epicardial (heart-surface) potentials with higher accuracy than common methods. It also improves the reconstruction of recovery isochrones, which is important when assessing substrate for cardiac arrhythmias. This novel technique opens potentially powerful opportunities for clinical application, by allowing to choose wavelet bases that are optimized for specific clinical questions.
Sparse and stable Markowitz portfolios.

PubMed

Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

2009-07-28

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.
Modeling the human as a controller in a multitask environment

NASA Technical Reports Server (NTRS)

Govindaraj, T.; Rouse, W. B.

1978-01-01

Modeling the human as a controller of slowly responding systems with preview is considered. Along with control tasks, discrete noncontrol tasks occur at irregular intervals. In multitask situations such as these, it has been observed that humans tend to apply piecewise constant controls. It is believed that the magnitude of controls and the durations for which they remain constant are dependent directly on the system bandwidth, preview distance, complexity of the trajectory to be followed, and nature of the noncontrol tasks. A simple heuristic model of human control behavior in this situation is presented. The results of a simulation study, whose purpose was determination of the sensitivity of the model to its parameters, are discussed.

Sox2: A multitasking networker

PubMed Central

Reiprich, Simone; Wegner, Michael

2014-01-01

The transcription factor Sox2 is best known as a pluripotency factor in stem and precursor cells and its expression generally correlates with an undifferentiated state. Proposed modes of action include those as classical transcription factor and pre-patterning factor with influence on histone modifications and chromatin structure. Recently, we provided the first detailed analysis of Sox2 expression and function during development of oligodendrocytes, the myelin-forming cells of the CNS. Surprisingly, we found evidence for a role of Sox2 as differentiation factor and found it to act through modulation of microRNA levels. Thus, we add new facets to the functional repertoire of Sox2 and throw light on the networking activity of this multitasking developmental regulator. PMID:27502481
Asynchronous, macrotasked relaxation strategies for the solution of viscous, hypersonic flows

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.

1991-01-01

A point-implicit, asynchronous macrotasked relaxation of the steady, thin-layer, Navier-Stokes equations is presented. The method employs multidirectional, single-level storage Gauss-Seidel relaxation sweeps, which effectively communicate perturbations across the entire domain in 2n sweeps, where n is the dimension of the domain. In order to enhance convergence the application of relaxation factors to specific components of the Jacobian is examined using a stability analysis of the advection and diffusion equations. Attention is also given to the complications associated with asynchronous multitasking. Solutions are generated for hypersonic flows over blunt bodies in two and three dimensions with chemical reactions, utilizing single-tasked and multitasked relaxation strategies.
Online sparse Gaussian process based human motion intent learning for an electrically actuated lower extremity exoskeleton.

PubMed

Long, Yi; Du, Zhi-Jiang; Chen, Chao-Feng; Dong, Wei; Wang, Wei-Dong

2017-07-01

The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems. GP regression is restrictive for large data sets due to its computation complexity. In this paper, an online sparse GP algorithm is constructed to learn the HMI. The original training dataset is collected when the user wears the exoskeleton system with friction compensation to perform unconstrained movement as far as possible. The dataset has two kinds of data, i.e., (1) physical HRI, which is collected by torque sensors placed at the interaction cuffs for the active joints, i.e., knee joints; (2) joint angular position, which is measured by optical position sensors. To reduce the computation complexity of GP, grey relational analysis (GRA) is utilized to specify the original dataset and provide the final training dataset. Those hyper-parameters are optimized offline by maximizing marginal likelihood and will be applied into online GP regression algorithm. The HMI, i.e., angular position of human joints, will be regarded as the reference trajectory for the mechanical legs. To verify the effectiveness of the proposed algorithm, experiments are performed on a subject at a natural speed. The experimental results show the HMI can be obtained in real time, which can be extended and employed in the similar exoskeleton systems.
Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

NASA Astrophysics Data System (ADS)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.
Statistical inference methods for sparse biological time series data.

PubMed

Ndukum, Juliet; Fonseca, Luís L; Santos, Helena; Voit, Eberhard O; Datta, Susmita

2011-04-25

Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.
A new surrogate modeling technique combining Kriging and polynomial chaos expansions – Application to uncertainty analysis in computational dosimetry

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

Kersaudy, Pierric, E-mail: pierric.kersaudy@orange.com; Whist Lab, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux; ESYCOM, Université Paris-Est Marne-la-Vallée, 5 boulevard Descartes, 77700 Marne-la-Vallée

2015-04-01

In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representationmore » of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.« less
Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-02-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.
Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-05-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.
A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

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

Liu, Wenyang; Cheung, Yam; Sawant, Amit

2016-05-15

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparsemore » regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.« less
Association of childhood abuse with homeless women's social networks.

PubMed

Green, Harold D; Tucker, Joan S; Wenzel, Suzanne L; Golinelli, Daniela; Kennedy, David P; Ryan, Gery W; Zhou, Annie J

2012-01-01

Childhood abuse has been linked to negative sequelae for women later in life including drug and alcohol use and violence as victim or perpetrator and may also affect the development of women's social networks. Childhood abuse is prevalent among at-risk populations of women (such as the homeless) and thus may have a stronger impact on their social networks. We conducted a study to: (a) develop a typology of sheltered homeless women's social networks; (b) determine whether childhood abuse was associated with the social networks of sheltered homeless women; and (c) determine whether those associations remained after accounting for past-year substance abuse and recent intimate partner abuse. A probability sample of 428 homeless women from temporary shelter settings in Los Angeles County completed a personal network survey that provided respondent information as well as information about their network members' demographics and level of interaction with each other. Cluster analyses identified groups of women who shared specific social network characteristics. Multinomial logistic regressions revealed variables associated with group membership. We identified three groups of women with differing social network characteristics: low-risk networks, densely connected risky networks (dense, risky), and sparsely connected risky networks (sparse, risky). Multinomial logistic regressions indicated that membership in the sparse, risky network group, when compared to the low-risk group, was associated with history of childhood physical abuse (but not sexual or emotional abuse). Recent drug abuse was associated with membership in both risky network groups; however, the association of childhood physical abuse with sparse, risky network group membership remained. Although these findings support theories proposing that the experience of childhood abuse can shape women's social networks, they suggest that it may be childhood physical abuse that has the most impact among homeless women. The effects of childhood physical abuse should be more actively investigated in clinical settings, especially those frequented by homeless women, particularly with respect to the formation of social networks in social contexts that may expose these women to greater risks. Copyright © 2012. Published by Elsevier Ltd.
Multiprocessing MCNP on an IBM RS/6000 cluster

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

McKinney, G.W.; West, J.T.

1993-01-01

The advent of high-performance computer systems has brought to maturity programming concepts like vectorization, multiprocessing, and multitasking. While there are many schools of thought as to the most significant factor in obtaining order-of-magnitude increases in performance, such speedup can only be achieved by integrating the computer system and application code. Vectorization leads to faster manipulation of arrays by overlapping instruction CPU cycles. Discrete ordinates codes, which require the solving of large matrices, have proved to be major benefactors of vectorization. Monte Carlo transport, on the other hand, typically contains numerous logic statements and requires extensive redevelopment to benefit from vectorization.more » Multiprocessing and multitasking provide additional CPU cycles via multiple processors. Such systems are generally designed with either common memory access (multitasking) or distributed memory access. In both cases, theoretical speedup, as a function of the number of processors (P) and the fraction of task time that multiprocesses (f), can be formulated using Amdahl's Law S ((f,P) = 1 f + f/P). However, for most applications this theoretical limit cannot be achieved, due to additional terms not included in Amdahl's Law. Monte Carlo transport is a natural candidate for multiprocessing, since the particle tracks are generally independent and the precision of the result increases as the square root of the number of particles tracked.« less
AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.

PubMed

Ma, Jianzhu; Wang, Sheng

2015-01-01

The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.
Psychobiological responses to critically evaluated multitasking.

PubMed

Wetherell, Mark A; Craw, Olivia; Smith, Kenny; Smith, Michael A

2017-12-01

In order to understand psychobiological responses to stress it is necessary to observe how people react to controlled stressors. A range of stressors exist for this purpose; however, laboratory stressors that are representative of real life situations provide more ecologically valid opportunities for assessing stress responding. The current study assessed psychobiological responses to an ecologically valid laboratory stressor involving multitasking and critical evaluation. The stressor elicited significant increases in psychological and cardiovascular stress reactivity; however, no cortisol reactivity was observed. Other socially evaluative laboratory stressors that lead to cortisol reactivity typically require a participant to perform tasks that involve verbal responses, whilst standing in front of evaluative others. The current protocol contained critical evaluation of cognitive performance; however, this was delivered from behind a seated participant. The salience of social evaluation may therefore be related to the response format of the task and the method of evaluation. That is, the current protocol did not involve the additional vulnerability associated with in person, face-to-face contact, and verbal delivery. Critical evaluation of multitasking provides an ecologically valid technique for inducing laboratory stress and provides an alternative tool for assessing psychological and cardiovascular reactivity. Future studies could additionally use this paradigm to investigate those components of social evaluation necessary for eliciting a cortisol response.
AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model

PubMed Central

Ma, Jianzhu; Wang, Sheng

2015-01-01

Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods. PMID:26339631
Cognitive—Motor Interference in an Ecologically Valid Street Crossing Scenario

PubMed Central

Janouch, Christin; Drescher, Uwe; Wechsler, Konstantin; Haeger, Mathias; Bock, Otmar; Voelcker-Rehage, Claudia

2018-01-01

Laboratory-based research revealed that gait involves higher cognitive processes, leading to performance impairments when executed with a concurrent loading task. Deficits are especially pronounced in older adults. Theoretical approaches like the multiple resource model highlight the role of task similarity and associated attention distribution problems. It has been shown that in cases where these distribution problems are perceived relevant to participant's risk of falls, older adults prioritize gait and posture over the concurrent loading task. Here we investigate whether findings on task similarity and task prioritization can be transferred to an ecologically valid scenario. Sixty-three younger adults (20–30 years of age) and 61 older adults (65–75 years of age) participated in a virtual street crossing simulation. The participants' task was to identify suitable gaps that would allow them to cross a simulated two way street safely. Therefore, participants walked on a manual treadmill that transferred their forward motion to forward displacements in a virtual city. The task was presented as a single task (crossing only) and as a multitask. In the multitask condition participants were asked, among others, to type in three digit numbers that were presented either visually or auditorily. We found that for both age groups, street crossing as well as typing performance suffered under multitasking conditions. Impairments were especially pronounced for older adults (e.g., longer crossing initiation phase, more missed opportunities). However, younger and older adults did not differ in the speed and success rate of crossing. Further, deficits were stronger in the visual compared to the auditory task modality for most parameters. Our findings conform to earlier studies that found an age-related decline in multitasking performance in less realistic scenarios. However, task similarity effects were inconsistent and question the validity of the multiple resource model within ecologically valid scenarios. PMID:29774001
Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2006-10-01

Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffrey's prior. The improved algorithm (BLogReg) is then typically two or three orders of magnitude faster than the original algorithm, as there is no longer a need for a model selection step. The BLogReg algorithm is also free from selection bias in performance estimation, a common pitfall in the application of machine learning algorithms in cancer classification. The SLogReg, BLogReg and Relevance Vector Machine (RVM) gene selection algorithms are evaluated over the well-studied colon cancer and leukaemia benchmark datasets. The leave-one-out estimates of the probability of test error and cross-entropy of the BLogReg and SLogReg algorithms are very similar, however the BlogReg algorithm is found to be considerably faster than the original SLogReg algorithm. Using nested cross-validation to avoid selection bias, performance estimation for SLogReg on the leukaemia dataset takes almost 48 h, whereas the corresponding result for BLogReg is obtained in only 1 min 24 s, making BLogReg by far the more practical algorithm. BLogReg also demonstrates better estimates of conditional probability than the RVM, which are of great importance in medical applications, with similar computational expense. A MATLAB implementation of the sparse logistic regression algorithm with Bayesian regularization (BLogReg) is available from http://theoval.cmp.uea.ac.uk/~gcc/cbl/blogreg/
Interaction Models for Functional Regression.

PubMed

Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

2016-02-01

A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.
Sparse and stable Markowitz portfolios

PubMed Central

Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

2009-01-01

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio. PMID:19617537
Evaluation of generalized degrees of freedom for sparse estimation by replica method

NASA Astrophysics Data System (ADS)

Sakata, A.

2016-12-01

We develop a method to evaluate the generalized degrees of freedom (GDF) for linear regression with sparse regularization. The GDF is a key factor in model selection, and thus its evaluation is useful in many modelling applications. An analytical expression for the GDF is derived using the replica method in the large-system-size limit with random Gaussian predictors. The resulting formula has a universal form that is independent of the type of regularization, providing us with a simple interpretation. Within the framework of replica symmetric (RS) analysis, GDF has a physical meaning as the effective fraction of non-zero components. The validity of our method in the RS phase is supported by the consistency of our results with previous mathematical results. The analytical results in the RS phase are calculated numerically using the belief propagation algorithm.
Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Attention in a multi-task environment

NASA Technical Reports Server (NTRS)

Andre, Anthony D.; Heers, Susan T.

1993-01-01

Two experiments used a low fidelity multi-task simulation to investigate the effects of cue specificity on task preparation and performance. Subjects performed a continuous compensatory tracking task and were periodically prompted to perform one of several concurrent secondary tasks. The results provide strong evidence that subjects enacted a strategy to actively divert resources towards secondary task preparation only when they had specific information about an upcoming task to be performed. However, this strategy was not as much affected by the type of task cued (Experiment 1) or its difficulty level (Experiment 2). Overall, subjects seemed aware of both the costs (degraded primary task tracking) and benefits (improved secondary task performance) of cue information. Implications of the present results for computational human performance/workload models are discussed.
A multitasking finite state architecture for computer control of an electric powertrain

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

Burba, J.C.

1984-01-01

Finite state techniques provide a common design language between the control engineer and the computer engineer for event driven computer control systems. They simplify communication and provide a highly maintainable control system understandable by both. This paper describes the development of a control system for an electric vehicle powertrain utilizing finite state concepts. The basics of finite state automata are provided as a framework to discuss a unique multitasking software architecture developed for this application. The architecture employs conventional time-sliced techniques with task scheduling controlled by a finite state machine representation of the control strategy of the powertrain. The complexitiesmore » of excitation variable sampling in this environment are also considered.« less
Mood states determine the degree of task shielding in dual-task performance.

PubMed

Zwosta, Katharina; Hommel, Bernhard; Goschke, Thomas; Fischer, Rico

2013-01-01

Current models of multitasking assume that dual-task performance and the degree of multitasking are affected by cognitive control strategies. In particular, cognitive control is assumed to regulate the amount of shielding of the prioritised task from crosstalk from the secondary task. We investigated whether and how task shielding is influenced by mood states. Participants were exposed to two short film clips, one inducing high and one inducing low arousal, of either negative or positive content. Negative mood led to stronger shielding of the prioritised task (i.e., less crosstalk) than positive mood, irrespective of arousal. These findings support the assumption that emotional states determine the parameters of cognitive control and play an important role in regulating dual-task performance.
Neurovision processor for designing intelligent sensors

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1992-03-01

A programmable multi-task neuro-vision processor, called the Positive-Negative (PN) neural processor, is proposed as a plausible hardware mechanism for constructing robust multi-task vision sensors. The computational operations performed by the PN neural processor are loosely based on the neural activity fields exhibited by certain nervous tissue layers situated in the brain. The neuro-vision processor can be programmed to generate diverse dynamic behavior that may be used for spatio-temporal stabilization (STS), short-term visual memory (STVM), spatio-temporal filtering (STF) and pulse frequency modulation (PFM). A multi- functional vision sensor that performs a variety of information processing operations on time- varying two-dimensional sensory images can be constructed from a parallel and hierarchical structure of numerous individually programmed PN neural processors.
Classification of vegetation types in military region

NASA Astrophysics Data System (ADS)

Gonçalves, Miguel; Silva, Jose Silvestre; Bioucas-Dias, Jose

2015-10-01

In decision-making process regarding planning and execution of military operations, the terrain is a determining factor. Aerial photographs are a source of vital information for the success of an operation in hostile region, namely when the cartographic information behind enemy lines is scarce or non-existent. The objective of present work is the development of a tool capable of processing aerial photos. The methodology implemented starts with feature extraction, followed by the application of an automatic selector of features. The next step, using the k-fold cross validation technique, estimates the input parameters for the following classifiers: Sparse Multinomial Logist Regression (SMLR), K Nearest Neighbor (KNN), Linear Classifier using Principal Component Expansion on the Joint Data (PCLDC) and Multi-Class Support Vector Machine (MSVM). These classifiers were used in two different studies with distinct objectives: discrimination of vegetation's density and identification of vegetation's main components. It was found that the best classifier on the first approach is the Sparse Logistic Multinomial Regression (SMLR). On the second approach, the implemented methodology applied to high resolution images showed that the better performance was achieved by KNN classifier and PCLDC. Comparing the two approaches there is a multiscale issue, in which for different resolutions, the best solution to the problem requires different classifiers and the extraction of different features.
Online estimation of lithium-ion battery capacity using sparse Bayesian learning

NASA Astrophysics Data System (ADS)

Hu, Chao; Jain, Gaurav; Schmidt, Craig; Strief, Carrie; Sullivan, Melani

2015-09-01

Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the capacity of the battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the capacity of a Li-ion battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the battery capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online capacity estimation computationally efficient. 10 years' continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.
Robotic technology results in faster and more robust surgical skill acquisition than traditional laparoscopy.

PubMed

Moore, Lee J; Wilson, Mark R; Waine, Elizabeth; Masters, Rich S W; McGrath, John S; Vine, Samuel J

2015-03-01

Technical surgical skills are said to be acquired quicker on a robotic rather than laparoscopic platform. However, research examining this proposition is scarce. Thus, this study aimed to compare the performance and learning curves of novices acquiring skills using a robotic or laparoscopic system, and to examine if any learning advantages were maintained over time and transferred to more difficult and stressful tasks. Forty novice participants were randomly assigned to either a robotic- or laparoscopic-trained group. Following one baseline trial on a ball pick-and-drop task, participants performed 50 learning trials. Participants then completed an immediate retention trial and a transfer trial on a two-instrument rope-threading task. One month later, participants performed a delayed retention trial and a stressful multi-tasking trial. The results revealed that the robotic-trained group completed the ball pick-and-drop task more quickly and accurately than the laparoscopic-trained group across baseline, immediate retention, and delayed retention trials. Furthermore, the robotic-trained group displayed a shorter learning curve for accuracy. The robotic-trained group also performed the more complex rope-threading and stressful multi-tasking transfer trials better. Finally, in the multi-tasking trial, the robotic-trained group made fewer tone counting errors. The results highlight the benefits of using robotic technology for the acquisition of technical surgical skills.
Multiprocessing MCNP on an IBM RS/6000 cluster

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

McKinney, G.W.; West, J.T.

1993-03-01

The advent of high-performance computer systems has brought to maturity programming concepts like vectorization, multiprocessing, and multitasking. While there are many schools of thought as to the most significant factor in obtaining order-of-magnitude increases in performance, such speedup can only be achieved by integrating the computer system and application code. Vectorization leads to faster manipulation of arrays by overlapping instruction CPU cycles. Discrete ordinates codes, which require the solving of large matrices, have proved to be major benefactors of vectorization. Monte Carlo transport, on the other hand, typically contains numerous logic statements and requires extensive redevelopment to benefit from vectorization.more » Multiprocessing and multitasking provide additional CPU cycles via multiple processors. Such systems are generally designed with either common memory access (multitasking) or distributed memory access. In both cases, theoretical speedup, as a function of the number of processors (P) and the fraction of task time that multiprocesses (f), can be formulated using Amdahl`s Law S ((f,P) = 1 f + f/P). However, for most applications this theoretical limit cannot be achieved, due to additional terms not included in Amdahl`s Law. Monte Carlo transport is a natural candidate for multiprocessing, since the particle tracks are generally independent and the precision of the result increases as the square root of the number of particles tracked.« less
Understanding Emergency Medicine Physicians Multitasking Behaviors Around Interruptions.

PubMed

Fong, Allan; Ratwani, Raj M

2018-06-11

Interruptions can adversely impact human performance, particularly in fast-paced and high-risk environments such as the emergency department (ED). Understanding physician behaviors before, during, and after interruptions is important to the design and promotion of safe and effective workflow solutions. However, traditional human factors based interruption models do not accurately reflect the complexities of real-world environments like the ED and may not capture multiple interruptions and multitasking. We present a more comprehensive framework for understanding interruptions that is composed of three phases, each with multiple levels: Interruption Start Transition, Interruption Engagement, and Interruption End Transition. This three-phase framework is not constrained to discrete task transitions, providing a robust method to categorize multitasking behaviors around interruptions. We apply this framework in categorizing 457 interruption episodes. 457 interruption episodes were captured during 36 hours of observation. The interrupted task was immediately suspended 348 (76.1%) times. Participants engaged in new self-initiated tasks during the interrupting task 164 (35.9%) times and did not directly resume the interrupted task in 284 (62.1%) interruption episodes. Using this framework provides a more detailed description of the types of physician behaviors in complex environments. Understanding the different types of interruption and resumption patterns, which may have a different impact on performance, can support the design of interruption mitigation strategies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Effects of imperfect automation and individual differences on concurrent performance of military and robotics tasks in a simulated multitasking environment.

PubMed

Chen, J Y C; Terrence, P I

2009-08-01

This study investigated the performance and workload of the combined position of gunner and robotics operator in a simulated military multitasking environment. Specifically, the study investigated how aided target recognition (AiTR) capabilities for the gunnery task with imperfect reliability (false-alarm-prone vs. miss-prone) might affect the concurrent robotics and communication tasks. Additionally, the study examined whether performance was affected by individual differences in spatial ability and attentional control. Results showed that when the robotics task was simply monitoring the video, participants had the best performance in their gunnery and communication tasks and the lowest perceived workload, compared with the other robotics tasking conditions. There was a strong interaction between the type of AiTR unreliability and participants' perceived attentional control. Overall, for participants with higher perceived attentional control, false-alarm-prone alerts were more detrimental; for low attentional control participants, conversely, miss-prone automation was more harmful. Low spatial ability participants preferred visual cueing and high spatial ability participants favoured tactile cueing. Potential applications of the findings include personnel selection for robotics operation, robotics user interface designs and training development. The present results will provide further understanding of the interplays among automation reliability, multitasking performance and individual differences in military tasking environments. These results will also facilitate the implementation of robots in military settings and will provide useful data to military system designs.
Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.
Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham).

PubMed

Bukar, Ali M; Ugail, Hassan

2017-09-01

Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an active appearance model (AAM) to extract facial features from available images. An aging function is then modelled using sparse partial least squares regression (sPLS). Thereafter, the aging function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham's facial image that was taken when he was 21 months old to the ages of 6, 14, and 22 years. The algorithm presented in this study could potentially be used to enhance the search for missing people worldwide. © 2017 American Academy of Forensic Sciences.
Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

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

2016-08-01

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

PubMed

Di Meglio, Paola; Perera, Gayathri K; Nestle, Frank O

2011-12-23

The skin provides the first line defense of the human body against injury and infection. By integrating recent findings in cutaneous immunology with fundamental concepts of skin biology, we portray the skin as a multitasking organ ensuring body homeostasis. Crosstalk between the skin and its microbial environment is also highlighted as influencing the response to injury, infection, and autoimmunity. The importance of the skin immune network is emphasized by the identification of several skin-resident cell subsets, each with its unique functions. Lessons learned from targeted therapy in inflammatory skin conditions, such as psoriasis, provide further insights into skin immune function. Finally, we look at the skin as an interacting network of immune signaling pathways exemplified by the development of a disease interactome for psoriasis. Copyright © 2011 Elsevier Inc. All rights reserved.
Multitasking the three-dimensional transport code TORT on CRAY platforms

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

Azmy, Y.Y.; Barnett, D.A.; Burre, C.A.

1996-04-01

The multitasking options in the three-dimensional neutral particle transport code TORT originally implemented for Cray`s CTSS operating system are revived and extended to run on Cray Y/MP and C90 computers using the UNICOS operating system. These include two coarse-grained domain decompositions; across octants, and across directions within an octant, termed Octant Parallel (OP), and Direction Parallel (DP), respectively. Parallel performance of the DP is significantly enhanced by increasing the task grain size and reducing load imbalance via dynamic scheduling of the discrete angles among the participating tasks. Substantial Wall Clock speedup factors, approaching 4.5 using 8 tasks, have been measuredmore » in a time-sharing environment, and generally depend on the test problem specifications, number of tasks, and machine loading during execution.« less
Improving multi-tasking ability through action videogames.

PubMed

Chiappe, Dan; Conger, Mark; Liao, Janet; Caldwell, J Lynn; Vu, Kim-Phuong L

2013-03-01

The present study examined whether action videogames can improve multi-tasking in high workload environments. Two groups with no action videogame experience were pre-tested using the Multi-Attribute Task Battery (MATB). It consists of two primary tasks; tracking and fuel management, and two secondary tasks; systems monitoring and communication. One group served as a control group, while a second played action videogames a minimum of 5 h a week for 10 weeks. Both groups returned for a post-assessment on the MATB. We found the videogame treatment enhanced performance on secondary tasks, without interfering with the primary tasks. Our results demonstrate action videogames can increase people's ability to take on additional tasks by increasing attentional capacity. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.
A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

PubMed Central

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

2016-01-01

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

PubMed

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.
Classical Testing in Functional Linear Models

PubMed Central

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155
Consequences of boundary-spanning demands and resources for work-to-family conflict and perceived stress.

PubMed

Voydanoff, Patricia

2005-10-01

Using work-family border theory, this article examines relationships between boundary-spanning demands and resources and work-to-family conflict and perceived stress. The analysis uses data from 2,109 respondents from the 2002 National Study of the Changing Workforce. The demands that were positively related to work-to-family conflict and perceived stress were commuting time, bringing work home, job contacts at home, and work-family multitasking. Work-family multitasking partially explained the effects of bringing work home and job contacts at home on conflict and stress. For resources, time off for family responsibilities and a supportive work-family culture showed negative associations with conflict and stress. Work-to-family conflict partially mediated relationships between several demands and resources and perceived stress. Copyright (c) 2005 APA, all rights reserved.

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