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Sample records for adaptive dictionary learning

  1. Dual-dictionary learning based MR image reconstruction with self-adaptive dictionaries.

    PubMed

    Jiansen Li; Ying Song; Jun Zhao

    2015-01-01

    Dual-dictionary learning method utilizes two dictionaries at two different resolution levels, a high resolution dictionary trained with full-data training set, and a low resolution dictionary co-trained with corresponding undersampled dataset. This method effectively incorporates a priori knowledge of typical structures, specific features and local details, leading to its success in magnetic resonance (MR) image reconstruction from highly undersampled k-space data. In this paper, we improve this dual-dictionary learning method by using self-adaptive dictionaries. The two level dictionaries are updated correspondingly in the inner iteration after updating the reconstruction result to maintain their matching accuracy. Experimental results show that the proposed method can improve the reconstruction quality efficiently and enhance the robustness significantly.

  2. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification.

  3. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. PMID:26055435

  4. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    SciTech Connect

    Valiollahzadeh, S; Clark, J; Mawlawi, O

    2014-06-15

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

  5. Multivariate temporal dictionary learning for EEG.

    PubMed

    Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

    2013-04-30

    This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

  6. Task-driven dictionary learning.

    PubMed

    Mairal, Julien; Bach, Francis; Ponce, Jean

    2012-04-01

    Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations. PMID:21808090

  7. Linearized Kernel Dictionary Learning

    NASA Astrophysics Data System (ADS)

    Golts, Alona; Elad, Michael

    2016-06-01

    In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.

  8. Dictionary learning for stereo image representation.

    PubMed

    Tošić, Ivana; Frossard, Pascal

    2011-04-01

    One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However, the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits of dictionary learning for distributed scene representation and camera pose estimation.

  9. Trainlets: Dictionary Learning in High Dimensions

    NASA Astrophysics Data System (ADS)

    Sulam, Jeremias; Ophir, Boaz; Zibulevsky, Michael; Elad, Michael

    2016-06-01

    Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. Yet, these methods have traditionally been restricted to small dimensions mainly due to the computational constraints that the dictionary learning problem entails. In the context of image processing, this implies handling small image patches. In this work we show how to efficiently handle bigger dimensions and go beyond the small patches in sparsity-based signal and image processing methods. We build our approach based on a new cropped wavelet decomposition, which enables a multi-scale analysis with virtually no border effects. We then employ this as the base dictionary within a double sparsity model to enable the training of adaptive dictionaries. To cope with the increase of training data, while at the same time improving the training performance, we present an Online Sparse Dictionary Learning (OSDL) algorithm to train this model effectively, enabling it to handle millions of examples. This work shows that dictionary learning can be up-scaled to tackle a new level of signal dimensions, obtaining large adaptable atoms that we call trainlets.

  10. Learning Stable Multilevel Dictionaries for Sparse Representations.

    PubMed

    Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas

    2015-09-01

    Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the development of efficient, robust, and provably good dictionary learning algorithms. Algorithmic stability and generalizability are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries, which can efficiently model any test data similar to the training samples. In this paper, we propose an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically. The algorithm employs a 1-D subspace clustering procedure, the K-hyperline clustering, to learn a hierarchical dictionary with multiple levels. We also propose an information-theoretic scheme to estimate the number of atoms needed in each level of learning and develop an ensemble approach to learn robust dictionaries. Using the proposed dictionaries, the sparse code for novel test data can be computed using a low-complexity pursuit procedure. We demonstrate the stability and generalization characteristics of the proposed algorithm using simulations. We also evaluate the utility of the multilevel dictionaries in compressed recovery and subspace learning applications.

  11. Tensor Dictionary Learning for Positive Definite Matrices.

    PubMed

    Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikolaos

    2015-11-01

    Sparse models have proven to be extremely successful in image processing and computer vision. However, a majority of the effort has been focused on sparse representation of vectors and low-rank models for general matrices. The success of sparse modeling, along with popularity of region covariances, has inspired the development of sparse coding approaches for these positive definite descriptors. While in earlier work, the dictionary was formed from all, or a random subset of, the training signals, it is clearly advantageous to learn a concise dictionary from the entire training set. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between sparse coding and dictionary update stages, and different atom update methods are described. A discriminative version of the dictionary learning approach is also proposed, which simultaneously learns dictionaries for different classes in classification or clustering. Experimental results demonstrate the advantage of learning dictionaries from data both from reconstruction and classification viewpoints. Finally, a software library is presented comprising C++ binaries for all the positive definite sparse coding and dictionary learning approaches presented here.

  12. Alternatively Constrained Dictionary Learning For Image Superresolution.

    PubMed

    Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun

    2014-03-01

    Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionary-pair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.

  13. Classification of transient signals using sparse representations over adaptive dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.

    2011-06-01

    Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

  14. Sparse time-frequency decomposition based on dictionary adaptation.

    PubMed

    Hou, Thomas Y; Shi, Zuoqiang

    2016-04-13

    In this paper, we propose a time-frequency analysis method to obtain instantaneous frequencies and the corresponding decomposition by solving an optimization problem. In this optimization problem, the basis that is used to decompose the signal is not known a priori. Instead, it is adapted to the signal and is determined as part of the optimization problem. In this sense, this optimization problem can be seen as a dictionary adaptation problem, in which the dictionary is adaptive to one signal rather than a training set in dictionary learning. This dictionary adaptation problem is solved by using the augmented Lagrangian multiplier (ALM) method iteratively. We further accelerate the ALM method in each iteration by using the fast wavelet transform. We apply our method to decompose several signals, including signals with poor scale separation, signals with outliers and polluted by noise and a real signal. The results show that this method can give accurate recovery of both the instantaneous frequencies and the intrinsic mode functions.

  15. Discriminative object tracking via sparse representation and online dictionary learning.

    PubMed

    Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

    2014-04-01

    We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

  16. Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

    PubMed

    Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

    2013-01-01

    Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces

  17. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

    PubMed

    Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

    2014-02-01

    This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

  18. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

    PubMed

    Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

    2016-01-01

    To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

  19. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

    PubMed

    Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

    2016-09-22

    To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

  20. Learning discriminative dictionary for group sparse representation.

    PubMed

    Sun, Yubao; Liu, Qingshan; Tang, Jinhui; Tao, Dacheng

    2014-09-01

    In recent years, sparse representation has been widely used in object recognition applications. How to learn the dictionary is a key issue to sparse representation. A popular method is to use l1 norm as the sparsity measurement of representation coefficients for dictionary learning. However, the l1 norm treats each atom in the dictionary independently, so the learned dictionary cannot well capture the multisubspaces structural information of the data. In addition, the learned subdictionary for each class usually shares some common atoms, which weakens the discriminative ability of the reconstruction error of each subdictionary. This paper presents a new dictionary learning model to improve sparse representation for image classification, which targets at learning a class-specific subdictionary for each class and a common subdictionary shared by all classes. The model is composed of a discriminative fidelity, a weighted group sparse constraint, and a subdictionary incoherence term. The discriminative fidelity encourages each class-specific subdictionary to sparsely represent the samples in the corresponding class. The weighted group sparse constraint term aims at capturing the structural information of the data. The subdictionary incoherence term is to make all subdictionaries independent as much as possible. Because the common subdictionary represents features shared by all classes, we only use the reconstruction error of each class-specific subdictionary for classification. Extensive experiments are conducted on several public image databases, and the experimental results demonstrate the power of the proposed method, compared with the state-of-the-arts.

  1. Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

    PubMed Central

    Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

    2014-01-01

    It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively. PMID:24871988

  2. Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

    PubMed

    Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

    2014-01-01

    It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

  3. Approximate nearest neighbors via dictionary learning

    NASA Astrophysics Data System (ADS)

    Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos

    2011-06-01

    Approximate Nearest Neighbors (ANN) in high dimensional vector spaces is a fundamental, yet challenging problem in many areas of computer science, including computer vision, data mining and robotics. In this work, we investigate this problem from the perspective of compressive sensing, especially the dictionary learning aspect. High dimensional feature vectors are seldom seen to be sparse in the feature domain; examples include, but not limited to Scale Invariant Feature Transform (SIFT) descriptors, Histogram Of Gradients, Shape Contexts, etc. Compressive sensing advocates that if a given vector has a dense support in a feature space, then there should exist an alternative high dimensional subspace where the features are sparse. This idea is leveraged by dictionary learning techniques through learning an overcomplete projection from the feature space so that the vectors are sparse in the new space. The learned dictionary aids in refining the search for the nearest neighbors to a query feature vector into the most likely subspace combination indexed by its non-zero active basis elements. Since the size of the dictionary is generally very large, distinct feature vectors are most likely to have distinct non-zero basis. Utilizing this observation, we propose a novel representation of the feature vectors as tuples of non-zero dictionary indices, which then reduces the ANN search problem into hashing the tuples to an index table; thereby dramatically improving the speed of the search. A drawback of this naive approach is that it is very sensitive to feature perturbations. This can be due to two possibilities: (i) the feature vectors are corrupted by noise, (ii) the true data vectors undergo perturbations themselves. Existing dictionary learning methods address the first possibility. In this work we investigate the second possibility and approach it from a robust optimization perspective. This boils down to the problem of learning a dictionary robust to feature

  4. Retinex image enhancement via a learned dictionary

    NASA Astrophysics Data System (ADS)

    Chang, Huibin; Ng, Michael K.; Wang, Wei; Zeng, Tieyong

    2015-01-01

    The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary. This approach is different from existing variational methods, and the advantage of this approach is that the reflectance component in the Retinex model can be represented with more details by the dictionary. A variational method based on the dynamic dictionaries is adopted here, where it changes with respect to iterations of the enhancement algorithm. Numerical examples are also reported to demonstrate that the proposed methods can provide better visual quality of the enhanced high-contrast images than the other variational methods, i.e., revealing more details in the low-light part.

  5. Online multi-modal robust non-negative dictionary learning for visual tracking.

    PubMed

    Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

    2015-01-01

    Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

  6. Online multi-modal robust non-negative dictionary learning for visual tracking.

    PubMed

    Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

    2015-01-01

    Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715

  7. Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

    PubMed Central

    Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

    2015-01-01

    Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715

  8. Information-Theoretic Dictionary Learning for Image Classification.

    PubMed

    Qiu, Qiang; Patel, Vishal M; Chellappa, Rama

    2014-11-01

    We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real data sets demonstrate the effectiveness of our approach for image classification tasks.

  9. Standards? Dictionaries and Their Development in Second Language Learning Contexts.

    ERIC Educational Resources Information Center

    Pakir, Anne

    1999-01-01

    Focuses on English dictionaries and their development in second-language-learning contexts, taking the perspective that standards are usually codified in reference grammars, pronouncing dictionaries, and word dictionaries. Presents contemporary discussions of "English" and "Englishes" in Asia, a phenomenon that has come about through the global…

  10. Bayesian nonparametric dictionary learning for compressed sensing MRI.

    PubMed

    Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

    2014-12-01

    We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

  11. Weakly supervised visual dictionary learning by harnessing image attributes.

    PubMed

    Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

    2014-12-01

    Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

  12. Cross-View Action Recognition via Transferable Dictionary Learning.

    PubMed

    Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

    2016-05-01

    Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

  13. MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

    PubMed

    Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

    2013-01-01

    Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary (DDD) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First, minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second, linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third, instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

  14. Dictionary learning for data recovery in positron emission tomography.

    PubMed

    Valiollahzadeh, SeyyedMajid; Clark, John W; Mawlawi, Osama

    2015-08-01

    Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients' studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.

  15. Dictionary learning for data recovery in positron emission tomography

    NASA Astrophysics Data System (ADS)

    Valiollahzadeh, SeyyedMajid; Clark, John W., Jr.; Mawlawi, Osama

    2015-08-01

    Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline). A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions. For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients’ studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.

  16. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

    PubMed

    Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

    2016-01-01

    Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  17. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

    PubMed Central

    Lu, Hongyang; Wei, Jingbo; Wang, Yuhao; Deng, Xiaohua

    2016-01-01

    Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values. PMID:27110235

  18. Multimodal Task-Driven Dictionary Learning for Image Classification.

    PubMed

    Bahrampour, Soheil; Nasrabadi, Nasser M; Ray, Asok; Jenkins, William Kenneth

    2016-01-01

    Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages of feature-level fusion based on the joint sparse representation of the multimodal inputs. In this paper, we propose a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information. In this task-driven formulation, the multimodal dictionaries are learned simultaneously with their corresponding classifiers. The resulting multimodal dictionaries can generate discriminative latent features (sparse codes) from the data that are optimized for a given task such as binary or multiclass classification. Moreover, we present an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level. The efficacy of the proposed algorithms for multimodal classification is illustrated on four different applications--multimodal face recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared with the counterpart reconstructive-based dictionary learning algorithms, the task-driven formulations are more computationally efficient in the sense that they can be equipped with more compact dictionaries and still achieve superior performance.

  19. Image fusion via nonlocal sparse K-SVD dictionary learning.

    PubMed

    Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

    2016-03-01

    Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

  20. Face recognition using multiple maximum scatter difference discrimination dictionary learning

    NASA Astrophysics Data System (ADS)

    Zhu, Yanyong; Dong, Jiwen; Li, Hengjian

    2015-10-01

    Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.

  1. Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning.

    PubMed

    Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, U K Arvind

    2016-03-01

    In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

  2. Supervised dictionary learning for inferring concurrent brain networks.

    PubMed

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  3. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    PubMed Central

    Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

    2014-01-01

    To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

  4. Sparsity-constrained PET image reconstruction with learned dictionaries

    NASA Astrophysics Data System (ADS)

    Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

    2016-09-01

    PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

  5. Sparsity-constrained PET image reconstruction with learned dictionaries.

    PubMed

    Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

    2016-09-01

    PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging. PMID:27494441

  6. Distributed dictionary learning for sparse representation in sensor networks.

    PubMed

    Liang, Junli; Zhang, Miaohua; Zeng, Xianyu; Yu, Guoyang

    2014-06-01

    This paper develops a distributed dictionary learning algorithm for sparse representation of the data distributed across nodes of sensor networks, where the sensitive or private data are stored or there is no fusion center or there exists a big data application. The main contributions of this paper are: 1) we decouple the combined dictionary atom update and nonzero coefficient revision procedure into two-stage operations to facilitate distributed computations, first updating the dictionary atom in terms of the eigenvalue decomposition of the sum of the residual (correlation) matrices across the nodes then implementing a local projection operation to obtain the related representation coefficients for each node; 2) we cast the aforementioned atom update problem as a set of decentralized optimization subproblems with consensus constraints. Then, we simplify the multiplier update for the symmetry undirected graphs in sensor networks and minimize the separable subproblems to attain the consistent estimates iteratively; and 3) dictionary atoms are typically constrained to be of unit norm in order to avoid the scaling ambiguity. We efficiently solve the resultant hidden convex subproblems by determining the optimal Lagrange multiplier. Some experiments are given to show that the proposed algorithm is an alternative distributed dictionary learning approach, and is suitable for the sensor network environment. PMID:24733009

  7. Distributed dictionary learning for sparse representation in sensor networks.

    PubMed

    Liang, Junli; Zhang, Miaohua; Zeng, Xianyu; Yu, Guoyang

    2014-06-01

    This paper develops a distributed dictionary learning algorithm for sparse representation of the data distributed across nodes of sensor networks, where the sensitive or private data are stored or there is no fusion center or there exists a big data application. The main contributions of this paper are: 1) we decouple the combined dictionary atom update and nonzero coefficient revision procedure into two-stage operations to facilitate distributed computations, first updating the dictionary atom in terms of the eigenvalue decomposition of the sum of the residual (correlation) matrices across the nodes then implementing a local projection operation to obtain the related representation coefficients for each node; 2) we cast the aforementioned atom update problem as a set of decentralized optimization subproblems with consensus constraints. Then, we simplify the multiplier update for the symmetry undirected graphs in sensor networks and minimize the separable subproblems to attain the consistent estimates iteratively; and 3) dictionary atoms are typically constrained to be of unit norm in order to avoid the scaling ambiguity. We efficiently solve the resultant hidden convex subproblems by determining the optimal Lagrange multiplier. Some experiments are given to show that the proposed algorithm is an alternative distributed dictionary learning approach, and is suitable for the sensor network environment.

  8. Sparse representation and dictionary learning penalized image reconstruction for positron emission tomography

    NASA Astrophysics Data System (ADS)

    Chen, Shuhang; Liu, Huafeng; Shi, Pengcheng; Chen, Yunmei

    2015-01-01

    Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionary provides the regularization for our effort, and iterative procedures are used to solve the maximum likelihood function formulated on Poisson statistics. Specifically, in our formulation, a dictionary could be trained on CT images, to provide intrinsic anatomical structures for the reconstructed images, or adaptively learned from the noisy measurements of PET. Accuracy of the strategy with very promising application results from Monte-Carlo simulations, and real data are demonstrated.

  9. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    ERIC Educational Resources Information Center

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  10. Image transformation based on learning dictionaries across image spaces.

    PubMed

    Jia, Kui; Wang, Xiaogang; Tang, Xiaoou

    2013-02-01

    In this paper, we propose a framework of transforming images from a source image space to a target image space, based on learning coupled dictionaries from a training set of paired images. The framework can be used for applications such as image super-resolution and estimation of image intrinsic components (shading and albedo). It is based on a local parametric regression approach, using sparse feature representations over learned coupled dictionaries across the source and target image spaces. After coupled dictionary learning, sparse coefficient vectors of training image patch pairs are partitioned into easily retrievable local clusters. For any test image patch, we can fast index into its closest local cluster and perform a local parametric regression between the learned sparse feature spaces. The obtained sparse representation (together with the learned target space dictionary) provides multiple constraints for each pixel of the target image to be estimated. The final target image is reconstructed based on these constraints. The contributions of our proposed framework are three-fold. 1) We propose a concept of coupled dictionary learning based on coupled sparse coding which requires the sparse coefficient vectors of a pair of corresponding source and target image patches to have the same support, i.e., the same indices of nonzero elements. 2) We devise a space partitioning scheme to divide the high-dimensional but sparse feature space into local clusters. The partitioning facilitates extremely fast retrieval of closest local clusters for query patches. 3) Benefiting from sparse feature-based image transformation, our method is more robust to corrupted input data, and can be considered as a simultaneous image restoration and transformation process. Experiments on intrinsic image estimation and super-resolution demonstrate the effectiveness and efficiency of our proposed method.

  11. Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

    PubMed

    Zeng, Xianhua; Bian, Wei; Liu, Wei; Shen, Jialie; Tao, Dacheng

    2015-11-01

    Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.

  12. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    SciTech Connect

    Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-15

    patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  13. Compressed Sensing Reconstruction of 3D Ultrasound Data Using Dictionary Learning and Line-Wise Subsampling.

    PubMed

    Lorintiu, Oana; Liebgott, Hervé; Alessandrini, Martino; Bernard, Olivier; Friboulet, Denis

    2015-12-01

    In this paper we present a compressed sensing (CS) method adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries that allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. In this study, the dictionary was learned using the K-SVD algorithm and CS reconstruction was performed on the non-log envelope data by removing 20% to 80% of the original data. Using numerically simulated images, we evaluate the influence of the training parameters and of the sampling strategy. The latter is done by comparing the two most common sampling patterns, i.e., point-wise and line-wise random patterns. The results show in particular that line-wise sampling yields an accuracy comparable to the conventional point-wise sampling. This indicates that CS acquisition of 3D data is feasible in a relatively simple setting, and thus offers the perspective of increasing the frame rate by skipping the acquisition of RF lines. Next, we evaluated this approach on US volumes of several ex vivo and in vivo organs. We first show that the learned dictionary approach yields better performances than conventional fixed transforms such as Fourier or discrete cosine. Finally, we investigate the generality of the learned dictionary approach and show that it is possible to build a general dictionary allowing to reliably reconstruct different volumes of different ex vivo or in vivo organs.

  14. Category Specific Dictionary Learning for Attribute Specific Feature Selection.

    PubMed

    Wang, Wei; Yan, Yan; Winkler, Stefan; Sebe, Nicu

    2016-03-01

    Attributes, as mid-level features, have demonstrated great potential in visual recognition tasks due to their excellent propagation capability through different categories. However, existing attribute learning methods are prone to learning the correlated attributes. To discover the genuine attribute specific features, many feature selection methods have been proposed. However, these feature selection methods are implemented at the level of raw features that might be very noisy, and these methods usually fail to consider the structural information in the feature space. To address this issue, in this paper, we propose a label constrained dictionary learning approach combined with a multilayer filter. The feature selection is implemented at dictionary level, which can better preserve the structural information. The label constrained dictionary learning suppresses the intra-class noise by encouraging the sparse representations of intra-class samples to lie close to their center. A multilayer filter is developed to discover the representative and robust attribute specific bases. The attribute specific bases are only shared among the positive samples or the negative samples. The experiments on the challenging Animals with Attributes data set and the SUN attribute data set demonstrate the effectiveness of our proposed method.

  15. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  16. Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection.

    PubMed

    Su, Hai; Xing, Fuyong; Yang, Lin

    2016-06-01

    Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96.

  17. Low-Dose X-ray CT Reconstruction via Dictionary Learning

    PubMed Central

    Xu, Qiong; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2013-01-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. PMID:22542666

  18. Low-dose X-ray CT reconstruction via dictionary learning.

    PubMed

    Xu, Qiong; Yu, Hengyong; Mou, Xuanqin; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2012-09-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. PMID:22542666

  19. Shape prior modeling using sparse representation and online dictionary learning.

    PubMed

    Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N

    2012-01-01

    The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient. PMID:23286160

  20. Tensor-based dictionary learning for dynamic tomographic reconstruction

    NASA Astrophysics Data System (ADS)

    Tan, Shengqi; Zhang, Yanbo; Wang, Ge; Mou, Xuanqin; Cao, Guohua; Wu, Zhifang; Yu, Hengyong

    2015-04-01

    In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction.

  1. Tensor-based Dictionary Learning for Dynamic Tomographic Reconstruction

    PubMed Central

    Tan, Shengqi; Zhang, Yanbo; Wang, Ge; Mou, Xuanqin; Cao, Guohua; Wu, Zhifang; Yu, Hengyong

    2015-01-01

    In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. PMID:25779991

  2. Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering.

    PubMed

    Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi

    2016-01-01

    The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children.

  3. Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering

    PubMed Central

    Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi

    2016-01-01

    The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650

  4. Sinogram denoising via simultaneous sparse representation in learned dictionaries

    NASA Astrophysics Data System (ADS)

    Karimi, Davood; Ward, Rabab K.

    2016-05-01

    Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster.

  5. Compressed Sensing Electron tomography using adaptive dictionaries: a simulation study

    NASA Astrophysics Data System (ADS)

    AlAfeef, A.; Cockshott, P.; MacLaren, I.; McVitie, S.

    2014-06-01

    Electron tomography (ET) is an increasingly important technique for examining the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of 2D projection images to be reconstructed into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process this inverse problem is considered ill-posed (i.e., no unique solution exists). Furthermore reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Compressed sensing (CS) has recently been applied to ET and showed promising results for reducing missing wedge artifacts caused by limited angle sampling. CS uses a nonlinear reconstruction algorithm that employs image sparsity as a priori knowledge to improve the accuracy of density reconstruction from a relatively small number of projections compared to other reconstruction techniques. However, The performance of CS recovery depends heavily on the degree of sparsity of the reconstructed image in the selected transform domain. Prespecified transformations such as spatial gradients provide sparse image representation, while synthesising the sparsifying transform based on the properties of the particular specimen may give even sparser results and can extend the application of CS to specimens that can not be sparsely represented with other transforms such as Total variation (TV). In this work, we show that CS reconstruction in ET can be significantly improved by tailoring the sparsity representation using a sparse dictionary learning principle.

  6. A discriminant multi-scale histopathology descriptor using dictionary learning

    NASA Astrophysics Data System (ADS)

    Romo, David; García-Arteaga, Juan D.; Arbeláez, Pablo; Romero, Eduardo

    2014-03-01

    When examining a histological sample, an expert must not only identify structures at different scale and conceptual levels, i.e. cellular, tissular and organic, but also recognize and integrate the visual cues of specific pathologies and histological concepts such as "gland", "carcinoma" or "collagen". It is necessary then to code the texture and color so that the relevant information present at different scales is emphasized and preserved. In this article we propose a novel multi-scale image descriptor using dictionaries that learn and code discriminant visual elements associated with specific histological concepts. The dictionaries are built separately for each concept using sparse coding algorithms. The descriptor's discrimination capacity is evaluated using a naive strategy that assigns a particular image to the class best represented by a particular dictionary. Results show how, even using this very simple approach, average recall and precision measures of 0.81 and 0.86 were obtained for the challenging problem of classifying epidermis, eccrine glands, hair follicle and nodular carcinoma in basal skin carcinoma images.

  7. Dictionary learning and sparse recovery for electrodermal activity analysis

    NASA Astrophysics Data System (ADS)

    Kelsey, Malia; Dallal, Ahmed; Eldeeb, Safaa; Akcakaya, Murat; Kleckner, Ian; Gerard, Christophe; Quigley, Karen S.; Goodwin, Matthew S.

    2016-05-01

    Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas including psychophysiology; however, the majority of this research is typically undertaken in laboratory settings. To extend the ecological validity of laboratory assessments, researchers are taking advantage of advances in wireless biosensors to gather EDA data in ambulatory settings, such as in school classrooms. While measuring EDA in naturalistic contexts may enhance ecological validity, it also introduces analytical challenges that current techniques cannot address. One limitation is the limited efficiency and automation of analysis techniques. Many groups either analyze their data by hand, reviewing each individual record, or use computationally inefficient software that limits timely analysis of large data sets. To address this limitation, we developed a method to accurately and automatically identify SCRs using curve fitting methods. Curve fitting has been shown to improve the accuracy of SCR amplitude and location estimations, but have not yet been used to reduce computational complexity. In this paper, sparse recovery and dictionary learning methods are combined to improve computational efficiency of analysis and decrease run time, while maintaining a high degree of accuracy in detecting SCRs. Here, a dictionary is first created using curve fitting methods for a standard SCR shape. Then, orthogonal matching pursuit (OMP) is used to detect SCRs within a dataset using the dictionary to complete sparse recovery. Evaluation of our method, including a comparison to for speed and accuracy with existing software, showed an accuracy of 80% and a reduced run time.

  8. Online English-English Learner Dictionaries Boost Word Learning

    ERIC Educational Resources Information Center

    Nurmukhamedov, Ulugbek

    2012-01-01

    Learners of English might be familiar with several online monolingual dictionaries that are not necessarily the best choices for the English as Second/Foreign Language (ESL/EFL) context. Although these monolingual online dictionaries contain definitions, pronunciation guides, and other elements normally found in general-use dictionaries, they are…

  9. Multiple instance dictionary learning for subsurface object detection using handheld EMI

    NASA Astrophysics Data System (ADS)

    Zare, Alina; Cook, Matthew; Alvey, Brendan; Ho, Dominic K.

    2015-05-01

    A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific training data samples that correspond to target and non-target are not known and difficult to determine manually. In this paper, a dictionary learning method that addresses this issue using the multiple instance learning techniques is presented. Results are shown on real EMI data sets.

  10. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    DOE PAGES

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

  11. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    SciTech Connect

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  12. Event oriented dictionary learning for complex event detection.

    PubMed

    Yan, Yan; Yang, Yi; Meng, Deyu; Liu, Gaowen; Tong, Wei; Hauptmann, Alexander G; Sebe, Nicu

    2015-06-01

    Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask lp -norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method. PMID:25794390

  13. The efficacy of dictionary use while reading for learning new words.

    PubMed

    Hamilton, Harley

    2012-01-01

    The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online bilingual multimedia English-American Sign Language (ASL) dictionary (OBMEAD), (b) a paper English-ASL dictionary (PBEAD), and (c) an online monolingual English dictionary (OMED). It was found that for immediate recall of target words, the OBMEAD was superior to both the PBEAD and the OMED. For later recall, no significant difference appeared between the OBMEAD and the PBEAD. For both of these, recall was statistically superior to recall for words learned via the OMED.

  14. The Role of Electronic Pocket Dictionaries as an English Learning Tool among Chinese Students

    ERIC Educational Resources Information Center

    Jian, Hua-Li; Sandnes, Frode Eika; Law, Kris M. Y.; Huang, Yo-Ping; Huang, Yueh-Min

    2009-01-01

    This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary.…

  15. Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure.

    PubMed

    Aranda, Ramon; Ramirez-Manzanares, Alonso; Rivera, Mariano

    2015-12-01

    On the analysis of the Diffusion-Weighted Magnetic Resonance Images, multi-compartment models overcome the limitations of the well-known Diffusion Tensor model for fitting in vivo brain axonal orientations at voxels with fiber crossings, branching, kissing or bifurcations. Some successful multi-compartment methods are based on diffusion dictionaries. The diffusion dictionary-based methods assume that the observed Magnetic Resonance signal at each voxel is a linear combination of the fixed dictionary elements (dictionary atoms). The atoms are fixed along different orientations and diffusivity profiles. In this work, we present a sparse and adaptive diffusion dictionary method based on the Diffusion Basis Functions Model to estimate in vivo brain axonal fiber populations. Our proposal overcomes the following limitations of the diffusion dictionary-based methods: the limited angular resolution and the fixed shapes for the atom set. We propose to iteratively re-estimate the orientations and the diffusivity profile of the atoms independently at each voxel by using a simplified and easier-to-solve mathematical approach. As a result, we improve the fitting of the Diffusion-Weighted Magnetic Resonance signal. The advantages with respect to the former Diffusion Basis Functions method are demonstrated on the synthetic data-set used on the 2012 HARDI Reconstruction Challenge and in vivo human data. We demonstrate that improvements obtained in the intra-voxel fiber structure estimations benefit brain research allowing to obtain better tractography estimations. Hence, these improvements result in an accurate computation of the brain connectivity patterns.

  16. Undersampled face recognition via robust auxiliary dictionary learning.

    PubMed

    Wei, Chia-Po; Wang, Yu-Chiang Frank

    2015-06-01

    In this paper, we address the problem of robust face recognition with undersampled training data. Given only one or few training images available per subject, we present a novel recognition approach, which not only handles test images with large intraclass variations such as illumination and expression. The proposed method is also to handle the corrupted ones due to occlusion or disguise, which is not present during training. This is achieved by the learning of a robust auxiliary dictionary from the subjects not of interest. Together with the undersampled training data, both intra and interclass variations can thus be successfully handled, while the unseen occlusions can be automatically disregarded for improved recognition. Our experiments on four face image datasets confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art sparse representation-based methods.

  17. Land-cover classification in SAR images using dictionary learning

    NASA Astrophysics Data System (ADS)

    Aktaş, Gizem; Bak, Çaǧdaş; Nar, Fatih; Şen, Nigar

    2015-10-01

    Land-cover classification in Synthetic Aperture Radar (SAR) images has significance in both civil and military remote sensing applications. Accurate classification is a challenging problem due to variety of natural and man-made objects, seasonal changes at acquisition time, and diversity of image reconstruction algorithms.. In this study, Feature Preserving Despeckling (FPD), which is an edge preserving total variation based speckle reduction method, is applied as a preprocessing step. To handle the mentioned challenges, a novel feature extraction schema combined with a super-pixel segmentation and dictionary learning based classification is proposed. Computational complexity is another issue to handle in processing of high dimensional SAR images. Computational complexity of the proposed method is linearly proportional to the size of the image since it does not require a sliding window that accesses the pixels multiple times. Accuracy of the proposed method is validated on the dataset composed of TerraSAR-X high resolutions spot mode SAR images.

  18. Sparse Representation-Based Image Quality Index With Adaptive Sub-Dictionaries.

    PubMed

    Li, Leida; Cai, Hao; Zhang, Yabin; Lin, Weisi; Kot, Alex C; Sun, Xingming

    2016-08-01

    Distortions cause structural changes in digital images, leading to degraded visual quality. Dictionary-based sparse representation has been widely studied recently due to its ability to extract inherent image structures. Meantime, it can extract image features with slightly higher level semantics. Intuitively, sparse representation can be used for image quality assessment, because visible distortions can cause significant changes to the sparse features. In this paper, a new sparse representation-based image quality assessment model is proposed based on the construction of adaptive sub-dictionaries. An overcomplete dictionary trained from natural images is employed to capture the structure changes between the reference and distorted images by sparse feature extraction via adaptive sub-dictionary selection. Based on the observation that image sparse features are invariant to weak degradations and the perceived image quality is generally influenced by diverse issues, three auxiliary quality features are added, including gradient, color, and luminance information. The proposed method is not sensitive to training images, so a universal dictionary can be adopted for quality evaluation. Extensive experiments on five public image quality databases demonstrate that the proposed method produces the state-of-the-art results, and it delivers consistently well performances when tested in different image quality databases.

  19. An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

    PubMed

    Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

    2016-02-01

    Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

  20. Multi-level discriminative dictionary learning with application to large scale image classification.

    PubMed

    Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

    2015-10-01

    The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

  1. Separation of seismic blended data by sparse inversion over dictionary learning

    NASA Astrophysics Data System (ADS)

    Zhou, Yanhui; Chen, Wenchao; Gao, Jinghuai

    2014-07-01

    Recent development of blended acquisition calls for the new procedure to process blended seismic measurements. Presently, deblending and reconstructing unblended data followed by conventional processing is the most practical processing workflow. We study seismic deblending by advanced sparse inversion with a learned dictionary in this paper. To make our method more effective, hybrid acquisition and time-dithering sequential shooting are introduced so that clean single-shot records can be used to train the dictionary to favor the sparser representation of data to be recovered. Deblending and dictionary learning with l1-norm based sparsity are combined to construct the corresponding problem with respect to unknown recovery, dictionary, and coefficient sets. A two-step optimization approach is introduced. In the step of dictionary learning, the clean single-shot data are selected as trained data to learn the dictionary. For deblending, we fix the dictionary and employ an alternating scheme to update the recovery and coefficients separately. Synthetic and real field data were used to verify the performance of our method. The outcome can be a significant reference in designing high-efficient and low-cost blended acquisition.

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

    PubMed

    Zhang, Ye; Yu, Tenglong; Wang, Wenwu

    2014-01-01

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

  3. Binukid Dictionary.

    ERIC Educational Resources Information Center

    Otanes, Fe T., Ed.; Wrigglesworth, Hazel

    1992-01-01

    The dictionary of Binukid, a language spoken in the Bukidnon province of the Philippines, is intended as a tool for students of Binukid and for native Binukid-speakers interested in learning English. A single dialect was chosen for this work. The dictionary is introduced by notes on Binukid grammar, including basic information about phonology and…

  4. Deformable segmentation via sparse representation and dictionary learning.

    PubMed

    Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

    2012-10-01

    "Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. PMID:22959839

  5. Sparsity-based Poisson denoising with dictionary learning.

    PubMed

    Giryes, Raja; Elad, Michael

    2014-12-01

    The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging, and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive-independent identically distributed. Gaussian noise, for which many effective algorithms are available. However, in a low-SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. Salmon et al took this route, proposing a patch-based exponential image representation model based on Gaussian mixture model, leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping-based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR and achieving state-of-the-art results in cases of low SNR. PMID:25312930

  6. Dictionary learning and time sparsity for dynamic MR data reconstruction.

    PubMed

    Caballero, Jose; Price, Anthony N; Rueckert, Daniel; Hajnal, Joseph V

    2014-04-01

    The reconstruction of dynamic magnetic resonance data from an undersampled k-space has been shown to have a huge potential in accelerating the acquisition process of this imaging modality. With the introduction of compressed sensing (CS) theory, solutions for undersampled data have arisen which reconstruct images consistent with the acquired samples and compliant with a sparsity model in some transform domain. Fixed basis transforms have been extensively used as sparsifying transforms in the past, but recent developments in dictionary learning (DL) have been shown to outperform them by training an overcomplete basis that is optimal for a particular dataset. We present here an iterative algorithm that enables the application of DL for the reconstruction of cardiac cine data with Cartesian undersampling. This is achieved with local processing of spatio-temporal 3D patches and by independent treatment of the real and imaginary parts of the dataset. The enforcement of temporal gradients is also proposed as an additional constraint that can greatly accelerate the convergence rate and improve the reconstruction for high acceleration rates. The method is compared to and shown to systematically outperform k- t FOCUSS, a successful CS method that uses a fixed basis transform. PMID:24710166

  7. 2.5D dictionary learning based computed tomography reconstruction

    NASA Astrophysics Data System (ADS)

    Luo, Jiajia; Eri, Haneda; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    A computationally efficient 2.5D dictionary learning (DL) algorithm is proposed and implemented in the model- based iterative reconstruction (MBIR) framework for low-dose CT reconstruction. MBIR is based on the minimization of a cost function containing data-fitting and regularization terms to control the trade-off between data-fidelity and image noise. Due to the strong denoising performance of DL, it has previously been considered as a regularizer in MBIR, and both 2D and 3D DL implementations are possible. Compared to the 2D case, 3D DL keeps more spatial information and generates images with better quality although it requires more computation. We propose a novel 2.5D DL scheme, which leverages the computational advantage of 2D-DL, while attempting to maintain reconstruction quality similar to 3D-DL. We demonstrate the effectiveness of this new 2.5D DL scheme for MBIR in low-dose CT. By applying the 2D DL method in three different orthogonal planes and calculating the sparse coefficients accordingly, much of the 3D spatial information can be preserved without incurring the computational penalty of the 3D DL method. For performance evaluation, we use baggage phantoms with different number of projection views. In order to quantitatively compare the performance of different algorithms, we use PSNR, SSIM and region based standard deviation to measure the noise level, and use the edge response to calculate the resolution. Experimental results with full view datasets show that the different DL based algorithms have similar performance and 2.5D DL has the best resolution. Results with sparse view datasets show that 2.5D DL outperforms both 2D and 3D DL in terms of noise reduction. We also compare the computational costs, and 2.5D DL shows strong advantage over 3D DL in both full-view and sparse-view cases.

  8. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    PubMed

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

  9. Group-sparse representation with dictionary learning for medical image denoising and fusion.

    PubMed

    Li, Shutao; Yin, Haitao; Fang, Leyuan

    2012-12-01

    Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

  10. The Children's Literature Dictionary: Definitions, Resources, and Learning Activities.

    ERIC Educational Resources Information Center

    Latrobe, Kathy H.; Brodie, Carolyn S.; White, Maureen

    This dictionary of terms from children's literature provides definitions of 325 concepts and principles that will help librarians and teachers give meaning to literary works being introduced in the classroom. The book's alphabetical organization of terms facilitates access to information when the reader is considering a specific term. All of the…

  11. Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images

    PubMed Central

    Zhou, Mingyuan; Chen, Haojun; Paisley, John; Ren, Lu; Li, Lingbo; Xing, Zhengming; Dunson, David; Sapiro, Guillermo; Carin, Lawrence

    2013-01-01

    Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, uniformly selected at random. Spatial interrelationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature. PMID:21693421

  12. Tag removal in cardiac tagged MRI images using coupled dictionary learning.

    PubMed

    Makram, Abram W; Rushdi, Muhammad A; Khalifa, Ayman M; El-Wakad, Mohamed T

    2015-01-01

    Tagged Magnetic Resonance Imaging (tMRI) is considered to be the gold standard for quantitative assessment of the cardiac local functions. However, the tagging patterns and low myocardium-to-blood-pool contrast of tagged images bring great challenges to cardiac image processing and analysis tasks such as myocardium segmentation and tracking. Hence, there has been growing interest in techniques for removing tagging lines. In this work, a method for removing tagging patterns in tagged MR images using a coupled dictionary learning (CDL) model is proposed. In this model, identical sparse representations are assumed for image patches in the tagged MRI and corresponding cine MRI image spaces. First, we learn a dictionary for the tagged MRI image space. Then, we compute a dictionary for the cine MRI image space so that corresponding tagged and cine patches have the same sparse codes in terms of their respective dictionaries. Finally, in order to produce the de-tagged (cine version) of a test tagged image, the sparse codes of the tagged patches and the trained cine dictionary are used together to construct the de-tagged patches. We have tested this tag removal method on a dataset of tagged cardiac MR images. Our experimental results compared favorably with a recently proposed tag removal method that removes tags in the frequency domain using an optimal band-stop filter of harmonic peaks.

  13. Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.

    PubMed

    Tong, Tong; Wolz, Robin; Coupé, Pierrick; Hajnal, Joseph V; Rueckert, Daniel

    2013-08-01

    We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods.

  14. English Digital Dictionaries as Valuable Blended Learning Tools for Palestinian College Students

    ERIC Educational Resources Information Center

    Dwaik, Raghad A. A.

    2015-01-01

    Digital technology has become an indispensable aspect of foreign language learning around the globe especially in the case of college students who are often required to finish extensive reading assignments within a limited time period. Such pressure calls for the use of efficient tools such as digital dictionaries to help them achieve their…

  15. Comparing the Effect of Using Monolingual versus Bilingual Dictionary on Iranian Intermediate EFL Learners' Vocabulary Learning

    ERIC Educational Resources Information Center

    Ahangari, Saeideh; Dogolsara, Shokoufeh Abbasi

    2015-01-01

    This study aimed to investigate the effect of using two types of dictionaries (monolingual and bilingual) on Iranian intermediate EFL learners' vocabulary learning. An OPT (Oxford placement test, 2001) was administered among 90 students 60 of whom were selected as the participants of this study. They were sophomore students studying English as a…

  16. Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization.

    PubMed

    Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui

    2015-01-19

    Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation.

  17. Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction.

    PubMed

    Wang, Bigong; Li, Liang

    2015-01-01

    As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.

  18. Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

    PubMed Central

    Wang, Bigong; Li, Liang

    2015-01-01

    As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training. PMID:26089956

  19. Prospective acceleration of diffusion tensor imaging with compressed sensing using adaptive dictionaries

    PubMed Central

    McClymont, Darryl; Teh, Irvin; Whittington, Hannah J.; Grau, Vicente

    2015-01-01

    Purpose Diffusion MRI requires acquisition of multiple diffusion‐weighted images, resulting in long scan times. Here, we investigate combining compressed sensing and a fast imaging sequence to dramatically reduce acquisition times in cardiac diffusion MRI. Methods Fully sampled and prospectively undersampled diffusion tensor imaging data were acquired in five rat hearts at acceleration factors of between two and six using a fast spin echo (FSE) sequence. Images were reconstructed using a compressed sensing framework, enforcing sparsity by means of decomposition by adaptive dictionaries. A tensor was fit to the reconstructed images and fiber tractography was performed. Results Acceleration factors of up to six were achieved, with a modest increase in root mean square error of mean apparent diffusion coefficient (ADC), fractional anisotropy (FA), and helix angle. At an acceleration factor of six, mean values of ADC and FA were within 2.5% and 5% of the ground truth, respectively. Marginal differences were observed in the fiber tracts. Conclusion We developed a new k‐space sampling strategy for acquiring prospectively undersampled diffusion‐weighted data, and validated a novel compressed sensing reconstruction algorithm based on adaptive dictionaries. The k‐space undersampling and FSE acquisition each reduced acquisition times by up to 6× and 8×, respectively, as compared to fully sampled spin echo imaging. Magn Reson Med 76:248–258, 2016. © 2015 Wiley Periodicals, Inc. PMID:26302363

  20. Sparse dictionary learning for resting-state fMRI analysis

    NASA Astrophysics Data System (ADS)

    Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

    2011-09-01

    Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

  1. Learning and Domain Adaptation

    NASA Astrophysics Data System (ADS)

    Mansour, Yishay

    Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition).

  2. Supervised block sparse dictionary learning for simultaneous clustering and classification in computational anatomy.

    PubMed

    Varol, Erdem; Davatzikos, Christos

    2014-01-01

    An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects.

  3. Supervised block sparse dictionary learning for simultaneous clustering and classification in computational anatomy.

    PubMed

    Varol, Erdem; Davatzikos, Christos

    2014-01-01

    An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects. PMID:25485410

  4. A Fast Algorithm for Learning Overcomplete Dictionary for Sparse Representation Based on Proximal Operators.

    PubMed

    Li, Zhenni; Ding, Shuxue; Li, Yujie

    2015-09-01

    We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coherence penalty for the dictionary atoms and with the sparsity regularization of the coefficient matrix. Because the problem is nonconvex and nonsmooth, this minimization problem cannot be solved efficiently by an ordinary optimization method. We propose a decomposition scheme and an alternating optimization that can turn the problem into a set of minimizations of piecewise quadratic and univariate subproblems, each of which is a single variable vector problem, of either one dictionary atom or one coefficient vector. Although the subproblems are still nonsmooth, remarkably they become much simpler so that we can find a closed-form solution by introducing a proximal operator. This leads to an efficient algorithm for sparse representation. To our knowledge, applying the proximal operator to the problem with an incoherence term and obtaining the optimal dictionary atoms in closed form with a proximal operator technique have not previously been studied. The main advantages of the proposed algorithm are that, as suggested by our analysis and simulation study, it has lower computational complexity and a higher convergence rate than state-of-the-art algorithms. In addition, for real applications, it shows good performance and significant reductions in computational time.

  5. Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

    PubMed

    Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

    2014-01-01

    An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

  6. Projection domain denoising method based on dictionary learning for low-dose CT image reconstruction.

    PubMed

    Zhang, Haiyan; Zhang, Liyi; Sun, Yunshan; Zhang, Jingyu

    2015-01-01

    Reducing X-ray tube current is one of the widely used methods for decreasing the radiation dose. Unfortunately, the signal-to-noise ratio (SNR) of the projection data degrades simultaneously. To improve the quality of reconstructed images, a dictionary learning based penalized weighted least-squares (PWLS) approach is proposed for sinogram denoising. The weighted least-squares considers the statistical characteristic of noise and the penalty models the sparsity of sinogram based on dictionary learning. Then reconstruct CT image using filtered back projection (FBP) algorithm from the denoised sinogram. The proposed method is particularly suitable for the projection data with low SNR. Experimental results show that the proposed method can get high-quality CT images when the signal to noise ratio of projection data declines sharply.

  7. Teaching and Testing Dictionary Use.

    ERIC Educational Resources Information Center

    Kernerman, Ari

    1998-01-01

    It is important to teach dictionary skills to elementary English-as-a-Second-Language students. An English learner's dictionary is a student's main source of information when lacking human help, and though English words may be forgotten, the dictionary skill is learned for life. This article discusses how to include dictionary use in an exam and…

  8. Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

    PubMed

    Pant, Jeevan K; Krishnan, Sridhar

    2014-04-01

    A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

  9. Automated Variability Selection in Time-domain Imaging Surveys Using Sparse Representations with Learned Dictionaries

    NASA Astrophysics Data System (ADS)

    Wozniak, Przemyslaw R.; Moody, D. I.; Ji, Z.; Brumby, S. P.; Brink, H.; Richards, J.; Bloom, J. S.

    2013-01-01

    Exponential growth in data streams and discovery power delivered by modern time-domain imaging surveys creates a pressing need for variability extraction algorithms that are both fully automated and highly reliable. The current state of the art methods based on image differencing are limited by the fact that for every real variable source the algorithm returns a large number of bogus "detections" caused by atmospheric effects and instrumental signatures coupled with imperfect image processing. Here we present a new approach to this problem inspired by recent advances in computer vision and train the machine directly on pixel data. The training data set comes from the Palomar Transient Factory survey and consists of small images centered around transient candidates with known real/bogus classification. This set of 441-dimensional vectors (21x21 pixel images) is then transformed to a linear representation using the so called dictionary, an overcomplete basis constructed separately for each class. The learning algorithm captures the fact that the intrinsic dimensionality of the input images is typically much lower than the size of the dictionary, and therefore the data vectors are well approximated with a small number of dictionary elements. This sparse representation can be used to construct informative features for any suitable machine learning classifier. In our preliminary analysis automatically extracted features approach the performance of features constructed by humans using subject domain knowledge.

  10. Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning

    PubMed Central

    Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D.; Prince, Jerry L.

    2016-01-01

    Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms. PMID:27601772

  11. Segmentation of thalamus from MR images via task-driven dictionary learning

    NASA Astrophysics Data System (ADS)

    Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D.; Prince, Jerry L.

    2016-03-01

    Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.

  12. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

    PubMed Central

    Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C.

    2014-01-01

    Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. PMID:23542422

  13. Noisy image magnification with total variation regularization and order-changed dictionary learning

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification algorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to solve this problem. The proposed algorithm takes the advantages of both regularization-based method and learning-based method. The first step is based on total variation (TV) regularization and the second step is based on sparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image and at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training algorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the proposed algorithm performs better than many other algorithms when the noise is not serious. The proposed algorithm can also provide better visual quality on natural LR images.

  14. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

    PubMed Central

    Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  15. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    PubMed

    Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  16. Sparse dictionary learning for fMRI analysis using autocorrelation maximization.

    PubMed

    Khalid, Muhammad Usman; Shah, Adnan; Seghouane, Abd-Krim

    2015-08-01

    In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These signals are considered as underlying sources and SDL is used to estimate them. However, the sparse GLM model does not take into account the autocorrelations in fMRI data. To address this shortcoming, a new model is proposed to incorporate the prior knowledge about lag-1 autocorrelation into dictionary update stage. This helps improve the sensitivity and specificity of the fMRI data during statistical analysis. Using a simulation study, the effect of the proposed dictionary update on sGLM is compared to conventional sGLM by utilizing various detrending techniques. Furthermore, the proposed update is validated in an sGLM framework for real fMRI datasets, which shows its better capability to estimate neural dynamics in presence of spatiotemporal dependencies.

  17. Learning a common dictionary for subject-transfer decoding with resting calibration.

    PubMed

    Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

    2015-05-01

    Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments.

  18. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

    NASA Astrophysics Data System (ADS)

    Chen, Yang; Yin, Xindao; Shi, Luyao; Shu, Huazhong; Luo, Limin; Coatrieux, Jean-Louis; Toumoulin, Christine

    2013-08-01

    In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.

  19. Extracting brain regions from rest fMRI with total-variation constrained dictionary learning.

    PubMed

    Abraham, Alexandre; Dohmatob, Elvis; Thirion, Bertrand; Samaras, Dimitris; Varoquaux, Gael

    2013-01-01

    Spontaneous brain activity reveals mechanisms of brain function and dysfunction. Its population-level statistical analysis based on functional images often relies on the definition of brain regions that must summarize efficiently the covariance structure between the multiple brain networks. In this paper, we extend a network-discovery approach, namely dictionary learning, to readily extract brain regions. To do so, we introduce a new tool drawing from clustering and linear decomposition methods by carefully crafting a penalty. Our approach automatically extracts regions from rest fMRI that better explain the data and are more stable across subjects than reference decomposition or clustering methods.

  20. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

    PubMed

    Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

    2016-01-15

    Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease.

  1. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

    PubMed

    Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

    2016-01-15

    Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. PMID:26524138

  2. Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation

    PubMed Central

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S.; Prince, Jerry L.; Pham, Dzung L.

    2015-01-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available, state-of-the art approaches. PMID:26340685

  3. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    PubMed

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  4. SU-E-I-41: Dictionary Learning Based Quantitative Reconstruction for Low-Dose Dual-Energy CT (DECT)

    SciTech Connect

    Xu, Q; Xing, L; Xiong, G; Elmore, K; Min, J

    2015-06-15

    Purpose: DECT collects two sets of projection data under higher and lower energies. With appropriates composition methods on linear attenuation coefficients, quantitative information about the object, such as density, can be obtained. In reality, one of the important problems in DECT is the radiation dose due to doubled scans. This work is aimed at establishing a dictionary learning based reconstruction framework for DECT for improved image quality while reducing the imaging dose. Methods: In our method, two dictionaries were learned respectively from the high-energy and lowenergy image datasets of similar objects under normal dose in advance. The linear attenuation coefficient was decomposed into two basis components with material based composition method. An iterative reconstruction framework was employed. Two basis components were alternately updated with DECT datasets and dictionary learning based sparse constraints. After one updating step under the dataset fidelity constraints, both high-energy and low-energy images can be obtained from the two basis components. Sparse constraints based on the learned dictionaries were applied to the high- and low-energy images to update the two basis components. The iterative calculation continues until a pre-set number of iteration was reached. Results: We evaluated the proposed dictionary learning method with dual energy images collected using a DECT scanner. We re-projected the projection data with added Poisson noise to reflect the low-dose situation. The results obtained by the proposed method were compared with that obtained using FBP based method and TV based method. It was found that the proposed approach yield better results than other methods with higher resolution and less noise. Conclusion: The use of dictionary learned from DECT images under normal dose is valuable and leads to improved results with much lower imaging dose.

  5. Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

    NASA Astrophysics Data System (ADS)

    Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

    2014-09-01

    Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

  6. Consolidating guided wave simulations and experimental data: a dictionary learning approach

    NASA Astrophysics Data System (ADS)

    Alguri, K. Supreet; Harley, Joel B.

    2016-04-01

    Modeling and simulating guided wave propagation in complex, geometric structures is a topic of significant interest in structural health monitoring. These models have the potential to benefit damage detection, localization, and characterization in structures where traditional algorithms fail. Numerical modelling (for example, using finite element or semi-analytical finite element methods) is a popular approach for simulating complex wave behavior. Yet, using these models to improve experimental data analysis remains difficult. Numerical simulations and experimental data rarely match due to uncertainty in the properties of the structures and the guided waves traveling within them. As a result, there is a significant need to reduce this uncertainty by incorporating experimental data into the models. In this paper, we present a dictionary learning framework to address this challenge. Specifically, use dictionary learning to combine numerical wavefield simulations with 24 simulated guided wave measurements with different frequency-dependent velocity characteristics (emulating an experimental system) to make accurate, global predictions about experimental wave behavior. From just 24 measurements, we show that we can predict and extrapolate guided wave behavior with accuracies greater than 92%.

  7. Generalized Coupled Dictionary Learning Approach With Applications to Cross-Modal Matching.

    PubMed

    Mandal, Devraj; Biswas, Soma

    2016-08-01

    Coupled dictionary learning (CDL) has recently emerged as a powerful technique with wide variety of applications ranging from image synthesis to classification tasks. In this paper, we extend the existing CDL approaches in two aspects to make them more suitable for the task of cross-modal matching. Data coming from different modalities may or may not be paired. For example, for image-text retrieval problem, 100 images of a class are available as opposed to only 50 samples of text data for training. Current CDL approaches are not designed to handle such scenarios, where classes of data points in one modality correspond to classes of data points in the other modality. Given the data from the two modalities, first two dictionaries are learnt for the respective modalities, so that the data have a sparse representation with respect to their own dictionaries. Then, the sparse coefficients from the two modalities are transformed in such a manner that data from the same class are maximally correlated, while that from different classes have very less correlation. This way of modeling the coupling between the sparse representations of the two modalities makes this approach work seamlessly for paired as well as unpaired data. The discriminative coupling term also makes the approach better suited for classification tasks. Experiments on different publicly available cross-modal data sets, namely, CUHK photosketch face data set, HFB visible and near-infrared facial images data set, IXMAS multiview action recognition data set, wiki image and text data set and Multiple Features data set, show that this generalized CDL approach performs better than the state-of-the-art for both paired as well as unpaired data.

  8. Generalized Coupled Dictionary Learning Approach With Applications to Cross-Modal Matching.

    PubMed

    Mandal, Devraj; Biswas, Soma

    2016-08-01

    Coupled dictionary learning (CDL) has recently emerged as a powerful technique with wide variety of applications ranging from image synthesis to classification tasks. In this paper, we extend the existing CDL approaches in two aspects to make them more suitable for the task of cross-modal matching. Data coming from different modalities may or may not be paired. For example, for image-text retrieval problem, 100 images of a class are available as opposed to only 50 samples of text data for training. Current CDL approaches are not designed to handle such scenarios, where classes of data points in one modality correspond to classes of data points in the other modality. Given the data from the two modalities, first two dictionaries are learnt for the respective modalities, so that the data have a sparse representation with respect to their own dictionaries. Then, the sparse coefficients from the two modalities are transformed in such a manner that data from the same class are maximally correlated, while that from different classes have very less correlation. This way of modeling the coupling between the sparse representations of the two modalities makes this approach work seamlessly for paired as well as unpaired data. The discriminative coupling term also makes the approach better suited for classification tasks. Experiments on different publicly available cross-modal data sets, namely, CUHK photosketch face data set, HFB visible and near-infrared facial images data set, IXMAS multiview action recognition data set, wiki image and text data set and Multiple Features data set, show that this generalized CDL approach performs better than the state-of-the-art for both paired as well as unpaired data. PMID:27295672

  9. The Efficacy of Dictionary Use while Reading for Learning New Words

    ERIC Educational Resources Information Center

    Hamilton, Harley

    2012-01-01

    The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online…

  10. Coupled Dictionary Learning for the Detail-Enhanced Synthesis of 3-D Facial Expressions.

    PubMed

    Liang, Haoran; Liang, Ronghua; Song, Mingli; He, Xiaofei

    2016-04-01

    The desire to reconstruct 3-D face models with expressions from 2-D face images fosters increasing interest in addressing the problem of face modeling. This task is important and challenging in the field of computer animation. Facial contours and wrinkles are essential to generate a face with a certain expression; however, these details are generally ignored or are not seriously considered in previous studies on face model reconstruction. Thus, we employ coupled radius basis function networks to derive an intermediate 3-D face model from a single 2-D face image. To optimize the 3-D face model further through landmarks, a coupled dictionary that is related to 3-D face models and their corresponding 3-D landmarks is learned from the given training set through local coordinate coding. Another coupled dictionary is then constructed to bridge the 2-D and 3-D landmarks for the transfer of vertices on the face model. As a result, the final 3-D face can be generated with the appropriate expression. In the testing phase, the 2-D input faces are converted into 3-D models that display different expressions. Experimental results indicate that the proposed approach to facial expression synthesis can obtain model details more effectively than previous methods can.

  11. RoLo: A Dictionary Interface that Minimizes Extraneous Cognitive Load of Lookup and Supports Incidental and Incremental Learning of Vocabulary

    ERIC Educational Resources Information Center

    Dang, Thanh-Dung; Chen, Gwo-Dong; Dang, Giao; Li, Liang-Yi; Nurkhamid

    2013-01-01

    Dictionary use can improve reading comprehension and incidental vocabulary learning. Nevertheless, great extraneous cognitive load imposed by the search process may reduce or even prevent the improvement. With the help of technology, dictionary users can now instantly access the meaning list of a searched word using a mouse click. However, they…

  12. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

    PubMed

    Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

    2015-07-21

    Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

  13. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data

    NASA Astrophysics Data System (ADS)

    Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

    2015-07-01

    Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

  14. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    SciTech Connect

    Gou, S; Rapacchi, S; Hu, P; Sheng, K

    2014-06-15

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.

  15. Using Computerized Bilingual Dictionaries To help Maximize English Vocabulary Learning at Japanese Colleges.

    ERIC Educational Resources Information Center

    Loucky, John Paul

    2003-01-01

    Compares various computerized bilingual dictionaries for their relative effectiveness in helping Japanese college students at several language proficiency levels to access new English target vocabulary. (Author/VWL)

  16. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  17. Skin Dictionary

    MedlinePlus

    ... your skin, hair, and nails Skin dictionary Camp Discovery Good Skin Knowledge lesson plans and activities Video library Find a ... your skin, hair, and nails Skin dictionary Camp Discovery Good Skin Knowledge lesson plans and activities Video library Find a ...

  18. Learning the Language of Difference: The Dictionary in the High School.

    ERIC Educational Resources Information Center

    Willinsky, John

    1987-01-01

    Reports on dictionaries' power to misrepresent gender. Examines the definitions of three terms (clitoris, penis, and vagina) in eight leading high school dictionaries. Concludes that the absence of certain female gender-related terms represents another instance of institutionalized silence about the experience of women. (MM)

  19. The Efficacy of Dictionary Use while Reading for Learning New Words

    ERIC Educational Resources Information Center

    Hamilton, Harley

    2012-01-01

    This paper describes a study investigating the use of three types of dictionaries by deaf (i.e., with severe to profound hearing loss) high school students while reading to determine the effectiveness of each type for acquiring the meanings of unknown vocabulary in text. The dictionary types used include an online bilingual multimedia English-ASL…

  20. Adaptive Learning and Risk Taking

    ERIC Educational Resources Information Center

    Denrell, Jerker

    2007-01-01

    Humans and animals learn from experience by reducing the probability of sampling alternatives with poor past outcomes. Using simulations, J. G. March (1996) illustrated how such adaptive sampling could lead to risk-averse as well as risk-seeking behavior. In this article, the author develops a formal theory of how adaptive sampling influences risk…

  1. Organization of Distributed Adaptive Learning

    ERIC Educational Resources Information Center

    Vengerov, Alexander

    2009-01-01

    The growing sensitivity of various systems and parts of industry, society, and even everyday individual life leads to the increased volume of changes and needs for adaptation and learning. This creates a new situation where learning from being purely academic knowledge transfer procedure is becoming a ubiquitous always-on essential part of all…

  2. Non-stationary noise estimation using dictionary learning and Gaussian mixture models

    NASA Astrophysics Data System (ADS)

    Hughes, James M.; Rockmore, Daniel N.; Wang, Yang

    2014-02-01

    Stationarity of the noise distribution is a common assumption in image processing. This assumption greatly simplifies denoising estimators and other model parameters and consequently assuming stationarity is often a matter of convenience rather than an accurate model of noise characteristics. The problematic nature of this assumption is exacerbated in real-world contexts, where noise is often highly non-stationary and can possess time- and space-varying characteristics. Regardless of model complexity, estimating the parameters of noise dis- tributions in digital images is a difficult task, and estimates are often based on heuristic assumptions. Recently, sparse Bayesian dictionary learning methods were shown to produce accurate estimates of the level of additive white Gaussian noise in images with minimal assumptions. We show that a similar model is capable of accu- rately modeling certain kinds of non-stationary noise processes, allowing for space-varying noise in images to be estimated, detected, and removed. We apply this modeling concept to several types of non-stationary noise and demonstrate the model's effectiveness on real-world problems, including denoising and segmentation of images according to noise characteristics, which has applications in image forensics.

  3. Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

    NASA Astrophysics Data System (ADS)

    Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

    2016-03-01

    A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

  4. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.

  5. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  6. Interoperability in Personalized Adaptive Learning

    ERIC Educational Resources Information Center

    Aroyo, Lora; Dolog, Peter; Houben, Geert-Jan; Kravcik, Milos; Naeve, Ambjorn; Nilsson, Mikael; Wild, Fridolin

    2006-01-01

    Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the…

  7. Terrestrial Laser Scanner Data Denoising by Dictionary Learning of Sparse Coding

    NASA Astrophysics Data System (ADS)

    Smigiel, E.; Alby, E.; Grussenmeyer, P.

    2013-07-01

    Point cloud processing is basically a signal processing issue. The huge amount of data which are collected with Terrestrial Laser Scanners or photogrammetry techniques faces the classical questions linked with signal or image processing. Among others, denoising and compression are questions which have to be addressed in this context. That is why, one has to turn attention to signal theory because it is susceptible to guide one's good practices or to inspire new ideas from the latest developments of this field. The literature have been showing for decades how strong and dynamic, the theoretical field is and how efficient the derived algorithms have become. For about ten years, a new technique has appeared: known as compressive sensing or compressive sampling, it is based first on sparsity which is an interesting characteristic of many natural signals. Based on this concept, many denoising and compression techniques have shown their efficiencies. Sparsity can also be seen as redundancy removal of natural signals. Taken along with incoherent measurements, compressive sensing has appeared and uses the idea that redundancy could be removed at the very early stage of sampling. Hence, instead of sampling the signal at high sampling rate and removing redundancy as a second stage, the acquisition stage itself may be run with redundancy removal. This paper gives some theoretical aspects of these ideas with first simple mathematics. Then, the idea of compressive sensing for a Terrestrial Laser Scanner is examined as a potential research question and finally, a denoising scheme based on a dictionary learning of sparse coding is experienced. Both the theoretical discussion and the obtained results show that it is worth staying close to signal processing theory and its community to take benefit of its latest developments.

  8. Perceptual learning in sensorimotor adaptation.

    PubMed

    Darainy, Mohammad; Vahdat, Shahabeddin; Ostry, David J

    2013-11-01

    Motor learning often involves situations in which the somatosensory targets of movement are, at least initially, poorly defined, as for example, in learning to speak or learning the feel of a proper tennis serve. Under these conditions, motor skill acquisition presumably requires perceptual as well as motor learning. That is, it engages both the progressive shaping of sensory targets and associated changes in motor performance. In the present study, we test the idea that perceptual learning alters somatosensory function and in so doing produces changes to human motor performance and sensorimotor adaptation. Subjects in these experiments undergo perceptual training in which a robotic device passively moves the subject's arm on one of a set of fan-shaped trajectories. Subjects are required to indicate whether the robot moved the limb to the right or the left and feedback is provided. Over the course of training both the perceptual boundary and acuity are altered. The perceptual learning is observed to improve both the rate and extent of learning in a subsequent sensorimotor adaptation task and the benefits persist for at least 24 h. The improvement in the present studies varies systematically with changes in perceptual acuity and is obtained regardless of whether the perceptual boundary shift serves to systematically increase or decrease error on subsequent movements. The beneficial effects of perceptual training are found to be substantially dependent on reinforced decision-making in the sensory domain. Passive-movement training on its own is less able to alter subsequent learning in the motor system. Overall, this study suggests perceptual learning plays an integral role in motor learning.

  9. Adaptively Ubiquitous Learning in Campus Math Path

    ERIC Educational Resources Information Center

    Shih, Shu-Chuan; Kuo, Bor-Chen; Liu, Yu-Lung

    2012-01-01

    The purposes of this study are to develop and evaluate the instructional model and learning system which integrate ubiquitous learning, computerized adaptive diagnostic testing system and campus math path learning. The researcher first creates a ubiquitous learning environment which is called "adaptive U-learning math path system". This system…

  10. Haida Dictionary.

    ERIC Educational Resources Information Center

    Lawrence, Erma, Comp.

    This Haida-English dictionary introduces the Alaskan Haida alphabet and gives Haida words with English translation and the English equivalent pronunciation. The first half of the dictionary is an introduction to the sound system and grammar of Haida. Short discussions of the following topics are included: vowels; consonants; semivowels; rounded…

  11. Adaptive Batch Mode Active Learning.

    PubMed

    Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

    2015-08-01

    Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

  12. The Multimedia Dictionary of American Sign Language: Learning Lessons About Language, Technology, and Business.

    ERIC Educational Resources Information Center

    Wilcox, Sherman

    2003-01-01

    Reports on the the Multimedia Dictionary of American Sign language, which was was conceived in he late 1980s as a melding of the pioneering work in American Sign language lexicography that had been carried out decades earlier and the newly emerging computer technologies that were integrating use of graphical user-interface designs, rapidly…

  13. Online Dictionaries and the Teaching/Learning of English in the Expanding Circle

    ERIC Educational Resources Information Center

    Fuertes-Olivera, Pedro A.; Cabello de Alba, Beatriz Perez

    2012-01-01

    This article follows current research on English for Specific Business Purposes, which focuses on the analysis of contextualized business genres and on identifying the strategies that can be associated with effective business communication (Nickerson, 2005). It explores whether free internet dictionaries can be used for promoting effective…

  14. Adaptable, Personalised E-Learning Incorporating Learning Styles

    ERIC Educational Resources Information Center

    Peter, Sophie E.; Bacon, Elizabeth; Dastbaz, Mohammad

    2010-01-01

    Purpose: The purpose of this paper is to discuss how learning styles and theories are currently used within personalised adaptable e-learning adaptive systems. This paper then aims to describe the e-learning platform iLearn and how this platform is designed to incorporate learning styles as part of the personalisation offered by the system.…

  15. The Influence of Electronic Dictionaries on Vocabulary Knowledge Extension

    ERIC Educational Resources Information Center

    Rezaei, Mojtaba; Davoudi, Mohammad

    2016-01-01

    Vocabulary learning needs special strategies in language learning process. The use of dictionaries is a great help in vocabulary learning and nowadays the emergence of electronic dictionaries has added a new and valuable resource for vocabulary learning. The present study aims to explore the influence of Electronic Dictionaries (ED) Vs. Paper…

  16. MANDARIN CHINESE DICTIONARY.

    ERIC Educational Resources Information Center

    WANG, FRED FANGYU

    IN RESPONSE TO THE NEEDS OF THE GROWING NUMBER OF AMERICAN HIGH SCHOOL AND COLLEGE STUDENTS LEARNING CHINESE, SETON HALL UNIVERSITY UNDERTOOK A CONTRACT WITH THE U.S. OFFICE OF EDUCATION TO COMPILE A BILINGUAL POCKET-SIZE DICTIONARY FOR BEGINNING STUDENTS OF SPOKEN MANDARIN CHINESE. THE PRESENT WORK IS THE CHINESE TO ENGLISH SECTION IN PRELIMINARY…

  17. Learning Dictionaries of Sparse Codes of 3D Movements of Body Joints for Real-Time Human Activity Understanding

    PubMed Central

    Qi, Jin; Yang, Zhiyong

    2014-01-01

    Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications. PMID:25473850

  18. Enhancement of snow cover change detection with sparse representation and dictionary learning

    NASA Astrophysics Data System (ADS)

    Varade, D.; Dikshit, O.

    2014-11-01

    Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

  19. Adapting Online Education to Different Learning Styles.

    ERIC Educational Resources Information Center

    Muir, Diana J.

    The purpose of this research project was to determine if online learning could be adapted to individual learning styles and if this made a difference in the standardized testing scores of Internet students. An overview is provided of current learning theories, including the four stages of learning (exposure, guided learning, independent, mastery)…

  20. Dynamic versus Static Dictionary with and without Printed Focal Words in e-Book Reading as Facilitator for Word Learning

    ERIC Educational Resources Information Center

    Korat, Ofra; Levin, Iris; Ben-Shabt, Anat; Shneor, Dafna; Bokovza, Limor

    2014-01-01

    We investigated the extent to which a dictionary embedded in an e-book with static or dynamic visuals with and without printed focal words affects word learning. A pretest-posttest design was used to measure gains of expressive words' meaning and their spelling. The participants included 250 Hebrew-speaking second graders from…

  1. Students' Understanding of Dictionary Entries: A Study with Respect to Four Learners' Dictionaries.

    ERIC Educational Resources Information Center

    Jana, Abhra; Amritavalli, Vijaya; Amritavalli, R.

    2003-01-01

    Investigates the effects of definitional information in the form of dictionary entries, on second language learners' vocabulary learning in an instructed setting. Indian students (Native Hindi speakers) of English received monolingual English dictionary entries of five previously unknown words from four different learner's dictionaries. Results…

  2. Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning.

    PubMed

    Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

    2015-01-01

    Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique.

  3. Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

    PubMed Central

    Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

    2015-01-01

    Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

  4. Adaptive Units of Learning and Educational Videogames

    ERIC Educational Resources Information Center

    Moreno-Ger, Pablo; Thomas, Pilar Sancho; Martinez-Ortiz, Ivan; Sierra, Jose Luis; Fernandez-Manjon, Baltasar

    2007-01-01

    In this paper, we propose three different ways of using IMS Learning Design to support online adaptive learning modules that include educational videogames. The first approach relies on IMS LD to support adaptation procedures where the educational games are considered as Learning Objects. These games can be included instead of traditional content…

  5. Adaptive Learning Systems: Beyond Teaching Machines

    ERIC Educational Resources Information Center

    Kara, Nuri; Sevim, Nese

    2013-01-01

    Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

  6. WordSpy: identifying transcription factor binding motifs by building a dictionary and learning a grammar

    PubMed Central

    Wang, Guandong; Yu, Taotao; Zhang, Weixiong

    2005-01-01

    Transcription factor (TF) binding sites or motifs (TFBMs) are functional cis-regulatory DNA sequences that play an essential role in gene transcriptional regulation. Although many experimental and computational methods have been developed, finding TFBMs remains a challenging problem. We propose and develop a novel dictionary based motif finding algorithm, which we call WordSpy. One significant feature of WordSpy is the combination of a word counting method and a statistical model which consists of a dictionary of motifs and a grammar specifying their usage. The algorithm is suitable for genome-wide motif finding; it is capable of discovering hundreds of motifs from a large set of promoters in a single run. We further enhance WordSpy by applying gene expression information to separate true TFBMs from spurious ones, and by incorporating negative sequences to identify discriminative motifs. In addition, we also use randomly selected promoters from the genome to evaluate the significance of the discovered motifs. The output from WordSpy consists of an ordered list of putative motifs and a set of regulatory sequences with motif binding sites highlighted. The web server of WordSpy is available at . PMID:15980501

  7. Mythomanics: A Painless Dictionary and Vocabulary Skills Builder.

    ERIC Educational Resources Information Center

    Thomson, Lynn M.

    1989-01-01

    Describes a dictionary game, adapted from the game "Balderdash," which builds skills in vocabulary and dictionary use, creative writing, and impromptu speaking, and simultaneously develops the ability to evaluate definitions critically. (MM)

  8. Encoding Dictionaries.

    ERIC Educational Resources Information Center

    Ide, Nancy

    1995-01-01

    Describes problems in devising a Text Encoding Initiative (TEI) encoding format for dictionaries. Asserts that the high degree of structuring and compression of information are among the most complex text types treated in the TEI. Concludes that the source of some TEI problems lies in the design of Standard Generalized Markup Language (SGML). (CFR)

  9. Lushootseed Dictionary

    ERIC Educational Resources Information Center

    Bates, Dawn; Hess, Thom; Hilbert, Vi

    The dictionary of Lushootseed, the Puget Salish Indian language spoken in the area of Seattle, Washington, begins with an introduction to the language's name, dialects, geographic distribution, research methodology and native informants, texts used as sources, and pronunciation and transcription. It also gives an overview of the way entries are…

  10. Integrating Learning Styles into Adaptive E-Learning System

    ERIC Educational Resources Information Center

    Truong, Huong May

    2015-01-01

    This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students' learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between…

  11. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    SciTech Connect

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  12. Linguistic and Cultural Strategies in ELT Dictionaries

    ERIC Educational Resources Information Center

    Corrius, Montse; Pujol, Didac

    2010-01-01

    There are three main types of ELT dictionaries: monolingual, bilingual, and bilingualized. Each type of dictionary, while having its own advantages, also hinders the learning of English as a foreign language and culture in so far as it is written from a homogenizing (linguistic- and culture-centric) perspective. This paper presents a new type of…

  13. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

    NASA Astrophysics Data System (ADS)

    Diaz-Hernandez, R.; Ortiz-Esquivel, A.; Peregrina-Barreto, H.; Altamirano-Robles, L.; Gonzalez-Bernal, J.

    2016-06-01

    The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu's invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

  14. Building Vocabulary: Dictionary Consultation and the ESL Student.

    ERIC Educational Resources Information Center

    Gonzalez, Orsini

    1999-01-01

    Describes two activities (the use of a newspaper and consultation with a dictionary) that English-as-a-second language (ESL) college students can use to independently learn new vocabulary. Finds that dictionary work was laborious but necessary, and that ESL students need to be taught prudent use of the dictionary. (SR)

  15. Adaptive Educational Software by Applying Reinforcement Learning

    ERIC Educational Resources Information Center

    Bennane, Abdellah

    2013-01-01

    The introduction of the intelligence in teaching software is the object of this paper. In software elaboration process, one uses some learning techniques in order to adapt the teaching software to characteristics of student. Generally, one uses the artificial intelligence techniques like reinforcement learning, Bayesian network in order to adapt…

  16. Different Futures of Adaptive Collaborative Learning Support

    ERIC Educational Resources Information Center

    Rummel, Nikol; Walker, Erin; Aleven, Vincent

    2016-01-01

    In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that--due to better-designed technology, grounded in research--avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years…

  17. Animal social learning: associations and adaptations.

    PubMed

    Reader, Simon M

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on 'ordinary' processes but with extraordinary consequences. PMID:27635227

  18. Animal social learning: associations and adaptations

    PubMed Central

    Reader, Simon M.

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on ‘ordinary’ processes but with extraordinary consequences. PMID:27635227

  19. Animal social learning: associations and adaptations

    PubMed Central

    Reader, Simon M.

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on ‘ordinary’ processes but with extraordinary consequences.

  20. Dictionary of Microscopy

    NASA Astrophysics Data System (ADS)

    Heath, Julian

    2005-10-01

    The past decade has seen huge advances in the application of microscopy in all areas of science. This welcome development in microscopy has been paralleled by an expansion of the vocabulary of technical terms used in microscopy: terms have been coined for new instruments and techniques and, as microscopes reach even higher resolution, the use of terms that relate to the optical and physical principles underpinning microscopy is now commonplace. The Dictionary of Microscopy was compiled to meet this challenge and provides concise definitions of over 2,500 terms used in the fields of light microscopy, electron microscopy, scanning probe microscopy, x-ray microscopy and related techniques. Written by Dr Julian P. Heath, Editor of Microscopy and Analysis, the dictionary is intended to provide easy navigation through the microscopy terminology and to be a first point of reference for definitions of new and established terms. The Dictionary of Microscopy is an essential, accessible resource for: students who are new to the field and are learning about microscopes equipment purchasers who want an explanation of the terms used in manufacturers' literature scientists who are considering using a new microscopical technique experienced microscopists as an aide mémoire or quick source of reference librarians, the press and marketing personnel who require definitions for technical reports.

  1. Adaptations to a Learning Resource

    ERIC Educational Resources Information Center

    Libbrecht, Paul

    2015-01-01

    Learning resources have been created to represent digital units of exchangeable materials that teachers and learners can pull from in order to support the learning processes. They resource themselves. Leveraging the web, one can often find these resources. But what characteristics do they need in order to be easily exchangeable? Although several…

  2. Flexible Ubiquitous Learning Management System Adapted to Learning Context

    NASA Astrophysics Data System (ADS)

    Jeong, Ji-Seong; Kim, Mihye; Park, Chan; Yoo, Jae-Soo; Yoo, Kwan-Hee

    This paper proposes a u-learning management system (ULMS) appropriate to the ubiquitous learning environment, with emphasis on the significance of context awareness and adaptation in learning. The proposed system supports the basic functions of an e-learning management system and incorporates a number of tools and additional features to provide a more customized learning service. The proposed system automatically corresponds to various forms of user terminal without modifying the existing system. The functions, formats, and course learning activities of the system are dynamically and adaptively constructed at runtime according to user terminals, course types, pedagogical goals as well as student characteristics and learning context. A prototype for university use has been implemented to demonstrate and evaluate the proposed approach. We regard the proposed ULMS as an ideal u-learning system because it can not only lead students into continuous and mobile 'anytime, anywhere' learning using any kind of terminal, but can also foster enhanced self-directed learning through the establishment of an adaptive learning environment.

  3. Exploring Adaptability through Learning Layers and Learning Loops

    ERIC Educational Resources Information Center

    Lof, Annette

    2010-01-01

    Adaptability in social-ecological systems results from individual and collective action, and multi-level interactions. It can be understood in a dual sense as a system's ability to adapt to disturbance and change, and to navigate system transformation. Inherent in this conception, as found in resilience thinking, are the concepts of learning and…

  4. Students Working with an English Learners' Dictionary on CD-ROM.

    ERIC Educational Resources Information Center

    Winkler, Birgit

    This paper examines the growing literature on pedagogical lexicography and the growing focus on how well the learner uses the dictionary in second language learning. Dictionaries are becoming more user-friendly. This study used the writing task to reveal new insights into how students use a CD-ROM dictionary. It found a lack of dictionary-using…

  5. Upholstery Dictionary 2. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this second of three picture dictionary booklets in the Upholstery series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  6. Brain aerobic glycolysis and motor adaptation learning

    PubMed Central

    Shannon, Benjamin J.; Vaishnavi, Sanjeev Neil; Vlassenko, Andrei G.; Shimony, Joshua S.; Rutlin, Jerrel; Raichle, Marcus E.

    2016-01-01

    Ten percent to 15% of glucose used by the brain is metabolized nonoxidatively despite adequate tissue oxygenation, a process termed aerobic glycolysis (AG). Because of the known role of glycolysis in biosynthesis, we tested whether learning-induced synaptic plasticity would lead to regionally appropriate, learning-dependent changes in AG. Functional MRI (fMRI) before, during, and after performance of a visual–motor adaptation task demonstrated that left Brodmann area 44 (BA44) played a key role in adaptation, with learning-related changes to activity during the task and altered resting-state, functional connectivity after the task. PET scans before and after task performance indicated a sustained increase in AG in left BA 44 accompanied by decreased oxygen consumption. Intersubject variability in behavioral adaptation rate correlated strongly with changes in AG in this region, as well as functional connectivity, which is consistent with a role for AG in synaptic plasticity. PMID:27217563

  7. Adapting Active Learning in Ethiopia

    ERIC Educational Resources Information Center

    Casale, Carolyn Frances

    2010-01-01

    Ethiopia is a developing country that has invested extensively in expanding its educational opportunities. In this expansion, there has been a drastic restructuring of its system of preparing teachers and teacher educators. Often, improving teacher quality is dependent on professional development that diversifies pedagogy (active learning). This…

  8. Image fusion using sparse overcomplete feature dictionaries

    DOEpatents

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

    2015-10-06

    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  9. Learning and adaptation in fuzzy neural systems

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.

    1992-03-01

    In recent years, an increasing number of researchers have become involved in the subject of fuzzy neural networks in the hope of combining the reasoning strength of fuzzy logic and the learning and adaptation power of neural networks. This provides a more powerful tool for fuzzy information processing and for exploring the functioning of human brains. In this paper, an attempt has been made to establish some basic models for fuzzy neurons. First, several possible fuzzy neuron models are proposed. Second, synaptic and somatic learning and adaptation mechanisms are proposed. Finally, the possibility of applying nonfuzzy neural networks approaches to fuzzy systems is also described.

  10. Adaptive Cognitive-Based Selection of Learning Objects

    ERIC Educational Resources Information Center

    Karampiperis, Pythagoras; Lin, Taiyu; Sampson, Demetrios G.; Kinshuk

    2006-01-01

    Adaptive cognitive-based selection is recognized as among the most significant open issues in adaptive web-based learning systems. In order to adaptively select learning resources, the definition of adaptation rules according to the cognitive style or learning preferences of the learners is required. Although some efforts have been reported in…

  11. The Latent Structure of Dictionaries.

    PubMed

    Vincent-Lamarre, Philippe; Massé, Alexandre Blondin; Lopes, Marcos; Lord, Mélanie; Marcotte, Odile; Harnad, Stevan

    2016-07-01

    How many words-and which ones-are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a "Strongly Connected Subset" of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest-the graph's "minimum feedback vertex set" or MinSet-is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. PMID:27424842

  12. Adaptive Learning Resources Sequencing in Educational Hypermedia Systems

    ERIC Educational Resources Information Center

    Karampiperis, Pythagoras; Sampson, Demetrios

    2005-01-01

    Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have…

  13. Adapting Cooperative Learning in Tertiary ELT

    ERIC Educational Resources Information Center

    Ning, Huiping

    2011-01-01

    An updated guideline for tertiary ELT in China has shifted the emphasis to the development of learners' ability to communicate in English. Using group work and getting learners actively involved in the actual use of English are highlighted more than before. This article focuses on adapting cooperative learning methods for ELT with tertiary…

  14. Making Mistakes: Emotional Adaptation and Classroom Learning

    ERIC Educational Resources Information Center

    McCaslin, Mary; Vriesema, Christine C.; Burggraf, Susan

    2016-01-01

    Background: We studied how students in Grades 4-6 participate in and emotionally adapt to the give-and-take of learning in classrooms, particularly when making mistakes. Our approach is consistent with researchers who (a) include cognitive appraisals in the study of emotional experiences, (b) consider how personal concerns might mediate…

  15. Adaptable Learning Assistant for Item Bank Management

    ERIC Educational Resources Information Center

    Nuntiyagul, Atorn; Naruedomkul, Kanlaya; Cercone, Nick; Wongsawang, Damras

    2008-01-01

    We present PKIP, an adaptable learning assistant tool for managing question items in item banks. PKIP is not only able to automatically assist educational users to categorize the question items into predefined categories by their contents but also to correctly retrieve the items by specifying the category and/or the difficulty level. PKIP adapts…

  16. Intertwining thesauri and dictionaries

    NASA Technical Reports Server (NTRS)

    Buchan, R. L.

    1989-01-01

    The use of dictionaries and thesauri in information retrieval is discussed. The structure and functions of thesauri and dictionaries are described. Particular attention is given to the format of the NASA Thesaurus. The relationship between thesauri and dictionaries, the need to regularize terminology, and the capitalization of words are examined.

  17. Spanish Special Purpose Dictionaries.

    ERIC Educational Resources Information Center

    Champion, James J.

    1982-01-01

    A variety of special purpose Spanish dictionaries available for students of Spanish are described, including dictionaries of groupings of associated words, technical language, regional and slang language, single authors' usage, historical periods, etymology, frequency, and reverse organization. Several illustrations of dictionary organization are…

  18. Intelligent robots that adapt, learn, and predict

    NASA Astrophysics Data System (ADS)

    Hall, E. L.; Liao, X.; Ghaffari, M.; Alhaj Ali, S. M.

    2005-10-01

    The purpose of this paper is to describe the concept and architecture for an intelligent robot system that can adapt, learn and predict the future. This evolutionary approach to the design of intelligent robots is the result of several years of study on the design of intelligent machines that could adapt using computer vision or other sensory inputs, learn using artificial neural networks or genetic algorithms, exhibit semiotic closure with a creative controller and perceive present situations by interpretation of visual and voice commands. This information processing would then permit the robot to predict the future and plan its actions accordingly. In this paper we show that the capability to adapt, and learn naturally leads to the ability to predict the future state of the environment which is just another form of semiotic closure. That is, predicting a future state without knowledge of the future is similar to making a present action without knowledge of the present state. The theory will be illustrated by considering the situation of guiding a mobile robot through an unstructured environment for a rescue operation. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots.

  19. Improving Adaptive Learning Technology through the Use of Response Times

    ERIC Educational Resources Information Center

    Mettler, Everett; Massey, Christine M.; Kellman, Philip J.

    2011-01-01

    Adaptive learning techniques have typically scheduled practice using learners' accuracy and item presentation history. We describe an adaptive learning system (Adaptive Response Time Based Sequencing--ARTS) that uses both accuracy and response time (RT) as direct inputs into sequencing. Response times are used to assess learning strength and…

  20. Adaptive functional systems: Learning with chaos

    NASA Astrophysics Data System (ADS)

    Komarov, M. A.; Osipov, G. V.; Burtsev, M. S.

    2010-12-01

    We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations.

  1. An Electronic Dictionary and Translation System for Murrinh-Patha

    ERIC Educational Resources Information Center

    Seiss, Melanie; Nordlinger, Rachel

    2012-01-01

    This paper presents an electronic dictionary and translation system for the Australian language Murrinh-Patha. Its complex verbal structure makes learning Murrinh-Patha very difficult. Design learning materials or a dictionary which is easy to understand and to use also presents a challenge. This paper discusses some of the difficulties posed by…

  2. MEAT: An Authoring Tool for Generating Adaptable Learning Resources

    ERIC Educational Resources Information Center

    Kuo, Yen-Hung; Huang, Yueh-Min

    2009-01-01

    Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

  3. Yet Another Adaptive Learning Management System Based on Felder and Silverman's Learning Styles and Mashup

    ERIC Educational Resources Information Center

    Chang, Yi-Hsing; Chen, Yen-Yi; Chen, Nian-Shing; Lu, You-Te; Fang, Rong-Jyue

    2016-01-01

    This study designs and implements an adaptive learning management system based on Felder and Silverman's Learning Style Model and the Mashup technology. In this system, Felder and Silverman's Learning Style model is used to assess students' learning styles, in order to provide adaptive learning to leverage learners' learning preferences.…

  4. French Dictionaries. Series: Specialised Bibliographies.

    ERIC Educational Resources Information Center

    Klaar, R. M.

    This is a list of French monolingual, French-English and English-French dictionaries available in December 1975. Dictionaries of etymology, phonetics, place names, proper names, and slang are included, as well as dictionaries for children and dictionaries of Belgian, Canadian, and Swiss French. Most other specialized dictionaries, encyclopedias,…

  5. Using Assistive Technology Adaptations To Include Students with Learning Disabilities in Cooperative Learning Activities.

    ERIC Educational Resources Information Center

    Bryant, Diane Pedrotty; Bryant, Brian R.

    1998-01-01

    Discusses a process for integrating technology adaptations for students with learning disabilities into cooperative-learning activities in terms of three components: (1) selecting adaptations; (2) monitoring use of adaptations during cooperative-learning activities; and (3) evaluating the adaptations' effectiveness. Barriers to and support systems…

  6. Adaptable Learning Pathway Generation with Ant Colony Optimization

    ERIC Educational Resources Information Center

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2009-01-01

    One of the new major directions in research on web-based educational systems is the notion of adaptability: the educational system adapts itself to the learning profile, preferences and ability of the student. In this paper, we look into the issues of providing adaptability with respect to learning pathways. We explore the state of the art with…

  7. AH-Questionnaire: An Adaptive Hierarchical Questionnaire for Learning Styles

    ERIC Educational Resources Information Center

    Ortigosa, Alvaro; Paredes, Pedro; Rodriguez, Pilar

    2010-01-01

    One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the…

  8. Critical Thinking, Developmental Learning, and Adaptive Flexibility in Organizational Leaders.

    ERIC Educational Resources Information Center

    Duchesne, Robert E., Jr.

    A study examined how developmental learning and adaptive flexibility relate to critical thinking though a survey of 119 organizational leaders (of 341) who had attended a 5-day Leadership Development Program. A questionnaire adapted from the Center for Creative Leadership's Job Challenge Profile measured developmental learning, the Adaptive Style…

  9. Development of Adaptive Kanji Learning System for Mobile Phone

    ERIC Educational Resources Information Center

    Li, Mengmeng; Ogata, Hiroaki; Hou, Bin; Hashimoto, Satoshi; Liu, Yuqin; Uosaki, Noriko; Yano, Yoneo

    2010-01-01

    This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects:…

  10. How Language Supports Adaptive Teaching through a Responsive Learning Culture

    ERIC Educational Resources Information Center

    Johnston, Peter; Dozier, Cheryl; Smit, Julie

    2016-01-01

    For students to learn optimally, teachers must design classrooms that are responsive to the full range of student development. The teacher must be adaptive, but so must each student and the learning culture itself. In other words, adaptive teaching means constructing a responsive learning culture that accommodates and even capitalizes on diversity…

  11. The New Oxford Picture Dictionary, English/Navajo Edition.

    ERIC Educational Resources Information Center

    Parnwell, E. C.

    This picture dictionary illustrates over 2,400 words. The dictionary is organized thematically, beginning with topics most useful for the survival needs of students in an English speaking country. However, teachers may adapt the order to reflect the needs of their students. Verbs are included on separate pages, but within topic areas in which they…

  12. Bilingual Dictionary of Mathematical Terms: English--Chinese.

    ERIC Educational Resources Information Center

    New York State Education Dept., Albany. Bureau of Bilingual Education.

    The bilingual dictionary has been adapted from a dictionary originally developed by Teresa Kow and Euphine Cheung in New York to assist Chinese students in their understanding of mathematics vocabulary and concepts in English. A total of 204 terms and definitions are provided in English with Chinese translations directly below the entries. Each…

  13. Search by Fuzzy Inference in a Children's Dictionary

    ERIC Educational Resources Information Center

    St-Jacques, Claude; Barriere, Caroline

    2005-01-01

    This research aims at promoting the usage of an online children's dictionary within a context of reading comprehension and vocabulary acquisition. Inspired by document retrieval approaches developed in the area of information retrieval (IR) research, we adapt a particular IR strategy, based on fuzzy logic, to a search in the electronic dictionary.…

  14. The SMAP Dictionary Management System

    NASA Technical Reports Server (NTRS)

    Smith, Kevin A.; Swan, Christoper A.

    2014-01-01

    The Soil Moisture Active Passive (SMAP) Dictionary Management System is a web-based tool to develop and store a mission dictionary. A mission dictionary defines the interface between a ground system and a spacecraft. In recent years, mission dictionaries have grown in size and scope, making it difficult for engineers across multiple disciplines to coordinate the dictionary development effort. The Dictionary Management Systemaddresses these issues by placing all dictionary information in one place, taking advantage of the efficiencies inherent in co-locating what were once disparate dictionary development efforts.

  15. Paper, Electronic or Online? Different Dictionaries for Different Activities

    ERIC Educational Resources Information Center

    Pasfield-Neofitou, Sarah

    2009-01-01

    Despite research suggesting that teachers highly influence their students' knowledge and use of language learning resources such as dictionaries (Loucky, 2005; Yamane, 2006), it appears that dictionary selection and use is considered something to be dealt with outside the classroom. As a result, many students receive too little advice to be able…

  16. Ahtna Athabaskan Dictionary.

    ERIC Educational Resources Information Center

    Kari, James, Ed.

    This dictionary of Ahtna, a dialect of the Athabaskan language family, is the first to integrate all morphemes into a single alphabetically arranged section of main entries, with verbs arranged according to a theory of Ahtna (and Athabascan) verb theme categories. An introductory section details dictionary format conventions used, presents a brief…

  17. Dictionary of Marketing Terms.

    ERIC Educational Resources Information Center

    Everhardt, Richard M.

    A listing of words and definitions compiled from more than 10 college and high school textbooks are presented in this dictionary of marketing terms. Over 1,200 entries of terms used in retailing, wholesaling, economics, and investments are included. This dictionary was designed to aid both instructors and students to better understand the…

  18. Dictionary as Database.

    ERIC Educational Resources Information Center

    Painter, Derrick

    1996-01-01

    Discussion of dictionaries as databases focuses on the digitizing of The Oxford English dictionary (OED) and the use of Standard Generalized Mark-Up Language (SGML). Topics include the creation of a consortium to digitize the OED, document structure, relational databases, text forms, sequence, and discourse. (LRW)

  19. 1500 System Geometric Dictionary.

    ERIC Educational Resources Information Center

    Peloquin, Paul V.

    A general description is provided of the "geometric dictionary," a graphic display aid, used by the Computer-Assisted Instruction Laboratory at the Pennsylvania State University. The purpose of the description is to enable the reader to duplicate and use the dictionary on any cathode ray tube terminal of the IBM 1500 system. The major advantages…

  20. Adaptive and perceptual learning technologies in medical education and training.

    PubMed

    Kellman, Philip J

    2013-10-01

    Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains.

  1. The Creation of Learner-Centred Dictionaries for Endangered Languages: A Rotuman Example

    ERIC Educational Resources Information Center

    Vamarasi, M.

    2014-01-01

    This article examines the creation of dictionaries for endangered languages (ELs). Though each dictionary is uniquely prepared for its users, all dictionaries should be based on sound principles of vocabulary learning, including the importance of lexical chunks, as emphasised by Michael Lewis in his "Lexical Approach." Many of the…

  2. An Adaptive Scaffolding E-Learning System for Middle School Students' Physics Learning

    ERIC Educational Resources Information Center

    Chen, Ching-Huei

    2014-01-01

    This study presents a framework that utilizes cognitive and motivational aspects of learning to design an adaptive scaffolding e-learning system. It addresses scaffolding processes and conditions for designing adaptive scaffolds. The features and effectiveness of this adaptive scaffolding e-learning system are discussed and evaluated. An…

  3. Which Dictionary? A Review of the Leading Learners' Dictionaries.

    ERIC Educational Resources Information Center

    Nesi, Hilary

    Three major dictionaries designed for learners of English as a second language are reviewed, their elements and approaches compared and evaluated, their usefulness for different learners discussed, and recommendations for future dictionary improvement made. The dictionaries in question are the "Oxford Advanced Learner's Dictionary," the "Longman…

  4. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    ERIC Educational Resources Information Center

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  5. The ABCs of Data Dictionaries

    ERIC Educational Resources Information Center

    Gould, Tate; Nicholas, Amy; Blandford, William; Ruggiero, Tony; Peters, Mary; Thayer, Sara

    2014-01-01

    This overview of the basic components of a data dictionary is designed to educate and inform IDEA Part C and Part B 619 state staff about the purpose and benefits of having up-to-date data dictionaries for their data systems. This report discusses the following topics: (1) What Is a Data Dictionary?; (2) Why Is a Data Dictionary Needed and How Can…

  6. Do Dictionaries Help Students Write?

    ERIC Educational Resources Information Center

    Nesi, Hilary

    Examples are given of real lexical errors made by learner writers, and consideration is given to the way in which three learners' dictionaries could deal with the lexical items that were misused. The dictionaries were the "Oxford Advanced Learner's Dictionary," the "Longman Dictionary of Contemporary English," and the "Chambers Universal Learners'…

  7. The environmental dictionary

    SciTech Connect

    King, J.J.

    1989-01-01

    The Environmental Dictionary was designed for individuals researching environmental regulations in the Code of Federal Regulations, Title 40, Protection of the Environment (40 CFR). This book defines the highly technical and sometimes confusing terminology used throughout 40 CFR. Definitions are from two sources: (1) Code of Federal Regulations, Title 40, Protection of the Environment, Revised as of July 1, 1987, and (2) The Federal Register (updates pertaining to 40 CFR) for the period covering 1 July 1987 through 30 June 1989. The Dictionary contains numerous cross-referencing and finding aids. Beyond its use in interpreting Title 40 regulations, the Dictionary may be of value to individuals that are in need of a dictionary that covers the myriad of technical terms used in the environmental sciences including the biological, chemical, legal, and planning aspects.

  8. Navajo-English Dictionary.

    ERIC Educational Resources Information Center

    Wall, Leon; Morgan, William

    A brief summary of the sound system of the Navajo language introduces this Navajo-English dictionary. Diacritical markings and an English definition are given for each Navajo word. Words are listed alphabetically by Navajo sound. (VM)

  9. Adaptive Device Context Based Mobile Learning Systems

    ERIC Educational Resources Information Center

    Pu, Haitao; Lin, Jinjiao; Song, Yanwei; Liu, Fasheng

    2011-01-01

    Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for…

  10. Culinary Arts Dictionary 1. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this first of three picture dictionary booklets in the Culinary Arts series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  11. Culinary Arts Dictionary 2. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this second of three picture dictionary booklets in the Culinary Arts series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  12. Arc Welding Dictionary 2. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this second of three picture dictionary booklets in the Arc Welding series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  13. Arc Welding Dictionary 3. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this third of three picture dictionary booklets in the Arc Welding series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  14. Arc Welding Dictionary 1. Project HIRE.

    ERIC Educational Resources Information Center

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this first of three picture dictionary booklets in the Arc Welding series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  15. Topological structure of dictionary graphs

    NASA Astrophysics Data System (ADS)

    Fukś, Henryk; Krzemiński, Mark

    2009-09-01

    We investigate the topological structure of the subgraphs of dictionary graphs constructed from WordNet and Moby thesaurus data. In the process of learning a foreign language, the learner knows only a subset of all words of the language, corresponding to a subgraph of a dictionary graph. When this subgraph grows with time, its topological properties change. We introduce the notion of the pseudocore and argue that the growth of the vocabulary roughly follows decreasing pseudocore numbers—that is, one first learns words with a high pseudocore number followed by smaller pseudocores. We also propose an alternative strategy for vocabulary growth, involving decreasing core numbers as opposed to pseudocore numbers. We find that as the core or pseudocore grows in size, the clustering coefficient first decreases, then reaches a minimum and starts increasing again. The minimum occurs when the vocabulary reaches a size between 103 and 104. A simple model exhibiting similar behavior is proposed. The model is based on a generalized geometric random graph. Possible implications for language learning are discussed.

  16. The Influence of Learning Behaviour on Team Adaptability

    ERIC Educational Resources Information Center

    Murray, Peter A.; Millett, Bruce

    2011-01-01

    Multiple contexts shape team activities and how they learn, and group learning is a dynamic construct that reflects a repertoire of potential behaviour. The purpose of this developmental paper is to examine how better learning behaviours in semi-autonomous teams improves the level of team adaptability and performance. The discussion suggests that…

  17. An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

    ERIC Educational Resources Information Center

    Drissi, Samia; Amirat, Abdelkrim

    2016-01-01

    Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

  18. How to Represent Adaptation in e-Learning with IMS Learning Design

    ERIC Educational Resources Information Center

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2007-01-01

    Adaptation in e-learning has been an important research topic for the last few decades in computer-based education. In adaptivity the behaviour of the user triggers some actions in the system that guides the learning process. In adaptability, the user makes changes and takes decisions. Progressing from computer-based training and adaptive…

  19. A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning

    ERIC Educational Resources Information Center

    Hsu, Wei-Chih; Li, Cheng-Hsiu

    2015-01-01

    This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…

  20. A Framework for Adaptive E-Learning Based on Distributed Re-Usable Learning Activities.

    ERIC Educational Resources Information Center

    Brusilovsky, Peter; Nijhavan, Hemanta

    This paper suggests that a way to the new generation of powerful E-learning systems starts on the crossroads of two emerging fields: courseware re-use and adaptive educational systems. The paper presents the KnowledgeTree, a framework for adaptive E-learning based on distributed re-usable learning activities currently under development. The goal…

  1. Diminished neural adaptation during implicit learning in autism.

    PubMed

    Schipul, Sarah E; Just, Marcel Adam

    2016-01-15

    Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups' brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD.

  2. A Model of Adaptive Language Learning

    ERIC Educational Resources Information Center

    Woodrow, Lindy J.

    2006-01-01

    This study applies theorizing from educational psychology and language learning to hypothesize a model of language learning that takes into account affect, motivation, and language learning strategies. The study employed a questionnaire to assess variables of motivation, self-efficacy, anxiety, and language learning strategies. The sample…

  3. Fair-view image reconstruction with dual dictionaries.

    PubMed

    Lu, Yang; Zhao, Jun; Wang, Ge

    2012-01-01

    In this paper, we formulate the problem of computed tomography (CT)under sparsity and few-view constraints, and propose a novel algorithm for image reconstruction from few-view data utilizing the simultaneous algebraic reconstruction technique (SART) coupled with dictionary learning, sparse representation and total variation (TV) minimization on two interconnected levels. The main feature of our algorithm is the use of two dictionaries: a transitional dictionary for atom matching and a global dictionary for image updating. The atoms in the global and transitional dictionaries represent the image patches from high-quality and low-quality CT images, respectively.Experiments with simulated and real projections were performed to evaluate and validate the proposed algorithm. The results reconstructed using the proposed approach are significantly better than those using either SART or SART–TV.

  4. Few-view image reconstruction with dual dictionaries

    PubMed Central

    Lu, Yang; Zhao, Jun; Wang, Ge

    2011-01-01

    In this paper, we formulate the problem of computed tomography (CT) under sparsity and few-view constraints, and propose a novel algorithm for image reconstruction from few-view data utilizing the simultaneous algebraic reconstruction technique (SART) coupled with dictionary learning, sparse representation and total variation (TV) minimization on two interconnected levels. The main feature of our algorithm is the use of two dictionaries: a transitional dictionary for atom matching and a global dictionary for image updating. The atoms in the global and transitional dictionaries represent the image patches from high-quality and low-quality CT images, respectively. Experiments with simulated and real projections were performed to evaluate and validate the proposed algorithm. The results reconstructed using the proposed approach are significantly better than those using either SART or SART–TV. PMID:22155989

  5. Dictionaries: British and American. The Language Library.

    ERIC Educational Resources Information Center

    Hulbert, James Root

    An account of the dictionaries, great and small, of the English-speaking world is given in this book. Subjects covered include the origin of English dictionaries, early dictionaries, Noah Webster and his successors to the present, abridged dictionaries, "The Oxford English Dictionary" and later dictionaries patterned after it, the process of…

  6. An Intelligent E-Learning System Based on Learner Profiling and Learning Resources Adaptation

    ERIC Educational Resources Information Center

    Tzouveli, Paraskevi; Mylonas, Phivos; Kollias, Stefanos

    2008-01-01

    Taking advantage of the continuously improving, web-based learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, learning systems do not generally adapt to learners' profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their knowledge…

  7. A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems

    ERIC Educational Resources Information Center

    Hwang, Gwo-Jen; Sung, Han-Yu; Hung, Chun-Ming; Huang, Iwen

    2013-01-01

    Learning styles are considered to be one of the factors that need to be taken into account in developing adaptive learning systems. However, few studies have been conducted to investigate if students have the ability to choose the best-fit e-learning systems or content presentation styles for themselves in terms of learning style perspective. In…

  8. Learning Experiences Reuse Based on an Ontology Modeling to Improve Adaptation in E-Learning Systems

    ERIC Educational Resources Information Center

    Hadj M'tir, Riadh; Rumpler, Béatrice; Jeribi, Lobna; Ben Ghezala, Henda

    2014-01-01

    Current trends in e-Learning focus mainly on personalizing and adapting the learning environment and learning process. Although their increasingly number, theses researches often ignore the concepts of capitalization and reuse of learner experiences which can be exploited later by other learners. Thus, the major challenge of distance learning is…

  9. Oxford dictionary of Physics

    NASA Astrophysics Data System (ADS)

    Isaacs, Alan

    The dictionary is derived from the Concise Science Dictionary, first published by Oxford University Press in 1984 (third edition, 1996). It consists of all the entries relating to physics in that dictionary, together with some of those entries relating to astronomy that are required for an understanding of astrophysics and many entries that relate to physical chemistry. It also contains a selection of the words used in mathematics that are relevant to physics, as well as the key words in metal science, computing, and electronics. For this third edition a number of words from quantum field physics and statistical mechanics have been added. Cosmology and particle physics have been updated and a number of general entries have been expanded.

  10. Adaptive strategies for cumulative cultural learning.

    PubMed

    Ehn, Micael; Laland, Kevin

    2012-05-21

    The demographic and ecological success of our species is frequently attributed to our capacity for cumulative culture. However, it is not yet known how humans combine social and asocial learning to generate effective strategies for learning in a cumulative cultural context. Here we explore how cumulative culture influences the relative merits of various pure and conditional learning strategies, including pure asocial and social learning, critical social learning, conditional social learning and individual refiner strategies. We replicate the Rogers' paradox in the cumulative setting. However, our analysis suggests that strategies that resolved Rogers' paradox in a non-cumulative setting may not necessarily evolve in a cumulative setting, thus different strategies will optimize cumulative and non-cumulative cultural learning.

  11. Enhancing Student Motivation and Learning within Adaptive Tutors

    ERIC Educational Resources Information Center

    Ostrow, Korinn S.

    2015-01-01

    My research is rooted in improving K-12 educational practice using motivational facets made possible through adaptive tutoring systems. In an attempt to isolate best practices within the science of learning, I conduct randomized controlled trials within ASSISTments, an online adaptive tutoring system that provides assistance and assessment to…

  12. Implementation of an Adaptive Learning System Using a Bayesian Network

    ERIC Educational Resources Information Center

    Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki

    2015-01-01

    An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…

  13. Integrating Adaptive Games in Student-Centered Virtual Learning Environments

    ERIC Educational Resources Information Center

    del Blanco, Angel; Torrente, Javier; Moreno-Ger, Pablo; Fernandez-Manjon, Baltasar

    2010-01-01

    The increasing adoption of e-Learning technology is facing new challenges, such as how to produce student-centered systems that can be adapted to each student's needs. In this context, educational video games are proposed as an ideal medium to facilitate adaptation and tracking of students' performance for assessment purposes, but integrating the…

  14. MEANING DISCRIMINATION IN BILINGUAL DICTIONARIES.

    ERIC Educational Resources Information Center

    IANNUCCI, JAMES E.

    SEMANTIC DISCRIMINATION OF POLYSEMOUS ENTRY WORDS IN BILINGUAL DICTIONARIES WAS DISCUSSED IN THE PAPER. HANDICAPS OF PRESENT BILINGUAL DICTIONARIES AND BARRIERS TO THEIR FULL UTILIZATION WERE ENUMERATED. THE AUTHOR CONCLUDED THAT (1) A BILINGUAL DICTIONARY SHOULD HAVE A DISCRIMINATION FOR EVERY TRANSLATION OF AN ENTRY WORD WHICH HAS SEVERAL…

  15. An English-Panjabi Dictionary.

    ERIC Educational Resources Information Center

    Bailey, T. Grahame

    This English-Panjabi dictionary was compiled from about 5800 English words and expressions. Not intended to be exhaustive, the dictionary contains expressions that are considered useful to Europeans for communication with the Panjabis. It may also be useful to Panjabi students in their use of English. In addition to the dictionary proper, an…

  16. Mr. Stockdale's Dictionary of Collocations.

    ERIC Educational Resources Information Center

    Stockdale, Joseph Gagen, III

    This dictionary of collocations was compiled by an English-as-a-Second-Language (ESL) teacher in Saudi Arabia who teaches adult, native speakers of Arabic. The dictionary is practical in teaching English because it helps to focus on everyday events and situations. The dictionary works as follows: the teacher looks up a word, such as "talk"; next…

  17. Incidental Vocabulary Learning and Recall by Intermediate Foreign Language Students: The Influence of Marginal Glosses, Dictionary Use, and Summary Writing

    ERIC Educational Resources Information Center

    Ghabanchi, Zargham; Ayoubi, Elham Sadat

    2012-01-01

    This study is an attempt to compare the effect of four reading conditions on incidental vocabulary learning and recall of intermediate EFL learners. A sample population of 120 Iranian intermediate students read two short passages in one of four reading conditions: 1) L1 Marginal Glosses (MG1--provision of L1 translations of unknown words), 2) L2…

  18. Combining Adaptive Hypermedia with Project and Case-Based Learning

    ERIC Educational Resources Information Center

    Papanikolaou, Kyparisia; Grigoriadou, Maria

    2009-01-01

    In this article we investigate the design of educational hypermedia based on constructivist learning theories. According to the principles of project and case-based learning we present the design rational of an Adaptive Educational Hypermedia system prototype named MyProject; learners working with MyProject undertake a project and the system…

  19. Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

    ERIC Educational Resources Information Center

    Kelly, Declan

    2008-01-01

    Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

  20. Adaptive E-Learning Environments: Research Dimensions and Technological Approaches

    ERIC Educational Resources Information Center

    Di Bitonto, Pierpaolo; Roselli, Teresa; Rossano, Veronica; Sinatra, Maria

    2013-01-01

    One of the most closely investigated topics in e-learning research has always been the effectiveness of adaptive learning environments. The technological evolutions that have dramatically changed the educational world in the last six decades have allowed ever more advanced and smarter solutions to be proposed. The focus of this paper is to depict…

  1. RASCAL: A Rudimentary Adaptive System for Computer-Aided Learning.

    ERIC Educational Resources Information Center

    Stewart, John Christopher

    Both the background of computer-assisted instruction (CAI) systems in general and the requirements of a computer-aided learning system which would be a reasonable assistant to a teacher are discussed. RASCAL (Rudimentary Adaptive System for Computer-Aided Learning) is a first attempt at defining a CAI system which would individualize the learning…

  2. Adaptation of the Patterns of Adaptive Learning Scales (PALS) to Turkish Students: Factorial Validity and Reliability

    ERIC Educational Resources Information Center

    Cikrikci-Demirtash, R. Nukhet

    2005-01-01

    The study presented in this article was conducted to determine psychometric features of scales for Turkish students by adapting the Patterns of Adaptive Learning Scales (PALS) developed by Midgley and others (2000) to the Turkish language in order to measure personal and classroom goal orientations. The scales were developed to test…

  3. Motor sequence learning and motor adaptation in primary cervical dystonia.

    PubMed

    Katschnig-Winter, Petra; Schwingenschuh, Petra; Davare, Marco; Sadnicka, Anna; Schmidt, Reinhold; Rothwell, John C; Bhatia, Kailash P; Edwards, Mark J

    2014-06-01

    Motor sequence learning and motor adaptation rely on overlapping circuits predominantly involving the basal ganglia and cerebellum. Given the importance of these brain regions to the pathophysiology of primary dystonia, and the previous finding of abnormal motor sequence learning in DYT1 gene carriers, we explored motor sequence learning and motor adaptation in patients with primary cervical dystonia. We recruited 12 patients with cervical dystonia and 11 healthy controls matched for age. Subjects used a joystick to move a cursor from a central starting point to radial targets as fast and accurately as possible. Using this device, we recorded baseline motor performance, motor sequence learning and a visuomotor adaptation task. Patients with cervical dystonia had a significantly higher peak velocity than controls. Baseline performance with random target presentation was otherwise normal. Patients and controls had similar levels of motor sequence learning and motor adaptation. Our patients had significantly higher peak velocity compared to controls, with similar movement times, implying a different performance strategy. The preservation of motor sequence learning in cervical dystonia patients contrasts with the previously observed deficit seen in patients with DYT1 gene mutations, supporting the hypothesis of differing pathophysiology in different forms of primary dystonia. Normal motor adaptation is an interesting finding. With our paradigm we did not find evidence that the previously documented cerebellar abnormalities in cervical dystonia have a behavioral correlate, and thus could be compensatory or reflect "contamination" rather than being directly pathological.

  4. A MARANAO DICTIONARY.

    ERIC Educational Resources Information Center

    MACARAYA, BATUA A.; MCKAUGHAN, HOWARD P.

    MARANAO, OF THE MALAYO-POLYNESIAN LANGUAGE GROUP, IS ONE OF THE EIGHT MAJOR LANGUAGES IN THE PHILIPPINES AND IS SPOKEN BY 400,000 MOSLEMS ON THE ISLAND OF MINDANAO. THIS DICTIONARY, BASED PRIMARILY ON THE SPOKEN FORM OF THE LANGUAGE, CONTAINS 18,000 MARANAO ENTRIES IN PART I AND THEIR ALPHABETIZED ENGLISH GLOSSES CROSS-REFERENCED IN PART II. THE…

  5. Computer Dictionary and Handbook.

    ERIC Educational Resources Information Center

    Sippl, Charles J.; Sippl, Charles P.

    Designed to provide a useful tool for the instruction and increased awareness of the growing number of computer users throughout the world, this edition of the dictionary was based on an information search which provided 22,000 separate definitions and concept explanations. Appended are an introduction to computer system principles and procedures,…

  6. Dictionary of Cotton

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Dictionary of Cotton has over 2,000 terms and definitions that were compiled by 33 researchers. It reflects the ongoing commitment of the International Cotton Advisory Committee, through its Technical Information Section, to the spread of knowledge about cotton to all those who have an interest ...

  7. An Arthurian Dictionary.

    ERIC Educational Resources Information Center

    Moorman, Charles; Moorman, Ruth

    Intended as a reference manual for students, this dictionary lists the characters, places, and topics connected with the legend of King Arthur from its first written appearances through Sir Thomas Malory's "Le Morte d'Arthur." The entries are generally limited to Celtic, French, German, and English materials and include characters and places that…

  8. The Primary Computer Dictionary.

    ERIC Educational Resources Information Center

    Girard, Suzanne; Willing, Kathlene

    Suitable for children from kindergarten to grade three, this dictionary is designed to introduce young children to computer terminology at a level that they will understand and find useful. It is also suitable for parents as a home resource, for library use, and as a handbook for teachers. The first sentence of each definition contains the kernel…

  9. A DICTIONARY OF SANGO.

    ERIC Educational Resources Information Center

    SAMARIN, WILLIAM J.; TABER, CHARLES R.

    THIS ENGLISH-SANGO DICTIONARY IS A COMPANION VOLUME TO "A GRAMMAR OF SANGO," ED 003 925. SANGO IS A TRADE LANGUAGE OF SUB-SAHARAN AFRICA AND IS A BRANCH OF THE NIGER-CONGO FAMILY. ILLUSTRATIVE EXAMPLES OF SANGO TERMS ARE USED AND MANY NOTES ARE ADDED TO MAKE UNDERSTANDING CLEARER. (JC)

  10. Holikachuk Noun Dictionary (Preliminary).

    ERIC Educational Resources Information Center

    Kari, James, Comp.; And Others

    This dictionary contains lists of nouns in the Holikachuk Athabaskan language as spoken by about twenty people, most of whom live in the village of Grayling, Alaska. The Holikachuk alphabet and sound system are presented. The nouns with English equivalents are listed according to the following categories: animals, fish, insects, birds, plants,…

  11. Dictionary of Black Culture.

    ERIC Educational Resources Information Center

    Baskin, Wade; Runes, Richard N.

    This dictionary is an encyclopedic survey of the cultural background and development of the black American, covering the basic issues, events, contributions and biographies germane to the subject. The author-compiler is Chairman of Classical Languages Department at Southeastern State College, Durant, Oklahoma. Richard Runes is practicing law as a…

  12. English-Cinyanja Dictionary.

    ERIC Educational Resources Information Center

    Zambesi Mission, Mitsidi (Malawi).

    This English-Chinyanja (Cinyanja) dictionary was prepared and published by the Zambesi Mission for use in Africa. Compilers of this volume were aided by Africans of Southern Angoniland and a Yao tribesman from the Blantyre district knowledgeable in Chinyanja. Spelling rules used are those of the United Translation Board, which has adopted…

  13. A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

    NASA Astrophysics Data System (ADS)

    Karimi, Davood; Ward, Rabab K.

    2016-03-01

    Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

  14. Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

    PubMed Central

    Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

    2015-01-01

    Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

  15. Evolutionary and adaptive learning in complex markets: a brief summary

    NASA Astrophysics Data System (ADS)

    Hommes, Cars H.

    2007-06-01

    We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

  16. Learning & retention in adaptive serious games.

    PubMed

    Bergeron, Bryan P

    2008-01-01

    Serious games are being actively explored as supplements to and, in some cases, replacement for traditional didactic lectures and computer-based instruction in venues ranging from medicine to the military. As part of an intelligent tutoring system (ITS) for nuclear event first responders, we designed and evaluated two serious games that were integrated with adaptive multimedia content. Results reveal that there was no decay in score six weeks following game-based training, which contrasts with results expected with traditional training. This study suggests that adaptive serious games may help integrate didactic content presented though conventional means.

  17. Teacher Adaptation to Open Learning Spaces

    ERIC Educational Resources Information Center

    Alterator, Scott; Deed, Craig

    2013-01-01

    The "open classroom" emerged as a reaction against the industrial-era enclosed and authoritarian classroom. Although contemporary school architecture continues to incorporate and express ideas of openness, more research is needed about how teachers adapt to new and different built contexts. Our purpose is to identify teacher reaction to…

  18. The immune system, adaptation, and machine learning

    NASA Astrophysics Data System (ADS)

    Farmer, J. Doyne; Packard, Norman H.; Perelson, Alan S.

    1986-10-01

    The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.

  19. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  20. Adaptive Politics, Social Learning, and Military Institutions.

    ERIC Educational Resources Information Center

    Bobrow, Davis B.

    Rates and forms of change in post-industrial societies will increasingly test the viability of democratic political systems. Social learning must become faster and more powerful as the deadline on political demands becomes shorter and the complexity and variety of demands become greater. The military can play an almost uniquely helpful role in…

  1. Professional Learning to Nurture Adaptive Teachers

    ERIC Educational Resources Information Center

    Lee, Kar-Tin

    2013-01-01

    This paper presents the findings of a study conducted in China to identify the potential benefits of incorporating robotics as an educational tool for 100 primary and 320 secondary school teachers of general technology. The Professional Learning Program was conducted from 2010-2013 in China. The major focus of the program was on the development…

  2. Women, Subjectivities and Learning to Be Adaptable

    ERIC Educational Resources Information Center

    Cavanagh, Jillian

    2010-01-01

    Purpose: The purpose of this paper is to advance understandings of the subjectivities that influence auxiliary-level female employees' work and learning experiences in general legal practice. Moreover, the aim is to maximise the opportunities for these workers. Design/methodology/approach: A broader critical ethnographic study investigated…

  3. Auditory-Perceptual Learning Improves Speech Motor Adaptation in Children

    PubMed Central

    Shiller, Douglas M.; Rochon, Marie-Lyne

    2015-01-01

    Auditory feedback plays an important role in children’s speech development by providing the child with information about speech outcomes that is used to learn and fine-tune speech motor plans. The use of auditory feedback in speech motor learning has been extensively studied in adults by examining oral motor responses to manipulations of auditory feedback during speech production. Children are also capable of adapting speech motor patterns to perceived changes in auditory feedback, however it is not known whether their capacity for motor learning is limited by immature auditory-perceptual abilities. Here, the link between speech perceptual ability and the capacity for motor learning was explored in two groups of 5–7-year-old children who underwent a period of auditory perceptual training followed by tests of speech motor adaptation to altered auditory feedback. One group received perceptual training on a speech acoustic property relevant to the motor task while a control group received perceptual training on an irrelevant speech contrast. Learned perceptual improvements led to an enhancement in speech motor adaptation (proportional to the perceptual change) only for the experimental group. The results indicate that children’s ability to perceive relevant speech acoustic properties has a direct influence on their capacity for sensory-based speech motor adaptation. PMID:24842067

  4. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies.

    PubMed

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-01-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people's adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics. PMID:27282089

  5. Auditory-perceptual learning improves speech motor adaptation in children.

    PubMed

    Shiller, Douglas M; Rochon, Marie-Lyne

    2014-08-01

    Auditory feedback plays an important role in children's speech development by providing the child with information about speech outcomes that is used to learn and fine-tune speech motor plans. The use of auditory feedback in speech motor learning has been extensively studied in adults by examining oral motor responses to manipulations of auditory feedback during speech production. Children are also capable of adapting speech motor patterns to perceived changes in auditory feedback; however, it is not known whether their capacity for motor learning is limited by immature auditory-perceptual abilities. Here, the link between speech perceptual ability and the capacity for motor learning was explored in two groups of 5- to 7-year-old children who underwent a period of auditory perceptual training followed by tests of speech motor adaptation to altered auditory feedback. One group received perceptual training on a speech acoustic property relevant to the motor task while a control group received perceptual training on an irrelevant speech contrast. Learned perceptual improvements led to an enhancement in speech motor adaptation (proportional to the perceptual change) only for the experimental group. The results indicate that children's ability to perceive relevant speech acoustic properties has a direct influence on their capacity for sensory-based speech motor adaptation.

  6. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    PubMed Central

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-01-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics. PMID:27282089

  7. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    NASA Astrophysics Data System (ADS)

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-06-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

  8. Generalized perceptual learning in the absence of sensory adaptation.

    PubMed

    Harris, Hila; Gliksberg, Michael; Sagi, Dov

    2012-10-01

    Repeated performance of visual tasks leads to long-lasting increased sensitivity to the trained stimulus, a phenomenon termed perceptual learning. A ubiquitous property of visual learning is specificity: performance improvement obtained during training applies only for the trained stimulus features, which are thought to be encoded in sensory brain regions [1-3]. However, recent results show performance decrements with an increasing number of trials within a training session [4, 5]. This selective sensitivity reduction is thought to arise due to sensory adaptation [5, 6]. Here we show, using the standard texture discrimination task [7], that location specificity is a consequence of sensory adaptation; that is, it results from selective reduced sensitivity due to repeated stimulation. Observers practiced the texture task with the target presented at a fixed location within a background texture. To remove adaptation, we added task-irrelevant ("dummy") trials with the texture oriented 45° relative to the target's orientation, known to counteract adaptation [8]. The results indicate location specificity with the standard paradigm, but complete generalization to a new location when adaptation is removed. We suggest that adaptation interferes with invariant pattern-discrimination learning by inducing network-dependent changes in local visual representations.

  9. Beyond adaptive-critic creative learning for intelligent mobile robots

    NASA Astrophysics Data System (ADS)

    Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

    2001-10-01

    Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it

  10. Data-Dictionary-Editing Program

    NASA Technical Reports Server (NTRS)

    Cumming, A. P.

    1989-01-01

    Access to data-dictionary relations and attributes made more convenient. Data Dictionary Editor (DDE) application program provides more convenient read/write access to data-dictionary table ("descriptions table") via data screen using SMARTQUERY function keys. Provides three main advantages: (1) User works with table names and field names rather than with table numbers and field numbers, (2) Provides online access to definitions of data-dictionary keys, and (3) Provides displayed summary list that shows, for each datum, which data-dictionary entries currently exist for any specific relation or attribute. Computer program developed to give developers of data bases more convenient access to the OMNIBASE VAX/IDM data-dictionary relations and attributes.

  11. Comparison of American medical dictionaries.

    PubMed Central

    Callard, J C; Fruehauf, E L

    1978-01-01

    Although American medical dictionaries are a valuable part of any medical library collection, the attributes of each of the four major dictionaries are often unknown and the reference material contained in each unused. The medical librarian should be aware of the differences and values of each dictionary and try to have at least one edition of each available to library users in order to maintain an adequate reference collection. PMID:354707

  12. The Making of the "Oxford English Dictionary."

    ERIC Educational Resources Information Center

    Winchester, Simon

    2003-01-01

    Summarizes remarks made to open the Gallaudet University conference on Dictionaries and the Standardization of languages. It concerns the making of what is arguably the world's greatest dictionary, "The Oxford English Dictionary." (VWL)

  13. Towards an Adaptive Multimedia Learning Environment: Enhancing the Student Experience.

    ERIC Educational Resources Information Center

    Kurzel, Frank; Slay, Jill; Rath, Michelle; Chau, Yenha

    This paper describes the development of an adaptive multimedia learning environment that utilizes multimedia presentation techniques in its interface while still providing Internet connectivity for management and delivery purposes. The system supports the WWW as its addressing space but uses the local client areas to store media items expensive in…

  14. Adaptive Learning in Psychology: Wayfinding in the Digital Age

    ERIC Educational Resources Information Center

    Dziuban, Charles D.; Moskal, Patsy D.; Cassisi, Jeffrey; Fawcett, Alexis

    2016-01-01

    This paper presents the results of a pilot study investigating the use of the Realizeit adaptive learning platform to deliver a fully online General Psychology course across two semesters. Through mutual cooperation, UCF and vendor (CCKF) researchers examined students' affective, behavioral, and cognitive reactions to the system. Student survey…

  15. Managing Adaptive Challenges: Learning with Principals in Bermuda and Florida

    ERIC Educational Resources Information Center

    Drago-Severson, Eleanor; Maslin-Ostrowski, Patricia; Hoffman, Alexander M.; Barbaro, Justin

    2014-01-01

    We interviewed eight principals from Bermuda and Florida about how they identify and manage their most pressing challenges. Their challenges are composed of both adaptive and technical work, requiring leaders to learn to diagnose and manage them. Challenges focused on change and were traced to accountability contexts, yet accountability was not…

  16. Adaptive Knowledge Management of Project-Based Learning

    ERIC Educational Resources Information Center

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

  17. Mispronunciation Detection for Language Learning and Speech Recognition Adaptation

    ERIC Educational Resources Information Center

    Ge, Zhenhao

    2013-01-01

    The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.…

  18. Amygdala-prefrontal interactions in (mal)adaptive learning.

    PubMed

    Likhtik, Ekaterina; Paz, Rony

    2015-03-01

    The study of neurobiological mechanisms underlying anxiety disorders has been shaped by learning models that frame anxiety as maladaptive learning. Pavlovian conditioning and extinction are particularly influential in defining learning stages that can account for symptoms of anxiety disorders. Recently, dynamic and task related communication between the basolateral complex of the amygdala (BLA) and the medial prefrontal cortex (mPFC) has emerged as a crucial aspect of successful evaluation of threat and safety. Ongoing patterns of neural signaling within the mPFC-BLA circuit during encoding, expression and extinction of adaptive learning are reviewed. The mechanisms whereby deficient mPFC-BLA interactions can lead to generalized fear and anxiety are discussed in learned and innate anxiety. Findings with cross-species validity are emphasized.

  19. Effects of dopaminergic therapy on locomotor adaptation and adaptive learning in persons with Parkinson's disease.

    PubMed

    Roemmich, Ryan T; Hack, Nawaz; Akbar, Umer; Hass, Chris J

    2014-07-15

    Persons with Parkinson's disease (PD) are characterized by multifactorial gait deficits, though the factors which influence the abilities of persons with PD to adapt and store new gait patterns are unclear. The purpose of this study was to investigate the effects of dopaminergic therapy on the abilities of persons with PD to adapt and store gait parameters during split-belt treadmill (SBT) walking. Ten participants with idiopathic PD who were being treated with stable doses of orally-administered dopaminergic therapy participated. All participants performed two randomized testing sessions on separate days: once while optimally-medicated (ON meds) and once after 12-h withdrawal from dopaminergic medication (OFF meds). During each session, locomotor adaptation was investigated as the participants walked on a SBT for 10 min while the belts moved at a 2:1 speed ratio. We assessed locomotor adaptive learning by quantifying: (1) aftereffects during de-adaptation (once the belts returned to tied speeds immediately following SBT walking) and (2) savings during re-adaptation (as the participants repeated the same SBT walking task after washout of aftereffects following the initial SBT task). The withholding of dopaminergic medication diminished step length aftereffects significantly during de-adaptation. However, both locomotor adaptation and savings were unaffected by levodopa. These findings suggest that dopaminergic pathways influence aftereffect storage but do not influence locomotor adaptation or savings within a single session of SBT walking. It appears important that persons with PD should be optimally-medicated if walking on the SBT as gait rehabilitation.

  20. Approach for Using Learner Satisfaction to Evaluate the Learning Adaptation Policy

    ERIC Educational Resources Information Center

    Jeghal, Adil; Oughdir, Lahcen; Tairi, Hamid; Radouane, Abdelhay

    2016-01-01

    The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze…

  1. Distributed adaptive simulation through standards-based integration of simulators and adaptive learning systems.

    PubMed

    Bergeron, Bryan; Cline, Andrew; Shipley, Jaime

    2012-01-01

    We have developed a distributed, standards-based architecture that enables simulation and simulator designers to leverage adaptive learning systems. Our approach, which incorporates an electronic competency record, open source LMS, and open source microcontroller hardware, is a low-cost, pragmatic option to integrating simulators with traditional courseware. PMID:22356955

  2. Introduction to project ALIAS: adaptive-learning image analysis system

    NASA Astrophysics Data System (ADS)

    Bock, Peter

    1992-03-01

    As an alternative to preprogrammed rule-based artificial intelligence, collective learning systems theory postulates a hierarchical network of cellular automata which acquire their knowledge through learning based on a series of trial-and-error interactions with an evaluating environment, much as humans do. The input to the hierarchical network is provided by a set of sensors which perceive the external world. Using both this perceived information and past experience (memory), the learning automata synthesize collections of trial responses, periodically modifying their memories based on internal evaluations or external evaluations from the environment. Based on collective learning systems theory, an adaptive transputer- based image-processing engine comprising a three-layer hierarchical network of 32 learning cells and 33 nonlearning cells has been applied to a difficult image processing task: the scale, phase, and translation-invariant detection of anomalous features in otherwise `normal' images. Known as adaptive learning image analysis system (ALIAS), this parallel-processing engine has been constructed and tested at the Research institute for Applied Knowledge Processing (FAW) in Ulm, Germany under the sponsorship of Robert Bosch GmbH. Results demonstrate excellent detection, discrimination, and localization of anomalies in binary images. Recent enhancements include the ability to process gray-scale images and the automatic supervised segmentation and classification of images. Current research is directed toward the processing of time-series data and the hierarchical extension of ALIAS from the sub-symbolic level to the higher levels of symbolic association.

  3. Review of EFL Learners' Habits in the Use of Pedagogical Dictionaries

    ERIC Educational Resources Information Center

    El-Sayed, Al-Nauman Al-Amin Ali; Siddiek, Ahmed Gumaa

    2013-01-01

    A dictionary is an important device for both: EFL teachers and EFL learners. It is highly needed to conduct effective teaching and learning. Many investigations were carried out to study the foreign language learners' habits in the use of their dictionaries in reading, writing, testing and translating. This paper is shedding light on this issue;…

  4. Usage in Contemporary American Dictionaries

    ERIC Educational Resources Information Center

    Creswell, Thomas J.

    1977-01-01

    An editorialized report of data accumulated in a study of current American practice in the treatment of problems of usage in general purpose dictionaries. Their descriptive objectivity is characterized as "that of the blind men examining the elephant." Such practices reveal little change from that of Samuel Johnson's 1755 English Dictionary. (AMH)

  5. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    ERIC Educational Resources Information Center

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  6. Peers as Resources for Learning: A Situated Learning Approach to Adapted Physical Activity in Rehabilitation

    ERIC Educational Resources Information Center

    Standal, Oyvind F.; Jespersen, Ejgil

    2008-01-01

    The purpose of this study was to investigate the learning that takes place when people with disabilities interact in a rehabilitation context. Data were generated through in-depth interviews and close observations in a 2 one-half week-long rehabilitation program, where the participants learned both wheelchair skills and adapted physical…

  7. Adaptive Web-Assisted Learning System for Students with Specific Learning Disabilities: A Needs Analysis Study

    ERIC Educational Resources Information Center

    Polat, Elif; Adiguzel, Tufan; Akgun, Ozcan Erkan

    2012-01-01

    Because there is, currently, no education system for primary school students in grades 1-3 who have specific learning disabilities in Turkey and because such students do not receive sufficient support from face-to-face counseling, a needs analysis was conducted in order to prepare an adaptive, web-assisted learning system according to variables…

  8. Building Adaptive Game-Based Learning Resources: The Integration of IMS Learning Design and

    ERIC Educational Resources Information Center

    Burgos, Daniel; Moreno-Ger, Pablo; Sierra, Jose Luis; Fernandez-Manjon, Baltasar; Specht, Marcus; Koper, Rob

    2008-01-01

    IMS Learning Design (IMS-LD) is a specification to create units of learning (UoLs), which express a certain pedagogical model or strategy (e.g., adaptive learning with games). However, the authoring process of a UoL remains difficult because of the lack of high-level authoring tools for IMS-LD, even more so when the focus is on specific topics,…

  9. Learning to speciate: The biased learning of mate preferences promotes adaptive radiation

    PubMed Central

    Gilman, R. Tucker; Kozak, Genevieve M.

    2015-01-01

    Bursts of rapid repeated speciation called adaptive radiations have generated much of Earth's biodiversity and fascinated biologists since Darwin, but we still do not know why some lineages radiate and others do not. Understanding what causes assortative mating to evolve rapidly and repeatedly in the same lineage is key to understanding adaptive radiation. Many species that have undergone adaptive radiations exhibit mate preference learning, where individuals acquire mate preferences by observing the phenotypes of other members of their populations. Mate preference learning can be biased if individuals also learn phenotypes to avoid in mates, and shift their preferences away from these avoided phenotypes. We used individual‐based computational simulations to study whether biased and unbiased mate preference learning promotes ecological speciation and adaptive radiation. We found that ecological speciation can be rapid and repeated when mate preferences are biased, but is inhibited when mate preferences are learned without bias. Our results suggest that biased mate preference learning may play an important role in generating animal biodiversity through adaptive radiation. PMID:26459795

  10. Learning to speciate: The biased learning of mate preferences promotes adaptive radiation.

    PubMed

    Gilman, R Tucker; Kozak, Genevieve M

    2015-11-01

    Bursts of rapid repeated speciation called adaptive radiations have generated much of Earth's biodiversity and fascinated biologists since Darwin, but we still do not know why some lineages radiate and others do not. Understanding what causes assortative mating to evolve rapidly and repeatedly in the same lineage is key to understanding adaptive radiation. Many species that have undergone adaptive radiations exhibit mate preference learning, where individuals acquire mate preferences by observing the phenotypes of other members of their populations. Mate preference learning can be biased if individuals also learn phenotypes to avoid in mates, and shift their preferences away from these avoided phenotypes. We used individual-based computational simulations to study whether biased and unbiased mate preference learning promotes ecological speciation and adaptive radiation. We found that ecological speciation can be rapid and repeated when mate preferences are biased, but is inhibited when mate preferences are learned without bias. Our results suggest that biased mate preference learning may play an important role in generating animal biodiversity through adaptive radiation.

  11. Adaptive distance metric learning for diffusion tensor image segmentation.

    PubMed

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  12. Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

    PubMed Central

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

  13. Learners' Perceptions and Illusions of Adaptivity in Computer-Based Learning Environments

    ERIC Educational Resources Information Center

    Vandewaetere, Mieke; Vandercruysse, Sylke; Clarebout, Geraldine

    2012-01-01

    Research on computer-based adaptive learning environments has shown exemplary growth. Although the mechanisms of effective adaptive instruction are unraveled systematically, little is known about the relative effect of learners' perceptions of adaptivity in adaptive learning environments. As previous research has demonstrated that the learners'…

  14. SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

    SciTech Connect

    Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D

    2014-06-01

    Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

  15. A Selective Bibliography of Finance Dictionaries.

    ERIC Educational Resources Information Center

    Ballard, Cheryl M.

    1997-01-01

    This study consists of four parts: (1) an analysis of business dictionaries; (2) a list of the dictionaries reviewed according to which audience (novice or advanced user) the dictionary can better serve; (3) a table listing the dictionaries and the percentage of the vocabulary list contained in each source; and (4) the vocabulary list used to aid…

  16. Language Standardization and Signed Language Dictionaries.

    ERIC Educational Resources Information Center

    Johnston, Trevor

    2003-01-01

    Discusses the issue of the use of signed language dictionaries in the standardization of signed languages with reference to the Australian Sign language (Auslan) dictionaries. Details the structure of the Auslan dictionaries and argues that bilingual, bidirectional dictionaries of this type must be produced if communities are to encourage language…

  17. Adaptivity in Game-Based Learning: A New Perspective on Story

    NASA Astrophysics Data System (ADS)

    Berger, Florian; Müller, Wolfgang

    Game-based learning as a novel form of e-learning still has issues in fundamental questions, the lack of a general model for adaptivity being one of them. Since adaptive techniques in traditional e-learning applications bear close similarity to certain interactive storytelling approaches, we propose a new notion of story as the joining element of arbitraty learning paths.

  18. Designing a Semantic Bliki System to Support Different Types of Knowledge and Adaptive Learning

    ERIC Educational Resources Information Center

    Huang, Shiu-Li; Yang, Chia-Wei

    2009-01-01

    Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one's learning performance. This study…

  19. Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

    ERIC Educational Resources Information Center

    Thalmann, Stefan

    2014-01-01

    Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

  20. Accessibility and Adaptability of Learning Objects: Responding to Metadata, Learning Patterns and Profiles of Needs and Preferences

    ERIC Educational Resources Information Center

    Green, Steve; Jones, Ray; Pearson, Elaine; Gkatzidou, Stavroula

    2006-01-01

    The case for learning patterns as a design method for accessible and adaptable learning objects is explored. Patterns and templates for the design of learning objects can be derived from successful existing learning resources. These patterns can then be reused in the design of new learning objects. We argue that by attending to criteria for reuse…

  1. Classic Classroom Activities: The Oxford Picture Dictionary Program.

    ERIC Educational Resources Information Center

    Weiss, Renee; Adelson-Goldstein, Jayme; Shapiro, Norma

    This teacher resource book offers over 100 reproducible communicative practice activities and 768 picture cards based on the vocabulary of the Oxford Picture Dictionary. Teacher's notes and instructions, including adaptations for multilevel classes, are provided. The activities book has up-to-date art and graphics, explaining over 3700 words. The…

  2. An adaptive online learning framework for practical breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Chu, Tianshu; Wang, Jie; Chen, Jiayu

    2016-03-01

    This paper presents an adaptive online learning (OL) framework for supporting clinical breast cancer (BC) diagnosis. Unlike traditional data mining, which trains a particular model from a fixed set of medical data, our framework offers robust OL models that can be updated adaptively according to new data sequences and newly discovered features. As a result, our framework can naturally learn to perform BC diagnosis using experts' opinions on sequential patient cases with cumulative clinical measurements. The framework integrates both supervised learning (SL) models for BC risk assessment and reinforcement learning (RL) models for decision-making of clinical measurements. In other words, online SL and RL interact with one another, and under a doctor's supervision, push the patient's diagnosis further. Furthermore, our framework can quickly update relevant model parameters based on current diagnosis information during the training process. Additionally, it can build flexible fitted models by integrating different model structures and plugging in the corresponding parameters during the prediction (or decision-making) process. Even when the feature space is extended, it can initialize the corresponding parameters and extend the existing model structure without loss of the cumulative knowledge. We evaluate the OL framework on real datasets from BCSC and WBC, and demonstrate that our SL models achieve accurate BC risk assessment from sequential data and incremental features. We also verify that the well-trained RL models provide promising measurement suggestions.

  3. Distributed reinforcement learning for adaptive and robust network intrusion response

    NASA Astrophysics Data System (ADS)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  4. Hazardous materials dictionary

    SciTech Connect

    Coleman, R.J.

    1987-01-01

    Parallel growth of the chemical industry of emergency response capabilities in the public and private sectors has created a new need for improved communications. A new vocabulary of important terms is emerging in each of the industries that transport, store and handle hazardous materials. This dictionary, representing a compilation of words and phrases from many relevant sources, will help document and standardize the nomenclature of hazardous materials. The authors have screened the technical discourse of the chemical, transportation, petroleum and medical fields, both governmental and private, to determine the most current expressions and their uses. The lexicographic goal has been to identify key terms, ambiguous and multiple meaning words, acronyms, symbols and even slang referring to hazardous materials reactions, storing and handling procedures.

  5. Dictionary of environmental quotations

    SciTech Connect

    Rodes, B.K.; Odell, R.

    1997-12-31

    Here are more than 3,700 quotations in 143 categories -- from Acid Rain to Zoos -- that provide a comprehensive collection of the wise and witty observations about the natural environment. The dictionary will delight, provoke, and inform readers. It is at once stimulating, entertaining, and enlightening, with quotations that provide a complete range of human thought about nature and the environment. Quotations have been drawn from a variety of documented sources, including poems, proverbs, slogans, radio, and television, congressional hearings, magazines, and newspapers. The authors of the quotes range from a philosopher in pre-Christian times to a contemporary economist, from a poet who speaks of forests to an engineer concerned with air pollutants.

  6. Performance & Emotion--A Study on Adaptive E-Learning Based on Visual/Verbal Learning Styles

    ERIC Educational Resources Information Center

    Beckmann, Jennifer; Bertel, Sven; Zander, Steffi

    2015-01-01

    Adaptive e-Learning systems are able to adjust to a user's learning needs, usually by user modeling or tracking progress. Such learner-adaptive behavior has rapidly become a hot topic for e-Learning, furthered in part by the recent rapid increase in the use of MOOCs (Massive Open Online Courses). A lack of general, individual, and situational data…

  7. Development of an Adaptive Learning System with Multiple Perspectives based on Students' Learning Styles and Cognitive Styles

    ERIC Educational Resources Information Center

    Yang, Tzu-Chi; Hwang, Gwo-Jen; Yang, Stephen Jen-Hwa

    2013-01-01

    In this study, an adaptive learning system is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive learning systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's learning style. An…

  8. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    PubMed Central

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  9. Medical Dictionary: MedlinePlus

    MedlinePlus

    ... of this page: https://medlineplus.gov/mplusdictionary.html Medical Dictionary To use the sharing features on this ... Search term GO GO Visit the tutorial, Understanding Medical Words You may also be interested in these ...

  10. Demons, Dictionaries, and Spelling Strategies.

    ERIC Educational Resources Information Center

    Dodd, Anne Wescott

    1988-01-01

    Suggests that personal spelling dictionaries and spelling error analysis are strategies which will help students improve their spelling more than memorizing lists of unrelated words from spelling books. (MS)

  11. NCI Dictionary of Genetics Terms

    Cancer.gov

    A dictionary of more than 150 genetics-related terms written for healthcare professionals, developed to support the comprehensive, evidence-based, peer-reviewed PDQ cancer genetics information summaries.

  12. English for Everyday Activities: A Picture Process Dictionary.

    ERIC Educational Resources Information Center

    Zwier, Lawrence J.

    These books are designed to help English-as-a-Second-Language (ESL) students learn the skills they need to communicate the step-by-step aspects of daily activities. Unlike most picture dictionaries, this is a verb-based multi-skills program that uses a student text with a clear and colorful pictorial detail as a starting point and focuses on the…

  13. An adaptive learning control system for large flexible structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.

    1985-01-01

    The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

  14. Adapted to explore: reinforcement learning in Autistic Spectrum Conditions.

    PubMed

    Yechiam, Eldad; Arshavsky, Olga; Shamay-Tsoory, Simone G; Yaniv, Shoshana; Aharon, Judith

    2010-03-01

    Recent studies have recorded a tendency of individuals with Autism Spectrum Conditions (ASC) to continually change their choices in repeated choice tasks. In the current study we examine if this finding implies that ASC individuals have a cognitive style that facilitates exploration and discovery. Six decision tasks were administered to adolescents with ASC and matched controls. Significant differences in shifting between choice options appeared in the Iowa Gambling task (Bechara, Damasio, Damasio, & Anderson, 1994). A formal cognitive modeling analysis demonstrated that for about half of the ASC participants the adaptation process did not conform to the standard reinforcement learning model. These individuals were only coarsely affected by choice-outcomes, and were more influenced by the exploratory value of choices, being attracted to previously un-explored alternatives. An examination of the five simpler decision tasks where the advantageous option was easier to determine showed no evidence of this pattern, suggesting that the shifting choice pattern is not an uncontrollable tendency independent of task outcomes. These findings suggest that ASC individuals have a unique adaptive learning style, which may be beneficial is some learning environment but maladaptive in others, particularly in social contexts. PMID:19913345

  15. Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

    PubMed Central

    Xu, Yunfei; Choi, Jongeun

    2011-01-01

    This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme. PMID:22163785

  16. Improving Voluntary Environmental Management Programs: Facilitating Learning and Adaptation

    NASA Astrophysics Data System (ADS)

    Genskow, Kenneth D.; Wood, Danielle M.

    2011-05-01

    Environmental planners and managers face unique challenges understanding and documenting the effectiveness of programs that rely on voluntary actions by private landowners. Programs, such as those aimed at reducing nonpoint source pollution or improving habitat, intend to reach those goals by persuading landowners to adopt behaviors and management practices consistent with environmental restoration and protection. Our purpose with this paper is to identify barriers for improving voluntary environmental management programs and ways to overcome them. We first draw upon insights regarding data, learning, and adaptation from the adaptive management and performance management literatures, describing three key issues: overcoming information constraints, structural limitations, and organizational culture. Although these lessons are applicable to a variety of voluntary environmental management programs, we then present the issues in the context of on-going research for nonpoint source water quality pollution. We end the discussion by highlighting important elements for advancing voluntary program efforts.

  17. Towards Motivation-Based Adaptation of Difficulty in E-Learning Programs

    ERIC Educational Resources Information Center

    Endler, Anke; Rey, Gunter Daniel; Butz, Martin V.

    2012-01-01

    The objective of this study was to investigate if an e-learning environment may use measurements of the user's current motivation to adapt the level of task difficulty for more effective learning. In the reported study, motivation-based adaptation was applied randomly to collect a wide range of data for different adaptations in a variety of…

  18. The Emotions of Socialization-Related Learning: Understanding Workplace Adaptation as a Learning Process.

    ERIC Educational Resources Information Center

    Reio, Thomas G., Jr.

    The influence of selected discrete emotions on socialization-related learning and perception of workplace adaptation was examined in an exploratory study. Data were collected from 233 service workers in 4 small and medium-sized companies in metropolitan Washington, D.C. The sample members' average age was 32.5 years, and the sample's racial makeup…

  19. The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

    NASA Astrophysics Data System (ADS)

    Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

    Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

  20. The Influence of Student Characteristics on the Use of Adaptive E-Learning Material

    ERIC Educational Resources Information Center

    van Seters, J. R.; Ossevoort, M. A.; Tramper, J.; Goedhart, M. J.

    2012-01-01

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting…

  1. Embedded sparse representation of fMRI data via group-wise dictionary optimization

    NASA Astrophysics Data System (ADS)

    Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

    2016-03-01

    Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

  2. Sparse regularization for force identification using dictionaries

    NASA Astrophysics Data System (ADS)

    Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

    2016-04-01

    The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

  3. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2016-01-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

  4. Developing a distributed data dictionary service

    NASA Technical Reports Server (NTRS)

    U'Ren, J.

    2000-01-01

    This paper will explore the use of the Lightweight Directory Access Protocol (LDAP) using the ISO 11179 Data Dictionary Schema as a mechanism for standardizing the structure and communication links between data dictionaries.

  5. Peers as resources for learning: a situated learning approach to adapted physical activity in rehabilitation.

    PubMed

    Standal, Øyvind F; Jespersen, Ejgil

    2008-07-01

    The purpose of this study was to investigate the learning that takes place when people with disabilities interact in a rehabilitation context. Data were generated through in-depth interviews and close observations in a 2 (1/2) week-long rehabilitation program, where the participants learned both wheelchair skills and adapted physical activities. The findings from the qualitative data analysis are discussed in the context of situated learning (Lave & Wenger, 1991; Wenger, 1998). The results indicate that peer learning extends beyond skills and techniques, to include ways for the participants to make sense of their situations as wheelchair users. Also, it was found that the community of practice established between the participants represented a critical corrective to instructions provided by rehabilitation professionals.

  6. Adaptive Resonance Theory: how a brain learns to consciously attend, learn, and recognize a changing world.

    PubMed

    Grossberg, Stephen

    2013-01-01

    Adaptive Resonance Theory, or ART, is a cognitive and neural theory of how the brain autonomously learns to categorize, recognize, and predict objects and events in a changing world. This article reviews classical and recent developments of ART, and provides a synthesis of concepts, principles, mechanisms, architectures, and the interdisciplinary data bases that they have helped to explain and predict. The review illustrates that ART is currently the most highly developed cognitive and neural theory available, with the broadest explanatory and predictive range. Central to ART's predictive power is its ability to carry out fast, incremental, and stable unsupervised and supervised learning in response to a changing world. ART specifies mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony during both unsupervised and supervised learning. ART provides functional and mechanistic explanations of such diverse topics as laminar cortical circuitry; invariant object and scenic gist learning and recognition; prototype, surface, and boundary attention; gamma and beta oscillations; learning of entorhinal grid cells and hippocampal place cells; computation of homologous spatial and temporal mechanisms in the entorhinal-hippocampal system; vigilance breakdowns during autism and medial temporal amnesia; cognitive-emotional interactions that focus attention on valued objects in an adaptively timed way; item-order-rank working memories and learned list chunks for the planning and control of sequences of linguistic, spatial, and motor information; conscious speech percepts that are influenced by future context; auditory streaming in noise during source segregation; and speaker normalization. Brain regions that are functionally described include visual and auditory neocortex; specific and nonspecific thalamic nuclei; inferotemporal, parietal, prefrontal, entorhinal, hippocampal, parahippocampal, perirhinal, and motor cortices

  7. LeadMine: a grammar and dictionary driven approach to entity recognition

    PubMed Central

    2015-01-01

    Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

  8. Mandarin Chinese Dictionary: English-Chinese.

    ERIC Educational Resources Information Center

    Wang, Fred Fangyu

    This dictionary is a companion volume to the "Mandarin Chinese Dictionary (Chinese-English)" published in 1967 by Seton Hall University. The purpose of the dictionary is to help English-speaking students produce Chinese sentences in certain cultural situations by looking up the English expressions. Natural, spoken Chinese expressions within the…

  9. Chinese-English Nuclear and Physics Dictionary.

    ERIC Educational Resources Information Center

    Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

    The Nuclear and Physics Dictionary is one of a series of Chinese-English technical dictionaries prepared by the Foreign Technology Division, United States Air Force Systems Command. The purpose of this dictionary is to provide rapid reference tools for translators, abstractors, and research analysts concerned with scientific and technical…

  10. The Ecological Impact of a Dictionary.

    ERIC Educational Resources Information Center

    Liddicoat, Anthony J.

    2000-01-01

    Discusses the impact of the development of a dictionary of Jersey Norman French. The dictionary has created a perception among speakers of Jersey French that they did not know the language. This perception appears to be the result of the ecological change that the dictionary produced by repositioning the language from an oral habitat to a written…

  11. The New Unabridged English-Persian Dictionary.

    ERIC Educational Resources Information Center

    Aryanpur, Abbas; Saleh, Jahan Shah

    This five-volume English-Persian dictionary is based on Webster's International Dictionary (1960 and 1961) and The Shorter Oxford English Dictionary (1959); it attempts to provide Persian equivalents of all the words of Oxford and all the key-words of Webster. Pronunciation keys for the English phonetic transcription and for the difficult Persian…

  12. The Oxford English Dictionary: A Brief History.

    ERIC Educational Resources Information Center

    Fritze, Ronald H.

    1989-01-01

    Reviews the development of English dictionaries in general and the Oxford English Dictionary (OED) in particular. The discussion covers the decision by the Philological Society to create the dictionary, the principles that guided its development, the involvement of James Augustus Henry Murray, the magnitude and progress of the project, and the…

  13. Pragmatics, Lexicography and Dictionaries of English.

    ERIC Educational Resources Information Center

    Zgusta, Ladislav

    1988-01-01

    Details a history of the term "pragmatics" and how it influences lexicography. Three pragmatic aspects of dictionaries are discussed, including: 1) the cultural setting; 2) equivalence in bilingual dictionaries; 3) the lexicographic definition. Lexicographic definitions illustrate how a dictionary is largely based on pragmatics. (Author/LMO)

  14. Dictionnaire de Fulfulde (Fulfulde Dictionary). Trainee's Book.

    ERIC Educational Resources Information Center

    Bautista, Andreliz; And Others

    This dictionary of Fulani (Fulfulde) is designed for Peace Corps volunteers serving in Niger, and reflects daily communication needs in that context. A brief introduction to pronouns and verb endings precedes the dictionary text. The dictionary is presented in two sections. The first lists English words alphabetically and their translations in…

  15. Seri Dictionary: People and Kinship Terms.

    ERIC Educational Resources Information Center

    Moser, Mary B.; Marlett, Stephen A.

    A subset of a Seri-English bilingual dictionary (in preparation) is presented that includes terms referring to people, kinship terms, and verbs closely related to them. This version includes an English-to-Seri dictionary with 61 basic terms and variants, and a Seri dictionary with both Spanish and English glosses. It uses a practical orthography…

  16. Webster and Worcester: The War of Dictionaries.

    ERIC Educational Resources Information Center

    Landau, Sidney

    1984-01-01

    Provides overview of dictionary publication in United States from Noah Webster's "A Compendious Dictionary of the English Language" (1806) to "Webster's Third New International Dictionary" (1961). Competition between Webster and Joseph Worcester, additional publication challenges to Webster's popularity, and practices in spelling, definitions, and…

  17. Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

    ERIC Educational Resources Information Center

    Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

    2012-01-01

    With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

  18. Illustrated Dictionary of Practical Astronomy

    NASA Astrophysics Data System (ADS)

    Kitchin, Chris

    This could be the first dictionary of astronomy specifically written for practical amateur astronomers. In addition to definitions, it provides an invaluable reference source for terms, techniques, instruments, formulas and processes for practising observers, both amateur and professional. A special feature of this dictionary is extended definitions for many topics; they give sufficient information for many of the techniques and items of instrumentation to be used as well as understood. With over 200 illustrations and extensive appendices, this is an essential reference book for every astronomer.

  19. Noise-robust superresolution based on a classified dictionary

    NASA Astrophysics Data System (ADS)

    Jeong, Shin-Cheol; Song, Byung Cheol

    2010-10-01

    Conventional learning-based superresolution algorithms tend to boost noise components existing in input images because the algorithms are usually learned in a noise-free environment. Even though a specific noise reduction algorithm is applied to noisy images prior to superresolution, visual quality degradation is inevitable due to the mismatch between noise-free images and denoised images. Accordingly, we present a noise-robust superresolution algorithm that overcomes this problem. In the learning phase, a dictionary classified according to noise level is constructed, and then a high-resolution image is synthesized using the dictionary in the inference phase. Experimental results show that the proposed algorithm outperforms existing algorithms for various noisy images.

  20. Breast image feature learning with adaptive deconvolutional networks

    NASA Astrophysics Data System (ADS)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  1. Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning.

    PubMed

    Corbacho, Fernando; Nishikawa, Kiisa C; Weerasuriya, Ananda; Liaw, Jim-Shih; Arbib, Michael A

    2005-12-01

    The previous companion paper describes the initial (seed) schema architecture that gives rise to the observed prey-catching behavior. In this second paper in the series we describe the fundamental adaptive processes required during learning after lesioning. Following bilateral transections of the hypoglossal nerve, anurans lunge toward mealworms with no accompanying tongue or jaw movement. Nevertheless anurans with permanent hypoglossal transections eventually learn to catch their prey by first learning to open their mouth again and then lunging their body further and increasing their head angle. In this paper we present a new learning framework, called schema-based learning (SBL). SBL emphasizes the importance of the current existent structure (schemas), that defines a functioning system, for the incremental and autonomous construction of ever more complex structure to achieve ever more complex levels of functioning. We may rephrase this statement into the language of Schema Theory (Arbib 1992, for a comprehensive review) as the learning of new schemas based on the stock of current schemas. SBL emphasizes a fundamental principle of organization called coherence maximization, that deals with the maximization of congruence between the results of an interaction (external or internal) and the expectations generated for that interaction. A central hypothesis consists of the existence of a hierarchy of predictive internal models (predictive schemas) all over the control center-brain-of the agent. Hence, we will include predictive models in the perceptual, sensorimotor, and motor components of the autonomous agent architecture. We will then show that predictive models are fundamental for structural learning. In particular we will show how a system can learn a new structural component (augment the overall network topology) after being lesioned in order to recover (or even improve) its original functionality. Learning after lesioning is a special case of structural

  2. Learning about stress: neural, endocrine and behavioral adaptations.

    PubMed

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD). PMID:27294884

  3. Learning about stress: neural, endocrine and behavioral adaptations.

    PubMed

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD).

  4. Hierarchically clustered adaptive quantization CMAC and its learning convergence.

    PubMed

    Teddy, S D; Lai, E M K; Quek, C

    2007-11-01

    The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically

  5. Learner Characteristic Based Learning Effort Curve Mode: The Core Mechanism on Developing Personalized Adaptive E-Learning Platform

    ERIC Educational Resources Information Center

    Hsu, Pi-Shan

    2012-01-01

    This study aims to develop the core mechanism for realizing the development of personalized adaptive e-learning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according…

  6. Learning without labeling: domain adaptation for ultrasound transducer localization.

    PubMed

    Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

    2013-01-01

    The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

  7. Learning without labeling: domain adaptation for ultrasound transducer localization.

    PubMed

    Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

    2013-01-01

    The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts. PMID:24505743

  8. Radiographic skills learning: procedure simulation using adaptive hypermedia.

    PubMed

    Costaridou, L; Panayiotakis, G; Pallikarakis, N; Proimos, B

    1996-10-01

    The design and development of a simulation tool supporting learning of radiographic skills is reported. This tool has by textual, graphical and iconic resources, organized according to a building-block, adaptive hypermedia approach, which is described and supported by an image base of radiographs. It offers interactive user-controlled simulation of radiographic imaging procedures. The development is based on a commercially available environment (Toolbook 3.0, Asymetrix Corporation). The core of the system is an attributed precedence (priority) graph, which represents a task outline (concept and resources structure), which is dynamically adjusted to selected procedures. The user interface imitates a conventional radiography system, i.e. operating console, tube, table, patient and cassette. System parameters, such as patient positioning, focus-to-patient distance, magnification, field dimensions, tube voltage and mAs are under user control. Their effects on image quality are presented, by means of an image base acquired under controlled exposure conditions. Innovative use of hypermedia, computer based learning and simulation principles and technology in the development of this tool resulted in an enhanced interactive environment providing radiographic parameter control and visualization of parameter effects on image quality. PMID:9038530

  9. Dictionary of Radio and Television.

    ERIC Educational Resources Information Center

    Pannett, W. E.

    This dictionary presents definitions of both the well-established terms and many new ones that have come into use with the advances that have taken place in the fields of radio and television. In many cases extended definitions are given in order to describe briefly elementary principles and circuits, while newer and more complex devices and…

  10. Marketing and Communications Media Dictionary.

    ERIC Educational Resources Information Center

    Vigrolio, Tom; Zahler, Jack

    The authors have compiled a dictionary of terms used in marketing, advertising, public relations, and radio/television, photography/filmmaking, and graphics. Included in the volume are articles of a general and historical interest regarding the various media covered in the definitions. A list of trade publications is appended. (JY)

  11. STRUCTURAL LINGUISTICS AND BILINGUAL DICTIONARIES.

    ERIC Educational Resources Information Center

    MALONE, KEMP

    A STRUCTURAL, LINGUISTIC APPROACH TO BILINGUAL DICTIONARIES WAS DESCRIBED. DETAILED DISCUSSIONS WERE INCLUDED FOR USES OF MORPHEMES, MORPHEMIC SEQUENCES, PHONEMES, PHONETIC TRANSCRIPTIONS, AND ALLOPHONES. THIS REPORT IS ONE OF A SERIES OF 13 PAPERS PRESENTED AT A CONFERENCE ON LEXICOGRAPHY, INDIANA UNIVERSITY, NOVEMBER 11-12, 1960. (GC)

  12. A Kurdish-English Dictionary.

    ERIC Educational Resources Information Center

    Wahby, Taufiq; Edmonds, C.J.

    The Kurdish of this dictionary is the standard language of belles-lettres, journalism, official and private correspondence, and formal speech of the Southern-Kirmanji dialect of Sulaimani in Iraq. It is the language adopted in Persia for Kurdish broadcasts and government-sponsored publications. Borrowings from Northern Kirmanji, Mukri, Sanandaji,…

  13. Pikchul Nioki Chulda (Picture Dictionary).

    ERIC Educational Resources Information Center

    Dixon, Joan; And Others

    Developed for students in kindergarten through second grade, this alphabet book is one in a series of picture dictionaries in the Pima language developed to instill pride in Pima students by presenting their language in print and to increase their vocabularies in both Pima and English. Introductory sections provide a brief history of the project…

  14. THAI-ENGLISH, STUDENT'S DICTIONARY.

    ERIC Educational Resources Information Center

    HAAS, MARY R.; AND OTHERS

    THE PRINCIPAL AIM OF THIS DICTIONARY IS TO PROVIDE A CONVENIENT REFERENCE WORK FOR THE ENGLISH-SPEAKING STUDENT OF THAI. THE FOLLOWING TYPES OF INFORMATION ARE CONTAINED WITH EACH ENTRY--(1) THE TRADITIONAL THAI SPELLING, (2) A CAREFULLY WORKED OUT PHONEMIC TRANSCRIPTION USING ROMAN LETTERS, (3) AN ABBREVIATION DESIGNATING THE WORD CLASS, WHERE…

  15. A KOREAN-ENGLISH DICTIONARY.

    ERIC Educational Resources Information Center

    MARTIN, SAMUEL E.; AND OTHERS

    ALTHOUGH THE PURPOSE OF THIS DICTIONARY IS TO "GIVE A FULL AND ACCURATE PORTRAYAL OF THE BASIC NATIVE KOREAN VOCABULARY," SOME OF THE COMMON AND USEFUL CHINESE AND EUROPEAN LOANWORDS HAVE BEEN INCLUDED AS WELL. THE AUTHORS (SAMUEL E. MARTIN, YANG HA LEE, AND SUNG-UN CHANG) HAVE FOLLOWED THE HANKUL SPELLING CONVENTIONS OF THE "UNIFIED SYSTEM" AND…

  16. Sentiment Polarity Analysis based multi-dictionary

    NASA Astrophysics Data System (ADS)

    Jiao, Jian; Zhou, Yanquan

    This paper presents a novel algorithm for Chinese online reviews, which identifies sentiment polarity. To determine the sentence is negative or positive, we extracted opinion words and identified their opinion targets by CRFs and establish the absolute emotional dictionary (AbED), the relative emotional dictionary (ReED), the field of emotional dictionary (FiED) and the field of targets and opinion words dictionary (TfED). With those emotional dictionary, negative dictionary and modified dictionary, we achieved an effective algorithm to discriminate sentiment polarity by multi-string pattern matching algorithm. For evaluation, we used car online reviews, hotel online reviews and computer online reviews which annotated positive or negative. Experimental results show that our proposed method has made a higher precision and recall rate.

  17. Examining the Relationship between Learning Organization Characteristics and Change Adaptation, Innovation, and Organizational Performance

    ERIC Educational Resources Information Center

    Kontoghiorghes, Constantine; Awbre, Susan M.; Feurig, Pamela L.

    2005-01-01

    The main purpose of this exploratory study was to examine the relationship between certain learning organization characteristics and change adaptation, innovation, and bottom-line organizational performance. The following learning organization characteristics were found to be the strongest predictors of rapid change adaptation, quick product or…

  18. The Future of Adaptive Learning: Does the Crowd Hold the Key?

    ERIC Educational Resources Information Center

    Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay

    2016-01-01

    Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students--explanations, feedback, and other pedagogical interactions. Considering the…

  19. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    ERIC Educational Resources Information Center

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  20. Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

    ERIC Educational Resources Information Center

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

    The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

  1. A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

    ERIC Educational Resources Information Center

    Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi

    2015-01-01

    The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…

  2. The Effects of Rapid Assessments and Adaptive Restudy Prompts in Multimedia Learning

    ERIC Educational Resources Information Center

    Renkl, Alexander; Skuballa, Irene T.; Schwonke, Rolf; Harr, Nora; Leber, Jasmin

    2015-01-01

    We investigated the effects of rapid assessment tasks and different adaptive restudy prompts in multimedia learning. The adaptivity was based on rapid assessment tasks that were interspersed throughout a multimedia learning environment. In Experiment 1 (N = 52 university students), we analyzed to which extent rapid assessment tasks were reactive…

  3. Exploring the Effects of Intercultural Learning on Cross-Cultural Adaptation in a Study Abroad Context

    ERIC Educational Resources Information Center

    Tsai, Yau

    2011-01-01

    This study targets Asian students studying abroad and explores the effects of intercultural learning on their cross-cultural adaptation by drawing upon a questionnaire survey. On the one hand, the results of this study find that under the influence of intercultural learning, students respond differently in their cross-cultural adaptation and no…

  4. An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment

    ERIC Educational Resources Information Center

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…

  5. A dictionary of Astronomy for the French Sign Language (LSF)

    NASA Astrophysics Data System (ADS)

    Proust, Dominique; Abbou, Daniel; Chab, Nasro

    2011-06-01

    Since a few years, the french deaf communauty have access to astronomy at Paris-Meudon observatory through a specific teaching adapted from the French Sign Language (Langue des Signes Françcaise, LSF) including direct observations with the observatory telescopes. From this experience, an encyclopedic dictionary of astronomy The Hands in the Stars is now available, containing more than 200 astronomical concepts. Many of them did not existed in Sign Language and can be now fully expressed and explained.

  6. Second Graders Learn Animal Adaptations through Form and Function Analogy Object Boxes

    ERIC Educational Resources Information Center

    Rule, Audrey C.; Baldwin, Samantha; Schell, Robert

    2008-01-01

    This study examined the use of form and function analogy object boxes to teach second graders (n = 21) animal adaptations. The study used a pretest-posttest design to examine animal adaptation content learned through focused analogy activities as compared with reading and Internet searches for information about adaptations of animals followed by…

  7. Embedding Knowledge Management into Business Logic of E-learning Platform for Obtaining Adaptivity

    NASA Astrophysics Data System (ADS)

    Burdescu, Dumitru Dan; Mihaescu, Marian Cristian; Logofatu, Bogdan

    Obtaining adaptivity is one of the main concerns in current e-Learning development. This chapter proposes a methodology for obtaining adaptivity by embedding knowledge management into the business logic of the e-Learning platform. Naïve Bayes classifier is used as machine learning algorithm for obtaining the resources that need to be further accessed by learners. The analysis is accomplished on a discipline that is well structured according to a concept map.

  8. Context-Adaptive Learning Designs by Using Semantic Web Services

    ERIC Educational Resources Information Center

    Dietze, Stefan; Gugliotta, Alessio; Domingue, John

    2007-01-01

    IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…

  9. The Power of Math Dictionaries in the Classroom

    ERIC Educational Resources Information Center

    Patterson, Lynn Gannon; Young, Ashlee Futrell

    2013-01-01

    This article investigates the value of a math dictionary in the elementary classroom and if elementary students prefer using a traditional math dictionary or a dictionary on an iPad. In each child's journey to reading with understanding, the dictionary can be a comforting and valuable resource. Would students find a math dictionary to be a…

  10. Contributions to Adaptive Educational Hypermedia Systems via On-Line Learning Style Estimation

    ERIC Educational Resources Information Center

    Botsios, Sotiris; Georgiou, Demetrius; Safouris, Nikolaos

    2008-01-01

    In order to establish an online diagnostic system for Learning Style Estimation that contributes to the adaptation of learning objects, we propose an easily applicable expert system founded on Bayesian Networks. The proposed system makes use of Learning Style theories and associated diagnostic techniques, simultaneously avoiding certain error…

  11. Constructive, Self-Regulated, Situated, and Collaborative Learning: An Approach for the Acquisition of Adaptive Competence

    ERIC Educational Resources Information Center

    de Corte, Erik

    2012-01-01

    In today's learning society, education must focus on fostering adaptive competence (AC) defined as the ability to apply knowledge and skills flexibly in different contexts. In this article, four major types of learning are discussed--constructive, self-regulated, situated, and collaborative--in relation to what students must learn in order to…

  12. Pedagogy to Empower Chinese Learners to Adapt to Western Learning Circumstances: A Longitudinal Case-Study

    ERIC Educational Resources Information Center

    Saravanamuthu, Kala; Yap, Christine

    2014-01-01

    Deficit theorisations of Chinese Learners studying in western countries are criticised for dichotomising learning attributes into Surface- or Deep-learning approaches. Subsequent context-dependent, small culture studies of students transiting between cultures theorise learning as a dynamic journey of adapting to a range/continuum of learning…

  13. Features: Real-Time Adaptive Feature and Document Learning for Web Search.

    ERIC Educational Resources Information Center

    Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

    2001-01-01

    Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

  14. A Framework for Adaptive Learning Design in a Web-Conferencing Environment

    ERIC Educational Resources Information Center

    Bower, Matt

    2016-01-01

    Many recent technologies provide the ability to dynamically adjust the interface depending on the emerging cognitive and collaborative needs of the learning episode. This means that educators can adaptively re-design the learning environment during the lesson, rather than purely relying on preemptive learning design thinking. Based on a…

  15. Learning to Be a Community: Schools Need Adaptable Models to Create Successful Programs

    ERIC Educational Resources Information Center

    Ermeling, Bradley A.; Gallimore, Ronald

    2013-01-01

    Making schools learning places for teachers as well as students is a timeless and appealing vision. The growing number of professional learning communities is a hopeful sign that profound change is on the way. This is the challenge learning communities face: Schools and districts need implementation models flexible enough to adapt to local…

  16. The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    ERIC Educational Resources Information Center

    Roessger, Kevin M.

    2014-01-01

    In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates…

  17. Adapting to test structure: letting testing teach what to learn.

    PubMed

    Garcia-Marques, Leonel; Nunes, Ludmila D; Marques, Pedro; Carneiro, Paula; Weinstein, Yana

    2015-01-01

    We propose that we encode and store information as a function of the particular ways we have used similar information in the past. More specifically, we contend that the experience of retrieval can serve as a powerful cue to the most effective ways to encode similar information in comparable future learning episodes. To explore these ideas, we did two studies in which all participants went through study-test cycles of single category lists while we manipulated the nature of the recognition tests. The recognition tests either included only same-category lures or only different-category lures. The experience of repeated testing leads participants to avoid conceptual-based strategies but only when conceptual knowledge was poorly diagnostic for recognition (i.e., in the same-category lures condition). In a second study with a similar manipulation, we showed that repeated testing with lures from the same category as study items improved performance in a final recall surprise test compared to conditions in which different-category lures were used. Such a difference is akin to the one obtained when encoding instructions focus on distinctive item features compared to cases in which the focus is on relational processing. We suggest that testing requirements lead to adaptive changes at encoding.

  18. Evolutionary perspectives on learning: conceptual and methodological issues in the study of adaptive specializations.

    PubMed

    Krause, Mark A

    2015-07-01

    Inquiry into evolutionary adaptations has flourished since the modern synthesis of evolutionary biology. Comparative methods, genetic techniques, and various experimental and modeling approaches are used to test adaptive hypotheses. In psychology, the concept of adaptation is broadly applied and is central to comparative psychology and cognition. The concept of an adaptive specialization of learning is a proposed account for exceptions to general learning processes, as seen in studies of Pavlovian conditioning of taste aversions, sexual responses, and fear. The evidence generally consists of selective associations forming between biologically relevant conditioned and unconditioned stimuli, with conditioned responses differing in magnitude, persistence, or other measures relative to non-biologically relevant stimuli. Selective associations for biologically relevant stimuli may suggest adaptive specializations of learning, but do not necessarily confirm adaptive hypotheses as conceived of in evolutionary biology. Exceptions to general learning processes do not necessarily default to an adaptive specialization explanation, even if experimental results "make biological sense". This paper examines the degree to which hypotheses of adaptive specializations of learning in sexual and fear response systems have been tested using methodologies developed in evolutionary biology (e.g., comparative methods, quantitative and molecular genetics, survival experiments). A broader aim is to offer perspectives from evolutionary biology for testing adaptive hypotheses in psychological science.

  19. Dictionary Approaches to Image Compression and Reconstruction

    NASA Technical Reports Server (NTRS)

    Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

    1998-01-01

    This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

  20. Dictionary Approaches to Image Compression and Reconstruction

    NASA Technical Reports Server (NTRS)

    Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

    1998-01-01

    This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as lambda, are discrete time signals, where y represents the dictionary index. A dictionary with a collection of these waveforms Is typically complete or over complete. Given such a dictionary, the goal is to obtain a representation Image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

  1. Introducing and adapting a novel method for investigating learning experiences in clinical learning environments.

    PubMed

    Lachmann, Hanna; Ponzer, Sari; Johansson, Unn-Britt; Karlgren, Klas

    2012-09-01

    The Contextual Activity Sampling System (CASS) is a novel methodology designed for collecting data of on-going learning experiences through frequent sampling by using mobile phones. This paper describes how it for the first time has been introduced to clinical learning environments. The purposes of this study were to cross-culturally adapt the CASS tool and questionnaire for use in clinical learning environments, investigate whether the methodology is suitable for collecting data and how it is experienced by students. A study was carried out with 51 students who reported about their activities and experiences five times a day during a 2-week course on an interprofessional training ward. Interviews were conducted after the course. The study showed that CASS provided a range of detailed and interesting qualitative and quantitative data, which we would not have been able to collect using traditional methods such as post-course questionnaires or interviews. Moreover, the participants reported that CASS worked well, was easy to use, helped them structure their days and reflect on their learning activities. This methodology proved to be a fruitful way of collecting information about experiences, which could be useful for not only researchers but also students, teachers and course designers.

  2. Adaptation of Conceptions of Learning Science Questionnaire into Turkish and Science Teacher Candidates' Conceptions of Learning Science

    ERIC Educational Resources Information Center

    Bahçivan, Eralp; Kapucu, Serkan

    2014-01-01

    The purposes of this study were to (1) adapt an instrument "The Conceptions of Learning Science (COLS) questionnaire" into Turkish, and (2) to determine Turkish science teacher candidates' COLS. Adapting the instrument four steps were followed. Firstly, COLS questionnaire was translated into Turkish. Secondly, COLS questionnaire was…

  3. Cases on Technological Adaptability and Transnational Learning: Issues and Challenges

    ERIC Educational Resources Information Center

    Mukerji, Siran, Ed.; Tripathi, Purnendu, Ed.

    2010-01-01

    Technology holds the key for bridging the gap between access to quality education and the need for enhanced learning experiences. This book contains case studies on divergent themes of personalized learning environments, inclusive learning for social change, innovative learning and assessment techniques, technology and international partnership…

  4. Translation lexicon acquisition from bilingual dictionaries

    NASA Astrophysics Data System (ADS)

    Doermann, David S.; Ma, Huanfeng; Karagol-Ayan, Burcu; Oard, Douglas W.

    2001-12-01

    Bilingual dictionaries hold great potential as a source of lexical resources for training automated systems for optical character recognition, machine translation and cross-language information retrieval. In this work we describe a system for extracting term lexicons from printed copies of bilingual dictionaries. We describe our approach to page and definition segmentation and entry parsing. We have used the approach to parse a number of dictionaries and demonstrate the results for retrieval using a French-English Dictionary to generate a translation lexicon and a corpus of English queries applied to French documents to evaluation cross-language IR.

  5. Design Framework for an Adaptive MOOC Enhanced by Blended Learning: Supplementary Training and Personalized Learning for Teacher Professional Development

    ERIC Educational Resources Information Center

    Gynther, Karsten

    2016-01-01

    The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional development. The framework has been evaluated by…

  6. Adapting the Speed of Reproduction of Audio Content and Using Text Reinforcement for Maximizing the Learning Outcome though Mobile Phones

    ERIC Educational Resources Information Center

    Munoz-Organero, M.; Munoz-Merino, P. J.; Kloos, Carlos Delgado

    2011-01-01

    The use of technology in learning environments should be targeted at improving the learning outcome of the process. Several technology enhanced techniques can be used for maximizing the learning gain of particular students when having access to learning resources. One of them is content adaptation. Adapting content is especially important when…

  7. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    PubMed

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management.

  8. The semantics of Chemical Markup Language (CML): dictionaries and conventions

    PubMed Central

    2011-01-01

    The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs. PMID:21999509

  9. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN. PMID:18632393

  10. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.

  11. Adaptive properties of differential learning rates for positive and negative outcomes.

    PubMed

    Cazé, Romain D; van der Meer, Matthijs A A

    2013-12-01

    The concept of the reward prediction error-the difference between reward obtained and reward predicted-continues to be a focal point for much theoretical and experimental work in psychology, cognitive science, and neuroscience. Models that rely on reward prediction errors typically assume a single learning rate for positive and negative prediction errors. However, behavioral data indicate that better-than-expected and worse-than-expected outcomes often do not have symmetric impacts on learning and decision-making. Furthermore, distinct circuits within cortico-striatal loops appear to support learning from positive and negative prediction errors, respectively. Such differential learning rates would be expected to lead to biased reward predictions and therefore suboptimal choice performance. Contrary to this intuition, we show that on static "bandit" choice tasks, differential learning rates can be adaptive. This occurs because asymmetric learning enables a better separation of learned reward probabilities. We show analytically how the optimal learning rate asymmetry depends on the reward distribution and implement a biologically plausible algorithm that adapts the balance of positive and negative learning rates from experience. These results suggest specific adaptive advantages for separate, differential learning rates in simple reinforcement learning settings and provide a novel, normative perspective on the interpretation of associated neural data.

  12. Adaptive Learning and Reduced Cognitive Uncertainty in a Financial Organization

    ERIC Educational Resources Information Center

    Antonsen, Yngve; Thunberg, Odd Arne; Tiller, Tom

    2010-01-01

    Purpose: This paper analyses and discusses the "learning activities" that comprise obligatory learning at work by employees each month. The management strategy is to use these learning activities to spread knowledge, exchange experience and implement new skills within the organisation. The purpose of this paper is to answer the question: to what…

  13. SLED: Semantic Label Embedding Dictionary Representation for Multilabel Image Annotation.

    PubMed

    Cao, Xiaochun; Zhang, Hua; Guo, Xiaojie; Liu, Si; Meng, Dan

    2015-09-01

    Most existing methods on weakly supervised image annotation rely on jointly unsupervised feature representation, the components of which are not directly correlated with specific labels. In practical cases, however, there is a big gap between the training and the testing data, say the label combination of the testing data is not always consistent with that of the training. To bridge the gap, this paper presents a semantic label embedding dictionary representation that not only achieves the discriminative feature representation for each label in the image, but also mines the semantic relevance between co-occurrence labels for context information. More specifically, to enhance the discriminative representation of labels, the training data is first divided into a set of overlapped groups by graph shift based on the exclusive label graph. Afterward, given a group of exclusive labels, we try to learn multiple label-specific dictionaries to explicitly decorrelate the feature representation of each label. A joint optimization approach is proposed according to the Fisher discrimination criterion for seeking its solution. Then, to discover the context information hidden in the co-occurrence labels, we explore the semantic relationship between visual words in dictionaries and labels in a multitask learning way with respect to the reconstruction coefficients of the training data. In the annotation stage, with the discriminative dictionaries and exclusive label groups as well as a group sparsity constraint, the reconstruction coefficients of a test image can be easily obtained. Finally, we introduce a label propagation scheme to compute the score of each label for the test image based on its reconstruction coefficients. Experimental results on three challenging data sets demonstrate that our proposed method leads to significant performance gains over existing methods. PMID:25935037

  14. Lost in Translation: Adapting a Face-to-Face Course Into an Online Learning Experience.

    PubMed

    Kenzig, Melissa J

    2015-09-01

    Online education has grown dramatically over the past decade. Instructors who teach face-to-face courses are being called on to adapt their courses to the online environment. Many instructors do not have sufficient training to be able to effectively move courses to an online format. This commentary discusses the growth of online learning, common challenges faced by instructors adapting courses from face-to-face to online, and best practices for translating face-to-face courses into online learning opportunities.

  15. Stimulating the cerebellum affects visuomotor adaptation but not intermanual transfer of learning

    PubMed Central

    Block, Hannah; Celnik, Pablo

    2013-01-01

    When systematic movement errors occur, the brain responds with a systematic change in motor behavior. This type of adaptive motor learning can transfer intermanually; adaptation of movements of the right hand in response to training with a perturbed visual signal (visuomotor adaptation) may carry over to the left hand. While visuomotor adaptation has been studied extensively, it is unclear whether the cerebellum, a structure involved in adaptation, is important for intermanual transfer as well. We addressed this question with three experiments in which subjects reached with their right hands as a 30° visuomotor rotation was introduced. Subjects received anodal or sham transcranial direct current stimulation (tDCS) on the trained (Experiment 1) or untrained (Experiment 2) hemisphere of the cerebellum, or, for comparison, motor cortex (M1). After the training period, subjects reached with their left hand, without visual feedback, to assess intermanual transfer of learning aftereffects. Stimulation of the right cerebellum caused faster adaptation, but none of the stimulation sites affected transfer. To ascertain whether cerebellar stimulation would increase transfer if subjects learned faster as well as a larger amount, in Experiment 3 anodal and sham cerebellar groups experienced a shortened training block such that the anodal group learned more than sham. Despite the difference in adaptation magnitude, transfer was similar across these groups, although smaller than in Experiment 1. Our results suggest that intermanual transfer of visuomotor learning does not depend on cerebellar activity, and that the number of movements performed at plateau is an important predictor of transfer. PMID:23625383

  16. Learning to push and learning to move: the adaptive control of contact forces

    PubMed Central

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

  17. Learning to push and learning to move: the adaptive control of contact forces.

    PubMed

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in "compatible pairs" connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions. PMID

  18. Perceptual Learning of Time-Compressed Speech: More than Rapid Adaptation

    PubMed Central

    Banai, Karen; Lavner, Yizhar

    2012-01-01

    Background Time-compressed speech, a form of rapidly presented speech, is harder to comprehend than natural speech, especially for non-native speakers. Although it is possible to adapt to time-compressed speech after a brief exposure, it is not known whether additional perceptual learning occurs with further practice. Here, we ask whether multiday training on time-compressed speech yields more learning than that observed during the initial adaptation phase and whether the pattern of generalization following successful learning is different than that observed with initial adaptation only. Methodology/Principal Findings Two groups of non-native Hebrew speakers were tested on five different conditions of time-compressed speech identification in two assessments conducted 10–14 days apart. Between those assessments, one group of listeners received five practice sessions on one of the time-compressed conditions. Between the two assessments, trained listeners improved significantly more than untrained listeners on the trained condition. Furthermore, the trained group generalized its learning to two untrained conditions in which different talkers presented the trained speech materials. In addition, when the performance of the non-native speakers was compared to that of a group of naïve native Hebrew speakers, performance of the trained group was equivalent to that of the native speakers on all conditions on which learning occurred, whereas performance of the untrained non-native listeners was substantially poorer. Conclusions/Significance Multiday training on time-compressed speech results in significantly more perceptual learning than brief adaptation. Compared to previous studies of adaptation, the training induced learning is more stimulus specific. Taken together, the perceptual learning of time-compressed speech appears to progress from an initial, rapid adaptation phase to a subsequent prolonged and more stimulus specific phase. These findings are consistent with

  19. Data Element Dictionary: Facilities. Preliminary Draft.

    ERIC Educational Resources Information Center

    Thomas, Charles R.

    The draft includes--(1) comments on file structure, (2) descriptions of dictionary organization and format, (3) alphabetical lists of elements, and (4) facilities related elements in dictionary form. The data element definitions in this draft are compatible with the Higher Education Facilities Classification and Inventory Procedures Manual, which…

  20. The Oxford Picture Dictionary. Beginning Workbook.

    ERIC Educational Resources Information Center

    Fuchs, Marjorie

    The beginning workbook of the Oxford Picture Dictionary is in full color and offers vocabulary reinforcement activities that correspond page for page with the dictionary. Clear and simple instructions with examples make it suitable for independent use in the classroom or at home. The workbook has up-to-date art and graphics, explaining over 3700…

  1. Chinese-English Aviation and Space Dictionary.

    ERIC Educational Resources Information Center

    Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

    The Aviation and Space Dictionary is the second of a series of Chinese-English technical dictionaries under preparation by the Foreign Technology Division, United States Air Force Systems Command. The purpose of the series is to provide rapid reference tools for translators, abstracters, and research analysts concerned with scientific and…

  2. Dictionaries of African Sign Languages: An Overview

    ERIC Educational Resources Information Center

    Schmaling, Constanze H.

    2012-01-01

    This article gives an overview of dictionaries of African sign languages that have been published to date most of which have not been widely distributed. After an introduction into the field of sign language lexicography and a discussion of some of the obstacles that authors of sign language dictionaries face in general, I will show problems…

  3. Getting the Most out of the Dictionary

    ERIC Educational Resources Information Center

    Marckwardt, Albert H.

    2012-01-01

    The usefulness of the dictionary as a reliable source of information for word meanings, spelling, and pronunciation is widely recognized. But even in these obvious matters, the information that the dictionary has to offer is not always accurately interpreted. With respect to pronunciation there seem to be two general pitfalls: (1) the…

  4. An English-Hausa Dictionary. Final Report.

    ERIC Educational Resources Information Center

    Newman, Roxana Ma

    This English-to-Hausa dictionary is designed as a practical reference for the English-speaker seeking information about the modern vocabulary and structure of the Hausa language. An introductory section explains the principles and conventions adopted in compiling the dictionary. The vocabulary includes English words that the average user is likely…

  5. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    ERIC Educational Resources Information Center

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  6. Higher-Order Thinking Development through Adaptive Problem-Based Learning

    ERIC Educational Resources Information Center

    Raiyn, Jamal; Tilchin, Oleg

    2015-01-01

    In this paper we propose an approach to organizing Adaptive Problem-Based Learning (PBL) leading to the development of Higher-Order Thinking (HOT) skills and collaborative skills in students. Adaptability of PBL is expressed by changes in fixed instructor assessments caused by the dynamics of developing HOT skills needed for problem solving,…

  7. ActiveTutor: Towards More Adaptive Features in an E-Learning Framework

    ERIC Educational Resources Information Center

    Fournier, Jean-Pierre; Sansonnet, Jean-Paul

    2008-01-01

    Purpose: This paper aims to sketch the emerging notion of auto-adaptive software when applied to e-learning software. Design/methodology/approach: The study and the implementation of the auto-adaptive architecture are based on the operational framework "ActiveTutor" that is used for teaching the topic of computer science programming in first-grade…

  8. The older person has a stroke: Learning to adapt using the Feldenkrais® Method.

    PubMed

    Jackson-Wyatt, O

    1995-01-01

    The older person with a stroke requires adapted therapeutic interventions to take into account normal age-related changes. The Feldenkrais® Method presents a model for learning to promote adaptability that addresses key functional changes seen with normal aging. Clinical examples related to specific functional tasks are discussed to highlight major treatment modifications and neuromuscular, psychological, emotional, and sensory considerations. PMID:27619899

  9. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    ERIC Educational Resources Information Center

    Hsu, Ching-Kun

    2015-01-01

    The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…

  10. A User-Centric Adaptive Learning System for E-Learning 2.0

    ERIC Educational Resources Information Center

    Huang, Shiu-Li; Shiu, Jung-Hung

    2012-01-01

    The success of Web 2.0 inspires e-learning to evolve into e-learning 2.0, which exploits collective intelligence to achieve user-centric learning. However, searching for suitable learning paths and content for achieving a learning goal is time consuming and troublesome on e-learning 2.0 platforms. Therefore, introducing formal learning in these…

  11. Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

    NASA Astrophysics Data System (ADS)

    Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

    2014-05-01

    We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the

  12. International Students' Culture Learning and Cultural Adaptation in China

    ERIC Educational Resources Information Center

    An, Ran; Chiang, Shiao-Yun

    2015-01-01

    This article examines international students' cultural adaptation at a major national university in China. A survey was designed to measure international students' adaptation to the Chinese sociocultural and educational environments in terms of five dimensions: (1) cultural empathy, (2) open-mindedness, (3) emotional stability, (4) social…

  13. Management Strategies for Complex Adaptive Systems: Sensemaking, Learning, and Improvisation

    ERIC Educational Resources Information Center

    McDaniel, Reuben R., Jr.

    2007-01-01

    Misspecification of the nature of organizations may be a major reason for difficulty in achieving performance improvement. Organizations are often viewed as machine-like, but complexity science suggests that organizations should be viewed as complex adaptive systems. I identify the characteristics of complex adaptive systems and give examples of…

  14. Academic Accountability and University Adaptation: The Architecture of an Academic Learning Organization.

    ERIC Educational Resources Information Center

    Dill, David D.

    1999-01-01

    Discussses various adaptations in organizational structure and governance of academic learning institutions, using case studies of universities that are attempting to improve the quality of teaching and the learning process. Identifies five characteristics typical of such organizations: (1) a culture of evidence; (2) improved coordination of…

  15. Impact of Nursing Learning Environments on Adaptive Competency Development in Baccalaureate Nursing Students.

    ERIC Educational Resources Information Center

    Laschinger, Heather K. Spence

    1992-01-01

    Kolb's experiential learning theory was used as a framework to study 179 generic baccalaureate students' perceptions of the different types of learning environments and adaptive competencies. Clinical experience and preceptorships contributed more to competency development than did nursing or nonnursing classes. (JOW)

  16. A Standard-Based Model for Adaptive E-Learning Platform for Mauritian Academic Institutions

    ERIC Educational Resources Information Center

    Kanaksabee, P.; Odit, M. P.; Ramdoyal, A.

    2011-01-01

    The key aim of this paper is to introduce a standard-based model for adaptive e-learning platform for Mauritian academic institutions and to investigate the conditions and tools required to implement this model. The main forces of the system are that it allows collaborative learning, communication among user, and reduce considerable paper work.…

  17. Educational Multimedia Profiling Recommendations for Device-Aware Adaptive Mobile Learning

    ERIC Educational Resources Information Center

    Moldovan, Arghir-Nicolae; Ghergulescu, Ioana; Muntean, Cristina Hava

    2014-01-01

    Mobile learning is seeing a fast adoption with the increasing availability and affordability of mobile devices such as smartphones and tablets. As the creation and consumption of educational multimedia content on mobile devices is also increasing fast, educators and mobile learning providers are faced with the challenge to adapt multimedia type…

  18. Partner Knowledge Awareness in Knowledge Communication: Learning by Adapting to the Partner

    ERIC Educational Resources Information Center

    Dehler Zufferey, Jessica; Bodemer, Daniel; Buder, Jurgen; Hesse, Friedrich W.

    2011-01-01

    Awareness of the knowledge of learning partners is not always sufficiently available in collaborative learning scenarios. To compensate, the authors propose to provide collaborators with partner knowledge awareness by means of a visualization tool. Partner knowledge awareness can be used to adapt messages toward the partner. This study…

  19. An Online Adaptive Learning Environment for Critical-Thinking-Infused English Literacy Instruction

    ERIC Educational Resources Information Center

    Yang, Ya-Ting Carolyn; Gamble, Jeffrey Hugh; Hung, Yu-Wan; Lin, Tzu-Yun

    2014-01-01

    Critical thinking (CT) and English literacy are two essential 21st century competencies that are a priority for teaching and learning in an increasingly digital learning environment. Taking advantage of innovations in educational technology, this study empirically investigates the effectiveness of CT-infused adaptive English literacy instruction…

  20. Towards Increased Relevance: Context-Adapted Models of the Learning Organization

    ERIC Educational Resources Information Center

    Örtenblad, Anders

    2015-01-01

    Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

  1. Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

    ERIC Educational Resources Information Center

    Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

    2010-01-01

    More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

  2. Perceived Control and Adaptive Coping: Programs for Adolescent Students Who Have Learning Disabilities

    ERIC Educational Resources Information Center

    Firth, Nola; Frydenberg, Erica; Greaves, Daryl

    2008-01-01

    This study explored the effect of a coping program and a teacher feedback intervention on perceived control and adaptive coping for 98 adolescent students who had specific learning disabilities. The coping program was modified to build personal control and to address the needs of students who have specific learning disabilities. The teacher…

  3. The Effect of Adaptive Confidence Strategies in Computer-Assisted Instruction on Learning and Learner Confidence

    ERIC Educational Resources Information Center

    Warren, Richard Daniel

    2012-01-01

    The purpose of this research was to investigate the effects of including adaptive confidence strategies in instructionally sound computer-assisted instruction (CAI) on learning and learner confidence. Seventy-one general educational development (GED) learners recruited from various GED learning centers at community colleges in the southeast United…

  4. Adaptation of the Students' Motivation towards Science Learning (SMTSL) Questionnaire in the Greek Language

    ERIC Educational Resources Information Center

    Dermitzaki, Irini; Stavroussi, Panayiota; Vavougios, Denis; Kotsis, Konstantinos T.

    2013-01-01

    The present study aimed at adapting in the Greek language the Students' Motivation Towards Science Learning (SMTSL) questionnaire developed by Tuan, Chin, and Shieh ("INT J SCI EDUC" 27(6): 639-654, 2005a) into a different cultural context, a different age group, that is, in university students and with a focus on physics learning.…

  5. Adaptive Human Scaffolding Facilitates Adolescents' Self-Regulated Learning with Hypermedia

    ERIC Educational Resources Information Center

    Azevedo, Roger; Cromley, Jennifer G.; Winters, Fielding I.; Moos, Daniel C.; Greene, Jeffrey A.

    2005-01-01

    This study examines the effectiveness of three scaffolding conditions on adolescents' learning about the circulatory system with a hypermedia learning environment. One hundred and eleven adolescents (n = 111) were randomly assigned to one of three scaffolding conditions (adaptive scaffolding (AS), fixed scaffolding (FS), or no scaffolding (NS))…

  6. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  7. A service based adaptive U-learning system using UX.

    PubMed

    Jeong, Hwa-Young; Yi, Gangman

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  8. A Service Based Adaptive U-Learning System Using UX

    PubMed Central

    Jeong, Hwa-Young

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

  9. Adapting Cognitive Walkthrough to Support Game Based Learning Design

    ERIC Educational Resources Information Center

    Farrell, David; Moffat, David C.

    2014-01-01

    For any given Game Based Learning (GBL) project to be successful, the player must learn something. Designers may base their work on pedagogical research, but actual game design is still largely driven by intuition. People are famously poor at unsupported methodical thinking and relying so much on instinct is an obvious weak point in GBL design…

  10. Psychosocial and Adaptive Deficits Associated with Learning Disability Subtypes

    ERIC Educational Resources Information Center

    Backenson, Erica M.; Holland, Sara C.; Kubas, Hanna A.; Fitzer, Kim R.; Wilcox, Gabrielle; Carmichael, Jessica A.; Fraccaro, Rebecca L.; Smith, Amanda D.; Macoun, Sarah J.; Harrison, Gina L.; Hale, James B.

    2015-01-01

    Children with specific learning disabilities (SLD) have deficits in the basic psychological processes that interfere with learning and academic achievement, and for some SLD subtypes, these deficits can also lead to emotional and/or behavior problems. This study examined psychosocial functioning in 123 students, aged 6 to 11, who underwent…

  11. Do learning rates adapt to the distribution of rewards?

    PubMed

    Gershman, Samuel J

    2015-10-01

    Studies of reinforcement learning have shown that humans learn differently in response to positive and negative reward prediction errors, a phenomenon that can be captured computationally by positing asymmetric learning rates. This asymmetry, motivated by neurobiological and cognitive considerations, has been invoked to explain learning differences across the lifespan as well as a range of psychiatric disorders. Recent theoretical work, motivated by normative considerations, has hypothesized that the learning rate asymmetry should be modulated by the distribution of rewards across the available options. In particular, the learning rate for negative prediction errors should be higher than the learning rate for positive prediction errors when the average reward rate is high, and this relationship should reverse when the reward rate is low. We tested this hypothesis in a series of experiments. Contrary to the theoretical predictions, we found that the asymmetry was largely insensitive to the average reward rate; instead, the dominant pattern was a higher learning rate for negative than for positive prediction errors, possibly reflecting risk aversion.

  12. Adaptative Peer to Peer Data Sharing for Technology Enhanced Learning

    NASA Astrophysics Data System (ADS)

    Angelaccio, Michele; Buttarazzi, Berta

    Starting from the hypothesis that P2P Data Sharing in a direct teaching scenario (e.g.: a classroom lesson) may lead to relevant benefits, this paper explores the features of EduSHARE a Collaborative Learning System useful for Enhanced Learning Process.

  13. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  14. Information on Quantifiers and Argument Structure in English Learner's Dictionaries.

    ERIC Educational Resources Information Center

    Lee, Thomas Hun-tak

    1993-01-01

    Lexicographers have been arguing for the inclusion of abstract and complex grammatical information in dictionaries. This paper examines the extent to which information about quantifiers and the argument structure of verbs is encoded in English learner's dictionaries. The Oxford Advanced Learner's Dictionary (1989), the Longman Dictionary of…

  15. Lessons Learned from the Everglades Collaborative Adaptive Management Program

    EPA Science Inventory

    Recent technical papers explore whether adaptive management (AM) is useful for environmental management and restoration efforts and discuss the many challenges to overcome for successful implementation, especially for large-scale restoration programs (McLain and Lee 1996; Levine ...

  16. Potentiating mGluR5 function with a positive allosteric modulator enhances adaptive learning.

    PubMed

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J; Nomura, Toshihiro; Stauffer, Shaun R; Lindsley, Craig W; Conn, P Jeffrey; Contractor, Anis

    2013-08-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5 signaling is required for different forms of adaptive learning. Here, we tested whether ADX47273, a selective positive allosteric modulator (PAM) of mGluR5, can enhance adaptive learning in mice. We found that systemic administration of the ADX47273 enhanced reversal learning in the Morris Water Maze, an adaptive task. In addition, we found that ADX47273 had no effect on single-session and multi-session extinction, but administration of ADX47273 after a single retrieval trial enhanced subsequent fear extinction learning. Together these results demonstrate a role for mGluR5 signaling in adaptive learning, and suggest that mGluR5 PAMs represent a viable strategy for treatment of maladaptive learning and for improving behavioral flexibility.

  17. Potentiating mGluR5 function with a positive allosteric modulator enhances adaptive learning

    PubMed Central

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J.; Nomura, Toshihiro; Stauffer, Shaun R.; Lindsley, Craig W.; Conn, P. Jeffrey; Contractor, Anis

    2013-01-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5 signaling is required for different forms of adaptive learning. Here, we tested whether ADX47273, a selective positive allosteric modulator (PAM) of mGluR5, can enhance adaptive learning in mice. We found that systemic administration of the ADX47273 enhanced reversal learning in the Morris Water Maze, an adaptive task. In addition, we found that ADX47273 had no effect on single-session and multi-session extinction, but administration of ADX47273 after a single retrieval trial enhanced subsequent fear extinction learning. Together these results demonstrate a role for mGluR5 signaling in adaptive learning, and suggest that mGluR5 PAMs represent a viable strategy for treatment of maladaptive learning and for improving behavioral flexibility. PMID:23869026

  18. Seeing is believing: effects of visual contextual cues on learning and transfer of locomotor adaptation.

    PubMed

    Torres-Oviedo, Gelsy; Bastian, Amy J

    2010-12-15

    Devices such as robots or treadmills are often used to drive motor learning because they can create novel physical environments. However, the learning (i.e., adaptation) acquired on these devices only partially generalizes to natural movements. What determines the specificity of motor learning, and can this be reliably made more general? Here we investigated the effect of visual cues on the specificity of split-belt walking adaptation. We systematically removed vision to eliminate the visual-proprioceptive mismatch that is a salient cue specific to treadmills: vision indicates that we are not moving while leg proprioception indicates that we are. We evaluated the adaptation of temporal and spatial features of gait (i.e., timing and location of foot landing), their transfer to walking over ground, and washout of adaptation when subjects returned to the treadmill. Removing vision during both training (i.e., on the treadmill) and testing (i.e., over ground) strongly improved the transfer of treadmill adaptation to natural walking. Removing vision only during training increased transfer of temporal adaptation, whereas removing vision only during testing increased the transfer of spatial adaptation. This dissociation reveals differences in adaptive mechanisms for temporal and spatial features of walking. Finally training without vision increased the amount that was learned and was linked to the variability in the behavior during adaptation. In conclusion, contextual cues can be manipulated to modulate the magnitude, transfer, and washout of device-induced learning in humans. These results bring us closer to our ultimate goal of developing rehabilitation strategies that improve movements beyond the clinical setting.

  19. Booksearch: What Dictionary (General or Specialized) Do You Find Useful or Interesting for Students?

    ERIC Educational Resources Information Center

    English Journal, 1988

    1988-01-01

    Presents classroom teachers' recommendations for a variety of dictionaries that may heighten students' interest in language: a reverse dictionary, a visual dictionary, WEIGHTY WORD BOOK, a collegiate desk dictionary, OXFORD ENGLISH DICTIONARY, DICTIONARY OF AMERICAN REGIONAL ENGLISH, and a dictionary of idioms. (ARH)

  20. Reinforcement learning by Hebbian synapses with adaptive thresholds.

    PubMed

    Pennartz, C M

    1997-11-01

    A central problem in learning theory is how the vertebrate brain processes reinforcing stimuli in order to master complex sensorimotor tasks. This problem belongs to the domain of supervised learning, in which errors in the response of a neural network serve as the basis for modification of synaptic connectivity in the network and thereby train it on a computational task. The model presented here shows how a reinforcing feedback can modify synapses in a neuronal network according to the principles of Hebbian learning. The reinforcing feedback steers synapses towards long-term potentiation or depression by critically influencing the rise in postsynaptic calcium, in accordance with findings on synaptic plasticity in mammalian brain. An important feature of the model is the dependence of modification thresholds on the previous history of reinforcing feedback processed by the network. The learning algorithm trained networks successfully on a task in which a population vector in the motor output was required to match a sensory stimulus vector presented shortly before. In another task, networks were trained to compute coordinate transformations by combining different visual inputs. The model continued to behave well when simplified units were replaced by single-compartment neurons equipped with several conductances and operating in continuous time. This novel form of reinforcement learning incorporates essential properties of Hebbian synaptic plasticity and thereby shows that supervised learning can be accomplished by a learning rule similar to those used in physiologically plausible models of unsupervised learning. The model can be crudely correlated to the anatomy and electrophysiology of the amygdala, prefrontal and cingulate cortex and has predictive implications for further experiments on synaptic plasticity and learning processes mediated by these areas.

  1. Vocal learning in elephants: neural bases and adaptive context.

    PubMed

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants.

  2. Vocal learning in elephants: neural bases and adaptive context

    PubMed Central

    Stoeger, Angela S; Manger, Paul

    2014-01-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. PMID:25062469

  3. Vocal learning in elephants: neural bases and adaptive context.

    PubMed

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. PMID:25062469

  4. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model

    PubMed Central

    Cheresiz, S. V.; Semenova, E. A.; Chepurnov, A. A.

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups. PMID:26989413

  5. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use.

    PubMed

    Brudner, Samuel N; Kethidi, Nikhit; Graeupner, Damaris; Ivry, Richard B; Taylor, Jordan A

    2016-03-01

    In sensorimotor adaptation tasks, feedback delays can cause significant reductions in the rate of learning. This constraint is puzzling given that many skilled behaviors have inherently long delays (e.g., hitting a golf ball). One difference in these task domains is that adaptation is primarily driven by error-based feedback, whereas skilled performance may also rely to a large extent on outcome-based feedback. This difference suggests that error- and outcome-based feedback may engage different learning processes, and these processes may be associated with different temporal constraints. We tested this hypothesis in a visuomotor adaptation task. Error feedback was indicated by the terminal position of a cursor, while outcome feedback was indicated by points. In separate groups of participants, the two feedback signals were presented immediately at the end of the movement, after a delay, or with just the error feedback delayed. Participants learned to counter the rotation in a similar manner regardless of feedback delay. However, the aftereffect, an indicator of implicit motor adaptation, was attenuated with delayed error feedback, consistent with the hypothesis that a different learning process supports performance under delay. We tested this by employing a task that dissociates the contribution of explicit strategies and implicit adaptation. We find that explicit aiming strategies contribute to the majority of the learning curve, regardless of delay; however, implicit learning, measured over the course of learning and by aftereffects, was significantly attenuated with delayed error-based feedback. These experiments offer new insight into the temporal constraints associated with different motor learning processes. PMID:26792878

  6. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    PubMed

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system. PMID:17385639

  7. Profiling Students' Adaptation Styles in Web-based Learning.

    ERIC Educational Resources Information Center

    Lee, Myung-Geun

    2001-01-01

    Discussion of Web-based instruction (WBI) focuses on a study of Korean universities that analyzed learners' adaptation styles and characteristics by retrospectively assessing the perceptions of various aspects of WBI. Considers computer literacy, interaction with instructor and students, difficulty of contents, and learners' perception of academic…

  8. Collaborative Learning with Multi-Touch Technology: Developing Adaptive Expertise

    ERIC Educational Resources Information Center

    Mercier, Emma M.; Higgins, Steven E.

    2013-01-01

    Developing fluency and flexibility in mathematics is a key goal of upper primary schooling, however, while fluency can be developed with practice, designing activities that support the development of flexibility is more difficult. Drawing on concepts of adaptive expertise, we developed a task for a multi-touch classroom, NumberNet, that aimed to…

  9. Children's Ability to Learn Evolutionary Explanations for Biological Adaptation

    ERIC Educational Resources Information Center

    Shtulman, Andrew; Neal, Cara; Lindquist, Gabrielle

    2016-01-01

    Research Findings: Evolution by natural selection is often relegated to the high school curriculum on the assumption that younger students cannot grasp its complexity. We sought to test that assumption by teaching children ages 4-12 (n = 96) a selection-based explanation for biological adaptation and comparing their success to that of adults…

  10. Adaptive memory: animacy effects persist in paired-associate learning.

    PubMed

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning. PMID:24813366

  11. Adaptive memory: animacy effects persist in paired-associate learning.

    PubMed

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

  12. An application of adaptive learning to malfunction recovery

    NASA Technical Reports Server (NTRS)

    Cruz, R. E.

    1986-01-01

    A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

  13. Learning and adaptation in the management of waterfowl harvests

    USGS Publications Warehouse

    Johnson, Fred A.

    2011-01-01

    A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

  14. Qualitative adaptive reward learning with success failure maps: applied to humanoid robot walking.

    PubMed

    Nassour, John; Hugel, Vincent; Ben Ouezdou, Fethi; Cheng, Gordon

    2013-01-01

    In the human brain, rewards are encoded in a flexible and adaptive way after each novel stimulus. Neurons of the orbitofrontal cortex are the key reward structure of the brain. Neurobiological studies show that the anterior cingulate cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differentiate between a learning mechanism that takes risks and one that averts risks. The tolerance to risk plays an important role in such a learning mechanism. Results have shown the differences in learning capacity between risk-taking and risk-avert behaviors. These neurological properties provide promising inspirations for robot learning based on rewards. In this paper, we propose a learning mechanism that is able to learn from negative and positive feedback with reward coding adaptively. It is composed of two phases: evaluation and decision making. In the evaluation phase, we use a Kohonen self-organizing map technique to represent success and failure. Decision making is based on an early warning mechanism that enables avoiding repeating past mistakes. The behavior to risk is modulated in order to gain experiences for success and for failure. Success map is learned with adaptive reward that qualifies the learned task in order to optimize the efficiency. Our approach is presented with an implementation on the NAO humanoid robot, controlled by a bioinspired neural controller based on a central pattern generator. The learning system adapts the oscillation frequency and the motor neuron gain in pitch and roll in order to walk on flat and sloped terrain, and to switch between them.

  15. Dictionnaires et encyclopedies (Dictionaries and Encyclopedias).

    ERIC Educational Resources Information Center

    Ferran, Pierre

    1988-01-01

    Eight French dictionaries and encyclopedic reference books are reviewed, focusing on their formats, characteristics, and intended uses. They include references for language, geopolitics and economics, economic history, signs and symbols, and an almanac. (MSE)

  16. Using dictionaries to study the mental lexicon.

    PubMed

    Anshen, F; Aronoff, M

    The notion of a mental lexicon has its historical roots in practical reference dictionaries. The distributional analysis of dictionaries provides one means of investigating the structure of the mental lexicon. We review our earlier work with dictionaries, based on a three-way horserace model of lexical access and production, and then present the most recent results of our ongoing analysis of the Oxford English Dictionary, Second Edition on CD-ROM, which traces changes in productivity over time of the English suffixes -ment and -ity, both of which originate in French borrowings. Our results lead us to question the validity of automatic analogy from a set of existing words as the driving force behind morphological productivity. PMID:10433734

  17. Using dictionaries to study the mental lexicon.

    PubMed

    Anshen, F; Aronoff, M

    The notion of a mental lexicon has its historical roots in practical reference dictionaries. The distributional analysis of dictionaries provides one means of investigating the structure of the mental lexicon. We review our earlier work with dictionaries, based on a three-way horserace model of lexical access and production, and then present the most recent results of our ongoing analysis of the Oxford English Dictionary, Second Edition on CD-ROM, which traces changes in productivity over time of the English suffixes -ment and -ity, both of which originate in French borrowings. Our results lead us to question the validity of automatic analogy from a set of existing words as the driving force behind morphological productivity.

  18. Sense-Analysis for a Historical Dictionary

    ERIC Educational Resources Information Center

    Aitken, A. J.

    1973-01-01

    Part of Lexicography and Dialect Geography, Festgabe for Hans Kurath''. Comments are based on the compiling of A Dictionary of the Older Scottish Tongue''. Paper presented at the Table Ronde de Lexicographie Historique, Florence, Italy, May 4, 1971. (DD)

  19. TUNS user guide supplement: Data dictionary

    NASA Technical Reports Server (NTRS)

    1988-01-01

    Provided is a data dictionary for the Technology Utilization Network System (TUNS) providing for each element name the long name, data type, data size, descriptive name and description, data of PRI clause, legal values, and location used.

  20. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  1. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

    PubMed Central

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  2. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  3. A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

    PubMed Central

    Wen, Hui; Xie, Weixin; Pei, Jihong

    2016-01-01

    This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

  4. A DICTIONARY OF SYRIAN ARABIC (DIALECT OF DAMASCUS), ENGLISH-ARABIC. ARABIC SERIES, NUMBER FIVE.

    ERIC Educational Resources Information Center

    ANI, MOUKHTAR; STOWASSER, KARL

    THIS DICTIONARY IS BASED ON THE DIALECT OF DAMASCUS, AS SPOKEN BY EDUCATED MUSLIMS. DAMASCUS OCCUPIES A CENTRAL POSITION IN MORE THAN THE OBVIOUS GEOGRAPHICAL SENSE. THE CITY IS A MAJOR ADMINISTRATIVE CENTER OF THE AREA AND, HISTORICALLY, AN IMPORTANT SEAT OF ISLAMIC CULTURE AND LEARNING. A NUMBER OF POLITICAL, SOCIOLOGICAL, AND GEOGRAPHICAL…

  5. Bilingual Dictionaries in Tests of L2 Writing Proficiency: Do They Make a Difference?

    ERIC Educational Resources Information Center

    East, Martin

    2007-01-01

    Whether test takers should be allowed access to dictionaries when taking L2 tests has been the subject of debate for a good number of years. Opinions differ according to how the test construct is understood and whether the underlying value system favours process-orientated assessment for learning, with its concern to elicit the test takers' best…

  6. OAEditor--A Framework for Editing Adaptive Learning Objects

    ERIC Educational Resources Information Center

    Pereira, Joao Carlos Rodrigues; Cabral, Lucidio dos Anjos Formiga; Oiveira, Ronei dos Santos; Bezerra, Lucimar Leandro; de Melo, Nisston Moraes Tavares

    2012-01-01

    Distance Learning supported by the WEB is a reality which is growing fast and, like any technological or empirical innovation, it reveals positive and negative aspects. An important aspect is in relation to the monitoring of the activities done by the students since an accurate online assessment of the knowledge acquired is an open and, therefore,…

  7. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  8. Primary Motor Cortex Involvement in Initial Learning during Visuomotor Adaptation

    ERIC Educational Resources Information Center

    Riek, Stephan; Hinder, Mark R.; Carson, Richard G.

    2012-01-01

    Human motor behaviour is continually modified on the basis of errors between desired and actual movement outcomes. It is emerging that the role played by the primary motor cortex (M1) in this process is contingent upon a variety of factors, including the nature of the task being performed, and the stage of learning. Here we used repetitive TMS to…

  9. Adapting to Change: What Motivates Manitoban Schools to Learn

    ERIC Educational Resources Information Center

    Lam, Y. L. Jack

    2004-01-01

    This study assesses the relative importance of environmental, intraorganizational, and contextual factors that explain the process and outcomes of organizational learning in six Manitoba schools. Based on the data provided by 265 teaching staff and their principals, the present findings verified that transformational leadership, supportive school…

  10. Cooperative Learning and Adaptive Instruction in a Mathematics Curriculum.

    ERIC Educational Resources Information Center

    Terwel, Jan; And Others

    1994-01-01

    Maintains that current research suggests that heterogeneous grouping is preferable. Reports on a study of a new mathematics curriculum using 600 students in 6 Dutch schools. Finds that students in heterogeneous classes taught with cooperative-learning techniques achieved more than students in traditional ability-grouped classrooms. (CFR)

  11. Adaptive Animation of Human Motion for E-Learning Applications

    ERIC Educational Resources Information Center

    Li, Frederick W. B.; Lau, Rynson W. H.; Komura, Taku; Wang, Meng; Siu, Becky

    2007-01-01

    Human motion animation has been one of the major research topics in the field of computer graphics for decades. Techniques developed in this area help present human motions in various applications. This is crucial for enhancing the realism as well as promoting the user interest in the applications. To carry this merit to e-learning applications,…

  12. Adaptation Provisioning with Respect to Learning Styles in a Web-Based Educational System: An Experimental Study

    ERIC Educational Resources Information Center

    Popescu, E.

    2010-01-01

    Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates…

  13. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective

    PubMed Central

    Duarte, Tiago; Culver, Diane M.

    2014-01-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology. PMID:25210408

  14. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    PubMed

    Duarte, Tiago; Culver, Diane M

    2014-10-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology.

  15. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    PubMed

    Duarte, Tiago; Culver, Diane M

    2014-10-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology. PMID:25210408

  16. Particle Swarm Social Model for Group Social Learning in Adaptive Environment

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Treadwell, Jim N; Patton, Robert M; Pullum, Laura L

    2008-01-01

    This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as insurgents or online communities.

  17. Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis

    PubMed Central

    Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

    2011-01-01

    The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results. PMID:22163938

  18. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  19. Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program

    USGS Publications Warehouse

    Melis, Theodore S.; Walters, Carl; Korman, Josh

    2015-01-01

    With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.

  20. Daytime sleep has no effect on the time course of motor sequence and visuomotor adaptation learning.

    PubMed

    Backhaus, Winifried; Braaß, Hanna; Renné, Thomas; Krüger, Christian; Gerloff, Christian; Hummel, Friedhelm C

    2016-05-01

    Sleep has previously been claimed to be essential for the continued learning processes of declarative information as well as procedural learning. This study was conducted to examine the importance of sleep, especially the effects of midday naps, on motor sequence and visuomotor adaptation learning. Thirty-five (27 females) healthy, young adults aged between 18 and 30years of age participated in the current study. Addressing potential differences in explicit sequence and motor adaptation learning participants were asked to learn both, a nine-element explicit sequence and a motor adaptation task, in a crossover fashion on two consecutive days. Both tasks were performed with their non-dominant left hand. Prior to learning, each participant was randomized to one of three interventions; (1) power nap: 10-20min sleep, (2) long nap: 50-80min sleep or (3) a 45-min wake-condition. Performance of the motor learning task took place prior to and after a midday rest period, as well as after a night of sleep. Both sleep conditions were dominated by Stage N2 sleep with embedded sleep spindles, which have been described to be associated with enhancement of motor performance. Significant performance changes were observed in both tasks across all interventions (sleep and wake) confirming that learning took place. In the present setup, the magnitude of motor learning was not sleep-dependent in young adults - no differences between the intervention groups (short nap, long nap, no nap) could be found. The effect of the following night of sleep was not influenced by the previous midday rest or sleep period. This finding may be related to the selectiveness of the human brain enhancing especially memory being thought of as important in the future. Previous findings on motor learning enhancing effects of sleep, especially of daytime sleep, are challenged.